0 and page k ∈ L) scores[j] ← scores[j] − |y[k] + g[j] ∗ z[k]| +|x[k]+y[k]+g[j]∗z[k])| end end Return k pages with highest scores 5.2.2 PageRank Flows We now present an intuitive analysis of the stochastic complementation method by decomposing the change in PageRank in terms of `leaks'' and `flows''.\nThis analysis is motivated by the decomposition given in (15).\nPageRank `flow'' is the increase in the local PageRanks originating from global page j.\nThe flows are represented by the non-negative vector (˜uT j f)z (equations (15) and (18)).\nThe scalar ˜uT j f can be thought of as the total amount of PageRank flow that page j has available to distribute.\nThe vector z dictates how the flow is allocated to the local domain; the flow that local page k receives is proportional to (within a constant factor due to the random surfer vector) the expected number of its inlinks.\nThe PageRank `leaks'' represent the decrease in PageRank resulting from the addition of page j.\nThe leakage can be quantified in terms of the non-positive vectors x and y (equations (16) and (17)).\nFor vector x, we can see from equation (19) that the amount of PageRank leaked by a local page is proportional to the weighted sum of the Page121 Research Track Paper Ranks of its siblings.\nThus, pages that have siblings with higher PageRanks (and low outlink counts) will experience more leakage.\nThe leakage caused by y is an artifact of the random surfer vector.\nWe will next show that if only the `flow'' term, (˜uT j f)z, is considered, then the resulting method is very similar to a heuristic proposed by Cho et al. [6] that has been widely used for the Crawling Through URL Ordering problem.\nThis heuristic is computationally cheaper, but as we will see later, not as effective as the Stochastic Complementation method.\nOur node selection strategy chooses global nodes that have the largest influence (equation (7)).\nIf this influence is approximated using only `flows'', the optimal node j∗ is: j∗ = argmaxj EL ˜uT j fz 1 = argmaxj ˜uT j f EL z 1 = argmaxj ˜uT j f = argmaxj α(DF + diag(uj))−1 uj + (1 − α) e + 1 , f = argmaxjfT (DF + diag(uj))−1 uj.\nThe resulting page selection score can be expressed as a sum of the PageRanks of each local page k that links to j, where each PageRank value is normalized by o[k]+1.\nInterestingly, the normalization that arises in our method differs from the heuristic given in [6], which normalizes by o[k].\nThe algorithm PF-Select, which is omitted due to lack of space, first computes the quantity fT (DF +diag(uj))−1 uj for each global page j, and then returns the pages with the k largest scores.\nTo see that the running time for this algorithm is O(n), note that the computation involved in this method is a subset of that needed for the SC-Select method (Algorithm 3), which was shown to have a running time of O(n).\n6.\nEXPERIMENTS In this section, we provide experimental evidence to verify the effectiveness of our algorithms.\nWe first outline our experimental methodology and then provide results across a variety of local domains.\n6.1 Methodology Given the limited resources available at an academic institution, crawling a section of the web that is of the same magnitude as that indexed by Google or Yahoo! is clearly infeasible.\nThus, for a given local domain, we approximate the global graph by crawling a local neighborhood around the domain that is several orders of magnitude larger than the local subgraph.\nEven though such a graph is still orders of magnitude smaller than the `true'' global graph, we contend that, even if there exist some highly influential pages that are very far away from our local domain, it is unrealistic for any local node selection algorithm to find them.\nSuch pages also tend to be highly unrelated to pages within the local domain.\nWhen explaining our node selection strategies in section 5, we made the simplifying assumption that our local graph contained no dangling nodes.\nThis assumption was only made to ease our analysis.\nOur implementation efficiently handles dangling links by replacing each zero column of our adjacency matrix with the uniform vector.\nWe evaluate the algorithm using the two node selection strategies given in Section 5.2, and also against the following baseline methods: • Random: Nodes are chosen uniformly at random among the known global nodes.\n• OutlinkCount: Global nodes with the highest number of outlinks from the local domain are chosen.\nAt each iteration of the FindGlobalPR algorithm, we evaluate performance by computing the difference between the current PageRank estimate of the local domain, ELf ELf 1 , and the global PageRank of the local domain ELg ELg 1 .\nAll PageRank calculations were performed using the uniform random surfer vector.\nAcross all experiments, we set the random surfer parameter α, to be .85, and used a convergence threshold of 10−6 .\nWe evaluate the difference between the local and global PageRank vectors using three different metrics: the L1 and L∞ norms, and Kendall``s tau.\nThe L1 norm measures the sum of the absolute value of the differences between the two vectors, and the L∞ norm measures the absolute value of the largest difference.\nKendall``s tau metric is a popular rank correlation measure used to compare PageRanks [2, 11].\nThis metric can be computed by counting the number of pairs of pairs that agree in ranking, and subtracting from that the number of pairs of pairs that disagree in ranking.\nThe final value is then normalized by the total number of n 2 such pairs, resulting in a [−1, 1] range, where a negative score signifies anti-correlation among rankings, and values near one correspond to strong rank correlation.\n6.2 Results Our experiments are based on two large web crawls and were downloaded using the web crawler that is part of the Nutch open source search engine project [18].\nAll crawls were restricted to only `http'' pages, and to limit the number of dynamically generated pages that we crawl, we ignored all pages with urls containing any of the characters `?''\n, `*'', `@'', or `=''.\nThe first crawl, which we will refer to as the `edu'' dataset, was seeded by homepages of the top 100 graduate computer science departments in the USA, as rated by the US News and World Report [16], and also by the home pages of their respective institutions.\nA crawl of depth 5 was performed, restricted to pages within the ‘.edu'' domain, resulting in a graph with approximately 4.7 million pages and 22.9 million links.\nThe second crawl was seeded by the set of pages under the `politics'' hierarchy in the dmoz open directory project[17].\nWe crawled all pages up to four links away, which yielded a graph with 4.4 million pages and 17.3 million links.\nWithin the `edu'' crawl, we identified the five site-specific domains corresponding to the websites of the top five graduate computer science departments, as ranked by the US News and World Report.\nThis yielded local domains of various sizes, from 10,626 (UIUC) to 59,895 (Berkeley).\nFor each of these site-specific domains with size n, we performed 50 iterations of the FindGlobalPR algorithm to crawl a total of 2n additional nodes.\nFigure 2(a) gives the (L1) difference from the PageRank estimate at each iteration to the global PageRank, for the Berkeley local domain.\nThe performance of this dataset was representative of the typical performance across the five computer science sitespecific local domains.\nInitially, the L1 difference between the global and local PageRanks ranged from .0469 (Stanford) to .149 (MIT).\nFor the first several iterations, the 122 Research Track Paper 0 5 10 15 20 25 30 35 40 45 50 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055 Number of Iterations GlobalandLocalPageRankDifference(L1) Stochastic Complement PageRank Flow Outlink Count Random 0 10 20 30 40 50 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 Number of Iterations GlobalandLocalPageRankDifference(L1) Stochastic Complement PageRank Flow Outlink Count Random 0 5 10 15 20 25 0.16 0.18 0.2 0.22 0.24 0.26 0.28 0.3 0.32 0.34 Number of Iterations GlobalandLocalPageRankDifference(L1) Stochastic Complement PageRank Flow Outlink Count Random (a) www.cs.berkeley.edu (b) www.enterstageright.com (c) Politics Figure 2: L1 difference between the estimated and true global PageRanks for (a) Berkeley``s computer science website, (b) the site-specific domain, www.enterstageright.com, and (c) the `politics'' topic-specific domain.\nThe stochastic complement method outperforms all other methods across various domains.\nthree link-based methods all outperform the random selection heuristic.\nAfter these initial iterations, the random heuristic tended to be more competitive with (or even outperform, as in the Berkeley local domain) the outlink count and PageRank flow heuristics.\nIn all tests, the stochastic complementation method either outperformed, or was competitive with, the other methods.\nTable 1 gives the average difference between the final estimated global PageRanks and the true global PageRanks for various distance measures.\nAlgorithm L1 L∞ Kendall Stoch.\nComp.\n.0384 .00154 .9257 PR Flow .0470 .00272 .8946 Outlink .0419 .00196 .9053 Random .0407 .00204 .9086 Table 1: Average final performance of various node selection strategies for the five site-specific computer science local domains.\nNote that Kendall``s Tau measures similarity, while the other metrics are dissimilarity measures.\nStochastic Complementation clearly outperforms the other methods in all metrics.\nWithin the `politics'' dataset, we also performed two sitespecific tests for the largest websites in the crawl: www.adamsmith.org, the website for the London based Adam Smith Institute, and www.enterstageright.com, an online conservative journal.\nAs with the `edu'' local domains, we ran our algorithm for 50 iterations, crawling a total of 2n nodes.\nFigure 2 (b) plots the results for the www.enterstageright.com domain.\nIn contrast to the `edu'' local domains, the Random and OutlinkCount methods were not competitive with either the SC-Select or the PF-Select methods.\nAmong all datasets and all node selection methods, the stochastic complementation method was most impressive in this dataset, realizing a final estimate that differed only .0279 from the global PageRank, a ten-fold improvement over the initial local PageRank difference of .299.\nFor the Adam Smith local domain, the initial difference between the local and global PageRanks was .148, and the final estimates given by the SC-Select, PF-Select, OutlinkCount, and Random methods were .0208, .0193, .0222, and .0356, respectively.\nWithin the `politics'' dataset, we constructed four topicspecific local domains.\nThe first domain consisted of all pages in the dmoz politics category, and also all pages within each of these sites up to two links away.\nThis yielded a local domain of 90,811 pages, and the results are given in figure 2 (c).\nBecause of the larger size of the topic-specific domains, we ran our algorithm for only 25 iterations to crawl a total of n nodes.\nWe also created topic-specific domains from three political sub-topics: liberalism, conservatism, and socialism.\nThe pages in these domains were identified by their corresponding dmoz categories.\nFor each sub-topic, we set the local domain to be all pages within three links from the corresponding dmoz category pages.\nTable 2 summarizes the performance of these three topic-specific domains, and also the larger political domain.\nTo quantify a global page j``s effect on the global PageRank values of pages in the local domain, we define page j``s impact to be its PageRank value, g[j], normalized by the fraction of its outlinks pointing to the local domain: impact(j) = oL[j] o[j] · g[j], where, oL[j] is the number of outlinks from page j to pages in the local domain L, and o[j] is the total number of j``s outlinks.\nIn terms of the random surfer model, the impact of page j is the probability that the random surfer (1) is currently at global page j in her random walk and (2) takes an outlink to a local page, given that she has already decided not to jump to a random page.\nFor the politics local domain, we found that many of the pages with high impact were in fact political pages that should have been included in the dmoz politics topic, but were not.\nFor example, the two most influential global pages were the political search engine www.askhenry.com, and the home page of the online political magazine, www.policyreview.com.\nAmong non-political pages, the home page of the journal Education Next was most influential.\nThe journal is freely available online and contains articles regarding various aspect of K-12 education in America.\nTo provide some anecdotal evidence for the effectiveness of our page selection methods, we note that the SC-Select method chose 11 pages within the www.educationnext.org domain, the PF-Select method discovered 7 such pages, while the OutlinkCount and Random methods found only 6 pages each.\nFor the conservative political local domain, the socialist website www.ornery.org had a very high impact score.\nThis 123 Research Track Paper All Politics: Algorithm L1 L2 Kendall Stoch.\nComp.\n.1253 .000700 .8671 PR Flow .1446 .000710 .8518 Outlink .1470 .00225 .8642 Random .2055 .00203 .8271 Conservativism: Algorithm L1 L2 Kendall Stoch.\nComp.\n.0496 .000990 .9158 PR Flow .0554 .000939 .9028 Outlink .0602 .00527 .9144 Random .1197 .00102 .8843 Liberalism: Algorithm L1 L2 Kendall Stoch.\nComp.\n.0622 .001360 .8848 PR Flow .0799 .001378 .8669 Outlink .0763 .001379 .8844 Random .1127 .001899 .8372 Socialism: Algorithm L1 L∞ Kendall Stoch.\nComp.\n.04318 .00439 .9604 PR Flow .0450 .004251 .9559 Outlink .04282 .00344 .9591 Random .0631 .005123 .9350 Table 2: Final performance among node selection strategies for the four political topic-specific crawls.\nNote that Kendall``s Tau measures similarity, while the other metrics are dissimilarity measures.\nwas largely due to a link from the front page of this site to an article regarding global warming published by the National Center for Public Policy Research, a conservative research group in Washington, DC.\nNot surprisingly, the global PageRank of this article (which happens to be on the home page of the NCCPR, www.nationalresearch.com), was approximately .002, whereas the local PageRank of this page was only .00158.\nThe SC-Select method yielded a global PageRank estimate of approximately .00182, the PFSelect method estimated a value of .00167, and the Random and OutlinkCount methods yielded values of .01522 and .00171, respectively.\n7.\nRELATED WORK The node selection framework we have proposed is similar to the url ordering for crawling problem proposed by Cho et al. in [6].\nWhereas our framework seeks to minimize the difference between the global and local PageRank, the objective used in [6] is to crawl the most highly (globally) ranked pages first.\nThey propose several node selection algorithms, including the outlink count heuristic, as well as a variant of our PF-Select algorithm which they refer to as the `PageRank ordering metric''.\nThey found this method to be most effective in optimizing their objective, as did a recent survey of these methods by Baeza-Yates et al. [1].\nBoldi et al. also experiment within a similar crawling framework in [2], but quantify their results by comparing Kendall``s rank correlation between the PageRanks of the current set of crawled pages and those of the entire global graph.\nThey found that node selection strategies that crawled pages with the highest global PageRank first actually performed worse (with respect to Kendall``s Tau correlation between the local and global PageRanks) than basic depth first or breadth first strategies.\nHowever, their experiments differ from our work in that our node selection algorithms do not use (or have access to) global PageRank values.\nMany algorithmic improvements for computing exact PageRank values have been proposed [9, 10, 14].\nIf such algorithms are used to compute the global PageRanks of our local domain, they would all require O(N) computation, storage, and bandwidth, where N is the size of the global domain.\nThis is in contrast to our method, which approximates the global PageRank and scales linearly with the size of the local domain.\nWang and Dewitt [22] propose a system where the set of web servers that comprise the global domain communicate with each other to compute their respective global PageRanks.\nFor a given web server hosting n pages, the computational, bandwidth, and storage requirements are also linear in n.\nOne drawback of this system is that the number of distinct web servers that comprise the global domain can be very large.\nFor example, our `edu'' dataset contains websites from over 3,200 different universities; coordinating such a system among a large number of sites can be very difficult.\nGan, Chen, and Suel propose a method for estimating the PageRank of a single page [5] which uses only constant bandwidth, computation, and space.\nTheir approach relies on the availability of a remote connectivity server that can supply the set of inlinks to a given page, an assumption not used in our framework.\nThey experimentally show that a reasonable estimate of the node``s PageRank can be obtained by visiting at most a few hundred nodes.\nUsing their algorithm for our problem would require that either the entire global domain first be downloaded or a connectivity server be used, both of which would lead to very large web graphs.\n8.\nCONCLUSIONS AND FUTURE WORK The internet is growing exponentially, and in order to navigate such a large repository as the web, global search engines have established themselves as a necessity.\nAlong with the ubiquity of these large-scale search engines comes an increase in search users'' expectations.\nBy providing complete and isolated coverage of a particular web domain, localized search engines are an effective outlet to quickly locate content that could otherwise be difficult to find.\nIn this work, we contend that the use of global PageRank in a localized search engine can improve performance.\nTo estimate the global PageRank, we have proposed an iterative node selection framework where we select which pages from the global frontier to crawl next.\nOur primary contribution is our stochastic complementation page selection algorithm.\nThis method crawls nodes that will most significantly impact the local domain and has running time linear in the number of nodes in the local domain.\nExperimentally, we validate these methods across a diverse set of local domains, including seven site-specific domains and four topic-specific domains.\nWe conclude that by crawling an additional n or 2n pages, our methods find an estimate of the global PageRanks that is up to ten times better than just using the local PageRanks.\nFurthermore, we demonstrate that our algorithm consistently outperforms other existing heuristics.\n124 Research Track Paper Often times, topic-specific domains are discovered using a focused web crawler which considers a page``s content in conjunction with link anchor text to decide which pages to crawl next [4].\nAlthough such crawlers have proven to be quite effective in discovering topic-related content, many irrelevant pages are also crawled in the process.\nTypically, these pages are deleted and not indexed by the localized search engine.\nThese pages can of course provide valuable information regarding the global PageRank of the local domain.\nOne way to integrate these pages into our framework is to start the FindGlobalPR algorithm with the current subgraph F equal to the set of pages that were crawled by the focused crawler.\nThe global PageRank estimation framework, along with the node selection algorithms presented, all require O(n) computation per iteration and bandwidth proportional to the number of pages crawled, Tk.\nIf the number of iterations T is relatively small compared to the number of pages crawled per iteration, k, then the bottleneck of the algorithm will be the crawling phase.\nHowever, as the number of iterations increases (relative to k), the bottleneck will reside in the node selection computation.\nIn this case, our algorithms would benefit from constant factor optimizations.\nRecall that the FindGlobalPR algorithm (Algorithm 2) requires that the PageRanks of the current expanded local domain be recomputed in each iteration.\nRecent work by Langville and Meyer [12] gives an algorithm to quickly recompute PageRanks of a given webgraph if a small number of nodes are added.\nThis algorithm was shown to give speedup of five to ten times on some datasets.\nWe plan to investigate this and other such optimizations as future work.\nIn this paper, we have objectively evaluated our methods by measuring how close our global PageRank estimates are to the actual global PageRanks.\nTo determine the benefit of using global PageRanks in a localized search engine, we suggest a user study in which users are asked to rate the quality of search results for various search queries.\nFor some queries, only the local PageRanks are used in ranking, and for the remaining queries, local PageRanks and the approximate global PageRanks, as computed by our algorithms, are used.\nThe results of such a study can then be analyzed to determine the added benefit of using the global PageRanks computed by our methods, over just using the local PageRanks.\nAcknowledgements.\nThis research was supported by NSF grant CCF-0431257, NSF Career Award ACI-0093404, and a grant from Sabre, Inc. 9.\nREFERENCES [1] R. Baeza-Yates, M. Marin, C. Castillo, and A. Rodriguez.\nCrawling a country: better strategies than breadth-first for web page ordering.\nWorld-Wide Web Conference, 2005.\n[2] P. Boldi, M. Santini, and S. Vigna.\nDo your worst to make the best: paradoxical effects in pagerank incremental computations.\nWorkshop on Web Graphs, 3243:168-180, 2004.\n[3] S. Brin and L. Page.\nThe anatomy of a large-scale hypertextual web search engine.\nComputer Networks and ISDN Systems, 33(1-7):107-117, 1998.\n[4] S. Chakrabarti, M. van den Berg, and B. Dom.\nFocused crawling: a new approach to topic-specific web resource discovery.\nWorld-Wide Web Conference, 1999.\n[5] Y. Chen, Q. Gan, and T. Suel.\nLocal methods for estimating pagerank values.\nConference on Information and Knowledge Management, 2004.\n[6] J. Cho, H. Garcia-Molina, and L. Page.\nEfficient crawling through url ordering.\nWorld-Wide Web Conference, 1998.\n[7] T. H. Haveliwala and S. D. Kamvar.\nThe second eigenvalue of the Google matrix.\nTechnical report, Stanford University, 2003.\n[8] T. Joachims, F. Radlinski, L. Granka, A. Cheng, C. Tillekeratne, and A. Patel.\nLearning retrieval functions from implicit feedback.\nhttp://www.cs.cornell.edu/People/tj/career.\n[9] S. D. Kamvar, T. H. Haveliwala, C. D. Manning, and G. H. Golub.\nExploiting the block structure of the web for computing pagerank.\nWorld-Wide Web Conference, 2003.\n[10] S. D. Kamvar, T. H. Haveliwala, C. D. Manning, and G. H. Golub.\nExtrapolation methods for accelerating pagerank computation.\nWorld-Wide Web Conference, 2003.\n[11] A. N. Langville and C. D. Meyer.\nDeeper inside pagerank.\nInternet Mathematics, 2004.\n[12] A. N. Langville and C. D. Meyer.\nUpdating the stationary vector of an irreducible markov chain with an eye on Google``s pagerank.\nSIAM Journal on Matrix Analysis, 2005.\n[13] P. Lyman, H. R. Varian, K. Swearingen, P. Charles, N. Good, L. L. Jordan, and J. Pal.\nHow much information 2003?\nSchool of Information Management and System, University of California at Berkely, 2003.\n[14] F. McSherry.\nA uniform approach to accelerated pagerank computation.\nWorld-Wide Web Conference, 2005.\n[15] C. D. Meyer.\nStochastic complementation, uncoupling markov chains, and the theory of nearly reducible systems.\nSIAM Review, 31:240-272, 1989.\n[16] US News and World Report.\nhttp://www.usnews.com.\n[17] Dmoz open directory project.\nhttp://www.dmoz.org.\n[18] Nutch open source search engine.\nhttp://www.nutch.org.\n[19] F. Radlinski and T. Joachims.\nQuery chains: learning to rank from implicit feedback.\nACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005.\n[20] S. Raghavan and H. Garcia-Molina.\nCrawling the hidden web.\nIn Proceedings of the Twenty-seventh International Conference on Very Large Databases, 2001.\n[21] T. Tin Tang, D. Hawking, N. Craswell, and K. Griffiths.\nFocused crawling for both topical relevance and quality of medical information.\nConference on Information and Knowledge Management, 2005.\n[22] Y. Wang and D. J. DeWitt.\nComputing pagerank in a distributed internet search system.\nProceedings of the 30th VLDB Conference, 2004.\n125 Research Track Paper"},"lvl-2":{"kind":"string","value":"Estimating the Global PageRank of Web Communities\nABSTRACT\nLocalized search engines are small-scale systems that index a particular community on the web.\nThey offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search capability over their relevant domains.\nOne disadvantage such systems have over large-scale search engines is the lack of global PageRank values.\nSuch information is needed to assess the value of pages in the localized search domain within the context of the web as a whole.\nIn this paper, we present well-motivated algorithms to estimate the global PageRank values of a local domain.\nThe algorithms are all highly scalable in that, given a local domain of size n, they use O (n) resources that include computation time, bandwidth, and storage.\nWe test our methods across a variety of localized domains, including site-specific domains and topic-specific domains.\nWe demonstrate that by crawling as few as n or 2n additional pages, our methods can give excellent global PageRank estimates.\n1.\nINTRODUCTION\nLocalized search engines are small-scale search engines that index only a single community of the web.\nSuch communities can be site-specific domains, such as pages within\nthe cs.utexas.edu domain, or topic-related communities--for example, political websites.\nCompared to the web graph crawled and indexed by large-scale search engines, the size of such local communities is typically orders of magnitude smaller.\nConsequently, the computational resources needed to build such a search engine are also similarly lighter.\nBy restricting themselves to smaller, more manageable sections of the web, localized search engines can also provide more precise and complete search capabilities over their respective domains.\nOne drawback of localized indexes is the lack of global information needed to compute link-based rankings.\nThe PageRank algorithm [3], has proven to be an effective such measure.\nIn general, the PageRank of a given page is dependent on pages throughout the entire web graph.\nIn the context of a localized search engine, if the PageRanks are computed using only the local subgraph, then we would expect the resulting PageRanks to reflect the perceived popularity within the local community and not of the web as a whole.\nFor example, consider a localized search engine that indexes political pages with conservative views.\nA person wishing to research the opinions on global warming within the conservative political community may encounter numerous such opinions across various websites.\nIf only local PageRank values are available, then the search results will reflect only strongly held beliefs within the community.\nHowever, if global PageRanks are also available, then the results can additionally reflect outsiders' views of the conservative community (those documents that liberals most often access within the conservative community).\nThus, for many localized search engines, incorporating global PageRanks can improve the quality of search results.\nHowever, the number of pages a local search engine indexes is typically orders of magnitude smaller than the number of pages indexed by their large-scale counterparts.\nLocalized search engines do not have the bandwidth, storage capacity, or computational power to crawl, download, and compute the global PageRanks of the entire web.\nIn this work, we present a method of approximating the global PageRanks of a local domain while only using resources of the same order as those needed to compute the PageRanks of the local subgraph.\nOur proposed method looks for a supergraph of our local subgraph such that the local PageRanks within this supergraph are close to the true global PageRanks.\nWe construct this supergraph by iteratively crawling global pages on the current web frontier--i.e., global pages with inlinks from pages that have already been crawled.\nIn order to provide\na good approximation to the global PageRanks, care must be taken when choosing which pages to crawl next; in this paper, we present a well-motivated page selection algorithm that also performs well empirically.\nThis algorithm is derived from a well-defined problem objective and has a running time linear in the number of local nodes.\nWe experiment across several types of local subgraphs, including four topic related communities and several sitespecific domains.\nTo evaluate performance, we measure the difference between the current global PageRank estimate and the global PageRank, as a function of the number of pages crawled.\nWe compare our algorithm against several heuristics and also against a baseline algorithm that chooses pages at random, and we show that our method outperforms these other methods.\nFinally, we empirically demonstrate that, given a local domain of size n, we can provide good approximations to the global PageRank values by crawling at most n or 2n additional pages.\nThe paper is organized as follows.\nSection 2 gives an overview of localized search engines and outlines their advantages over global search.\nSection 3 provides background on the PageRank algorithm.\nSection 4 formally defines our problem, and section 5 presents our page selection criteria and derives our algorithms.\nSection 6 provides experimental results, section 7 gives an overview of related work, and, finally, conclusions are given in section 8.\n2.\nLOCALIZED SEARCH ENGINES\nLocalized search engines index a single community of the web, typically either a site-specific community, or a topicspecific community.\nLocalized search engines enjoy three major advantages over their large-scale counterparts: they are relatively inexpensive to build, they can offer more precise search capability over their local domain, and they can provide a more complete index.\nThe resources needed to build a global search engine are enormous.\nA 2003 study by Lyman et al. [13] found that the ` surface web' (publicly available static sites) consists of 8.9 billion pages, and that the average size of these pages is approximately 18.7 kilobytes.\nTo download a crawl of this size, approximately 167 terabytes of space is needed.\nFor a researcher who wishes to build a search engine with access to a couple of workstations or a small server, storage of this magnitude is simply not available.\nHowever, building a localized search engine over a web community of a hundred thousand pages would only require a few gigabytes of storage.\nThe computational burden required to support search queries over a database this size is more manageable as well.\nWe note that, for topic-specific search engines, the relevant community can be efficiently identified and downloaded by using a focused crawler [21, 4].\nFor site-specific domains, the local domain is readily available on their own web server.\nThis obviates the need for crawling or spidering, and a complete and up-to-date index of the domain can thus be guaranteed.\nThis is in contrast to their large-scale counterparts, which suffer from several shortcomings.\nFirst, crawling dynamically generated pages--pages in the ` hidden web'--has been the subject of research [20] and is a non-trivial task for an external crawler.\nSecond, site-specific domains can enable the robots exclusion policy.\nThis prohibits external search engines' crawlers from downloading content from the domain, and an external\nResearch Track Paper\nsearch engine must instead rely on outside links and anchor text to index these restricted pages.\nBy restricting itself to only a specific domain of the internet, a localized search engine can provide more precise search results.\nConsider the canonical ambiguous search query, ` jaguar', which can refer to either the car manufacturer or the animal.\nA scientist trying to research the habitat and evolutionary history of a jaguar may have better success using a finely tuned zoology-specific search engine than querying Google with multiple keyword searches and wading through irrelevant results.\nA method to learn better ranking functions for retrieval was recently proposed by Radlinski and Joachims [19] and has been applied to various local domains, including Cornell University's website [8].\n3.\nPAGERANK OVERVIEW\nThe PageRank algorithm defines the importance of web pages by analyzing the underlying hyperlink structure of a web graph.\nThe algorithm works by building a Markov chain from the link structure of the web graph and computing its stationary distribution.\nOne way to compute the stationary distribution of a Markov chain is to find the limiting distribution of a random walk over the chain.\nThus, the PageRank algorithm uses what is sometimes referred to as the ` random surfer' model.\nIn each step of the random walk, the ` surfer' either follows an outlink from the current page (i.e. the current node in the chain), or jumps to a random page on the web.\nWe now precisely define the PageRank problem.\nLet U be an m × m adjacency matrix for a given web graph such that Uji = 1 if page i links to page j and Uji = 0 otherwise.\nWe define the PageRank matrix PU to be:\nis column stochastic, α is a given scalar such that 0 <α <1, e is the vector of all ones, and v is a non-negative, L1normalized vector, sometimes called the ` random surfer' vector.\nNote that the matrix D − 1 U is well-defined only if each column of U has at least one non-zero entry--i.e., each page in the webgraph has at least one outlink.\nIn the presence of such ` dangling nodes' that have no outlinks, one commonly used solution, proposed by Brin et al. [3], is to replace each zero column of U by a non-negative, L1-normalized vector.\nThe PageRank vector r is the dominant eigenvector of the PageRank matrix, r = PU r.\nWe will assume, without loss of generality, that r has an L1-norm of one.\nComputationally, r can be computed using the power method.\nThis method first chooses a random starting vector r (0), and iteratively multiplies the current vector by the PageRank matrix PU; see Algorithm 1.\nIn general, each iteration of the power method can take O (m2) operations when PU is a dense matrix.\nHowever, in practice, the number of links in a web graph will be of the order of the number of pages.\nBy exploiting the sparsity of the PageRank matrix, the work per iteration can be reduced to O (km), where k is the average number of links per web page.\nIt has also been shown that the total number of iterations needed for convergence is proportional to α and does not depend on the size of the web graph [11, 7].\nFinally, the total space needed is also O (km), mainly to store the matrix U.\nResearch Track Paper\nALGORITHM 1: A linear time (per iteration) algorithm for computing PageRank.\n4.\nPROBLEM DEFINITION\nGiven a local domain L, let G be an N x N adjacency matrix for the entire connected component of the web that contains L, such that Gji = 1 if page i links to page j and Gji = 0 otherwise.\nWithout loss of generality, we will partition G as:\nwhere L is the n x n local subgraph corresponding to links inside the local domain, Lout is the subgraph that corresponds to links from the local domain pointing out to the global domain, Gout is the subgraph containing links from the global domain into the local domain, and Gwithin contains links within the global domain.\nWe assume that when building a localized search engine, only pages inside the local domain are crawled, and the links between these pages are represented by the subgraph L.\nThe links in Lout are also known, as these point from crawled pages in the local domain to uncrawled pages in the global domain.\nAs defined in equation (1), PG is the PageRank matrix formed from the global graph G, and we define the global PageRank vector of this graph to be g. Let the n-length vector p ∗ be the L1-normalized vector corresponding to the global PageRank of the pages in the local domain L:\nwhere EL = [I | 0] is the restriction matrix that selects the components from g corresponding to nodes in L. Let p denote the PageRank vector constructed from the local domain subgraph L.\nIn practice, the observed local PageRank p and the global PageRank p ∗ will be quite different.\nOne would expect that as the size of local matrix L approaches the size of global matrix G, the global PageRank and the observed local PageRank will become more similar.\nThus, one approach to estimating the global PageRank is to crawl the entire global domain, compute its PageRank, and extract the PageRanks of the local domain.\nTypically, however, n \"N, i.e., the number of global pages is much larger than the number of local pages.\nTherefore, crawling all global pages will quickly exhaust all local resources (computational, storage, and bandwidth) available to create the local search engine.\nWe instead seek a supergraph Fˆ of our local subgraph L with size O (n).\nOur goal\nALGORITHM 2: The FINDGLOBALPR algorithm.\nis to find such a supergraph Fˆ with PageRank fˆ, so that fˆ when restricted to L is close to p ∗.\nFormally, we seek to minimize GlobalDif f (We choose the L1 norm for measuring the error as it does not place excessive weight on outliers (as the L2 norm does, for example), and also because it is the most commonly used distance measure in the literature for comparing PageRank vectors, as well as for detecting convergence of the algorithm [3].\nfor constructing ˆ We propose a greedy framework, given in Algorithm 2, F. Initially, F is set to the local subgraph L, and the PageRank f of this graph is computed.\nThe algorithm then proceeds as follows.\nFirst, the SELECTNODES algorithm (which we discuss in the next section) is called and it returns a set of k nodes to crawl next from the set of nodes in the current crawl frontier, Fout.\nThese selected nodes are then crawled to expand the local subgraph, F, and the PageRanks of this expanded graph are then recomputed.\nThese steps are repeated for each of T iterations.\nFinally, the PageRank vector ˆp, which is restricted to pages within the original local domain, is returned.\nGiven our computation, bandwidth, and memory restrictions, we will assume that the algorithm will crawl at most O (n) pages.\nSince the PageRanks are computed in each iteration of the algorithm, which is an O (n) operation, we will also assume that the number of iterations T is a constant.\nOf course, the main challenge here is in selecting which set of k nodes to crawl next.\nIn the next section, we formally define the problem and give efficient algorithms.\n5.\nNODE SELECTION\nIn this section, we present node selection algorithms that operate within the greedy framework presented in the previous section.\nWe first give a well-defined criteria for the page selection problem and provide experimental evidence that this criteria can effectively identify pages that optimize our problem objective (3).\nWe then present our main alFINDGLOBALPR (L, Lout, T, k) Input: L: zero-one adjacency matrix for the local domain, Lout: zero-one outlink matrix from L to global subgraph as in (2), T: number of iterations, k: number of pages to crawl per iteration.\nOutput: ˆp: an improved estimate of the global Page\ngorithmic contribution of the paper, a method with linear running time that is derived from this page selection criteria.\nFinally, we give an intuitive analysis of our algorithm in terms of ` leaks' and ` flows'.\nWe show that if only the ` flow' is considered, then the resulting method is very similar to a widely used page selection heuristic [6].\n5.1 Formulation\nFor a given page j in the global domain, we define the expanded local graph Fj:\nwhere uj is the zero-one vector containing the outlinks from F into page j, and s contains the inlinks from page j into the local domain.\nNote that we do not allow self-links in this framework.\nIn practice, self-links are often removed, as they only serve to inflate a given page's PageRank.\nObserve that the inlinks into F from node j are not known until after node j is crawled.\nTherefore, we estimate this inlink vector as the expectation over inlink counts among the set of already crawled pages,\nIn practice, for any given page, this estimate may not reflect the true inlinks from that page.\nFurthermore, this expectation is sampled from the set of links within the crawled domain, whereas a better estimate would also use links from the global domain.\nHowever, the latter distribution is not known to a localized search engine, and we contend that the above estimate will, on average, be a better estimate than the uniform distribution, for example.\nLet the PageRank of F be f.\nWe express the PageRank fj + of the expanded local graph Fj as\nwhere xj is the PageRank of the candidate global node j, and fj is the L1-normalized PageRank vector restricted to the pages in F.\nSince directly optimizing our problem goal requires knowing the global PageRank p ∗, we instead propose to crawl those nodes that will have the greatest influence on the PageRanks of pages in the original local domain L:\nExperimentally, the influence score is a very good predictor of our problem objective (3).\nFor each candidate global node j, figure 1 (a) shows the objective function value Global Diff (fj) as a function of the influence of page j.\nThe local domain used here is a crawl of conservative political pages (we will provide more details about this dataset in section 6); we observed similar results in other domains.\nThe correlation is quite strong, implying that the influence criteria can effectively identify pages that improve the global PageRank estimate.\nAs a baseline, figure 1 (b) compares our objective with an alternative criteria, outlink count.\nThe outlink count is defined as the number of outlinks from the local domain to page j.\nThe correlation here is much weaker.\nResearch Track Paper\nFigure 1: (a) The correlation between our influence page selection criteria (7) and the actual objective function (3) value is quite strong.\n(b) This is in contrast to other criteria, such as outlink count, which exhibit a much weaker correlation.\n5.2 Computation\nAs described, for each candidate global page j, the influence score (7) must be computed.\nIf fj is computed exactly for each global page j, then the PageRank algorithm would need to be run for each of the O (n) such global pages j we consider, resulting in an O (n2) computational cost for the node selection method.\nThus, computing the exact value of fj will lead to a quadratic algorithm, and we must instead turn to methods of approximating this vector.\nThe algorithm we present works by performing one power method iteration used by the PageRank algorithm (Algorithm 1).\nThe convergence rate for the PageRank algorithm has been shown to equal the random surfer probability α [7, 11].\nGiven a starting vector x (0), if k PageRank iterations are performed, the current PageRank solution x (k) satisfies:\nwhere x * is the desired PageRank vector.\nTherefore, if only one iteration is performed, choosing a good starting vector is necessary to achieve an accurate approximation.\nWe partition the PageRank matrix PFj, corresponding to the ~ x ~ subgraph Fj as:\nand diag (uj) is the diagonal matrix with the (i, i) th entry equal to one if the ith element of uj equals one, and is zero otherwise.\nWe have assumed here that the random surfer vector is the uniform vector, and that L has no ` dangling links'.\nThese assumptions are not necessary and serve only to simplify discussion and analysis.\nA simple approach for estimating fj is the following.\nFirst, estimate the PageRank fj + of Fj by computing one PageRank iteration over the matrix PFj, using the starting vec ~ f ~ tor ν =.\nThen, estimate fj by removing the last\nResearch Track Paper\ncomponent from our estimate of fj + (i.e., the component corresponding to the added node j), and renormalizing.\nThe problem with this approach is in the starting vector.\nRecall from (6) that xj is the PageRank of the added node j.\nThe difference between the actual PageRank fj + of PFj and the starting vector ν is\nThus, by (8), after one PageRank iteration, we expect our estimate of fj + to still have an error of about 2αxj.\nIn particular, for candidate nodes j with relatively high PageRank xj, this method will yield more inaccurate results.\nWe will next present a method that eliminates this bias and runs in O (n) time.\n5.2.1 Stochastic Complementation\nSince fj +, as given in (6) is the PageRank of the matrix PFj, we have: ~ fj (1 − xj) ~ ~ F˜ s˜ ~ ~ fj (1 − xj) ~ = xj ˜uTj w xj cally, the addition of page k does not change the PageRanks of the pages in F, and thus fk = f. By construction of the stochastic complement, fk = Sfk, so the approximation given in equation (11) will yield the exact solution.\nNext, we present the computational details needed to efficiently compute the quantity ~ fj − f ~ 1 over all known global pages j.\nWe begin by expanding the difference fj − f, where the vector fj is estimated as in (11),\nNote that the matrix (DF + diag (uj)) − 1 is diagonal.\nLetting o [k] be the outlink count for page k in F, we can express the kth diagonal element as:\nNoting that (o [k] + 1) − 1 = o [k] − 1 − (o [k] (o [k] + 1)) − 1 and rewriting this in matrix form yields\nThe matrix S = F˜ + (1 − w) − 1s ˜˜uTj is known as the stochastic complement of the column stochastic matrix PFj with respect to the sub matrix F˜.\nThe theory of stochastic complementation is well studied, and it can be shown the stochastic complement of an irreducible matrix (such as the PageRank matrix) is unique.\nFurthermore, the stochastic complement is also irreducible and therefore has a unique stationary distribution as well.\nFor an extensive study, see [15].\nIt can be easily shown that the sub-dominant eigenvalue of S is at most ~ +1 ~ α, where ~ is the size of F.\nFor sufficiently large ~, this value will be very close to α.\nThis is important, as other properties of the PageRank algorithm, notably the algorithm's sensitivity, are dependent on this value [11].\nIn this method, we estimate the length ~ vector fj by computing one PageRank iteration over the ~ × ~ stochastic complement S, starting at the vector f: fj ≈ Sf.\n(11) This is in contrast to the simple method outlined in the previous section, which first iterates over the (~ + 1) × (~ + 1) matrix PFj to estimate fj +, and then removes the last component from the estimate and renormalizes to approximate fj.\nThe problem with the latter method is in the choice of the (~ + 1) length starting vector, ν.\nConsequently, the PageRank estimate given by the simple method differs from the true PageRank by at least 2αxj, where xj is the PageRank of page j. By using the stochastic complement, we can establish a tight lower bound of zero for this difference.\nTo see this, consider the case in which a node k is added to F to form the augmented local subgraph Fk, and that\nRecall that, by definition, we have PF = αF D − 1F + (1 − α) e ~.\nSubstituting (13) and (14) in (12) yields\nnoting that by definition, f = PF f, and defining the vectors x, y, and z to be\nThe first term x is a sparse vector, and takes non-zero values only for local pages k ~ that are siblings of the global page j.\nWe define (i, j) ∈ F if and only if F [j, i] = 1 (equivalently, page i links to page j) and express the value of the component x [k ~] as:\nwhere o [k], as before, is the number of outlinks from page k in the local domain.\nNote that the last two terms, y and z are not dependent on the current global node j. Given the function hj (f) = ~ y + (˜uTj f) z ~ 1, the quantity ~ fj − f ~ 1 the PageRank of this new graph is\nIf we can compute the function hj in linear time, then we can compute each value of ~ fj − f ~ 1 using an additional amount of time that is proportional to the number of nonzero components in x.\nThese optimizations are carried out in Algorithm 3.\nNote that (20) computes the difference between all components of f and fj, whereas our node selection criteria, given in (7), is restricted to the components corresponding to nodes in the original local domain L. Let us examine Algorithm 3 in more detail.\nFirst, the algorithm computes the outlink counts for each page in the local domain.\nThe algorithm then computes the quantity ˜uTj f for each known global page j.\nThis inner product can be written as where the second term sums over the set of local pages that link to page j.\nSince the total number of edges in Fout was assumed to have size O ($) (recall that f is the number of pages in F), the running time of this step is also O ($).\nThe algorithm then computes the vectors y and z, as given in (17) and (18), respectively.\nThe L1NORMDIFF method is called on the components of these vectors which correspond to the pages in L, and it estimates the value of ~ EL (y + (˜uTj f) z) ~ 1 for each page j.\nThe estimation works as follows.\nFirst, the values of ˜uTj f are discretized uniformly into c values {a1,..., ac}.\nThe quantity ~ EL (y + aiz) ~ 1 is then computed for each discretized value of ai and stored in a table.\nTo evaluate ~ EL (y + az) ~ 1 for some a ∈ [a1, ac], the closest discretized value ai is determined, and the corresponding entry in the table is used.\nThe total running time for this method is linear in f and the discretization parameter c (which we take to be a constant).\nWe note that if exact values are desired, we have also developed an algorithm that runs in O (f log f) time that is not described here.\nIn the main loop, we compute the vector x, as defined in equation (16).\nThe nested loops iterate over the set of pages in F that are siblings of page j. Typically, the size of this set is bounded by a constant.\nFinally, for each page j, the scores vector is updated over the set of non-zero components k of the vector x with k ∈ L.\nThis set has size equal to the number of local siblings of page j, and is a subset of the total number of siblings of page j. Thus, each iteration of the main loop takes constant time, and the total running time of the main loop is O ($).\nSince we have assumed that the size of F will not grow larger than O (n), the total running time for the algorithm is O (n).\nALGORITHM 3: Node Selection via Stochastic Complementation.\nSC-SELECT (F, Fout, f, k) Input: F: zero-one adjacency matrix of size f corresponding to the current local subgraph, Fout: zero-one outlink matrix from F to global subgraph, f: PageRank of F, k: number of pages to return Output: pages: set of k pages to crawl next {Compute outlink sums for local subgraph}\nReturn k pages with highest scores\n5.2.2 PageRank Flows\nWe now present an intuitive analysis of the stochastic complementation method by decomposing the change in PageRank in terms of ` leaks' and ` flows'.\nThis analysis is motivated by the decomposition given in (15).\nPageRank ` flow' is the increase in the local PageRanks originating from global page j.\nThe flows are represented by the non-negative vector (˜uTj f) z (equations (15) and (18)).\nThe scalar ˜uTj f can be thought of as the total amount of PageRank flow that page j has available to distribute.\nThe vector z dictates how the flow is allocated to the local domain; the flow that local page k receives is proportional to (within a constant factor due to the random surfer vector) the expected number of its inlinks.\nThe PageRank ` leaks' represent the decrease in PageRank resulting from the addition of page j.\nThe leakage can be quantified in terms of the non-positive vectors x and y (equations (16) and (17)).\nFor vector x, we can see from equation (19) that the amount of PageRank leaked by a local page is proportional to the weighted sum of the Page\nResearch Track Paper\nRanks of its siblings.\nThus, pages that have siblings with higher PageRanks (and low outlink counts) will experience more leakage.\nThe leakage caused by y is an artifact of the random surfer vector.\nWe will next show that if only the ` flow' term, (˜uTj f) z, is considered, then the resulting method is very similar to a heuristic proposed by Cho et al. [6] that has been widely used for the \"Crawling Through URL Ordering\" problem.\nThis heuristic is computationally cheaper, but as we will see later, not as effective as the Stochastic Complementation method.\nOur node selection strategy chooses global nodes that have the largest influence (equation (7)).\nIf this influence is approximated using only ` flows', the optimal node j ∗ is:\nThe resulting page selection score can be expressed as a sum of the PageRanks of each local page k that links to j, where each PageRank value is normalized by o [k] +1.\nInterestingly, the normalization that arises in our method differs from the heuristic given in [6], which normalizes by o [k].\nThe algorithm PF-SELECT, which is omitted due to lack of space, first computes the quantity fT (DF + diag (uj)) − 1uj for each global page j, and then returns the pages with the k largest scores.\nTo see that the running time for this algorithm is O (n), note that the computation involved in this method is a subset of that needed for the SC-SELECT method (Algorithm 3), which was shown to have a running time of O (n).\n6.\nEXPERIMENTS\nIn this section, we provide experimental evidence to verify the effectiveness of our algorithms.\nWe first outline our experimental methodology and then provide results across a variety of local domains.\n6.1 Methodology\nGiven the limited resources available at an academic institution, crawling a section of the web that is of the same magnitude as that indexed by Google or Yahoo! is clearly infeasible.\nThus, for a given local domain, we approximate the global graph by crawling a local neighborhood around the domain that is several orders of magnitude larger than the local subgraph.\nEven though such a graph is still orders of magnitude smaller than the ` true' global graph, we contend that, even if there exist some highly influential pages that are very far away from our local domain, it is unrealistic for any local node selection algorithm to find them.\nSuch pages also tend to be highly unrelated to pages within the local domain.\nWhen explaining our node selection strategies in section 5, we made the simplifying assumption that our local graph contained no dangling nodes.\nThis assumption was only made to ease our analysis.\nOur implementation efficiently handles dangling links by replacing each zero column of our adjacency matrix with the uniform vector.\nWe evaluate the algorithm using the two node selection strategies given in Section 5.2, and also against the following baseline methods:\n• RANDOM: Nodes are chosen uniformly at random among the known global nodes.\n• OUTLiNKCOUNT: Global nodes with the highest number of outlinks from the local domain are chosen.\nAt each iteration of the FiNDGLOBALPR algorithm, we evaluate performance by computing the difference between the current PageRank estimate of the local domain, ELf ~ ELf ~ 1, and the global PageRank of the local domain ELg ~ ELg ~ 1.\nAll PageRank calculations were performed using the uniform random surfer vector.\nAcross all experiments, we set the random surfer parameter α, to be .85, and used a convergence threshold of 10 − 6.\nWe evaluate the difference between the local and global PageRank vectors using three different metrics: the L1 and L ∞ norms, and Kendall's tau.\nThe L1 norm measures the sum of the absolute value of the differences between the two vectors, and the L ∞ norm measures the absolute value of the largest difference.\nKendall's tau metric is a popular rank correlation measure used to compare PageRanks [2, 11].\nThis metric can be computed by counting the number of pairs of pairs that agree in ranking, and subtracting from that the number of pairs of pairs that disagree in ranking.\nThe final value is then normalized by the total number of n such pairs, resulting in a [− 1, 1] range, where\na negative score signifies anti-correlation among rankings, and values near one correspond to strong rank correlation.\n6.2 Results\nOur experiments are based on two large web crawls and were downloaded using the web crawler that is part of the Nutch open source search engine project [18].\nAll crawls were restricted to only ` http' pages, and to limit the number of dynamically generated pages that we crawl, we ignored all pages with urls containing any of the characters `? '\n, ` *', ` @', or ` ='.\nThe first crawl, which we will refer to as the ` edu' dataset, was seeded by homepages of the top 100 graduate computer science departments in the USA, as rated by the US News and World Report [16], and also by the home pages of their respective institutions.\nA crawl of depth 5 was performed, restricted to pages within the ‘.edu' domain, resulting in a graph with approximately 4.7 million pages and 22.9 million links.\nThe second crawl was seeded by the set of pages under the ` politics' hierarchy in the dmoz open directory project [17].\nWe crawled all pages up to four links away, which yielded a graph with 4.4 million pages and 17.3 million links.\nWithin the ` edu' crawl, we identified the five site-specific domains corresponding to the websites of the top five graduate computer science departments, as ranked by the US News and World Report.\nThis yielded local domains of various sizes, from 10,626 (UIUC) to 59,895 (Berkeley).\nFor each of these site-specific domains with size n, we performed 50 iterations of the FiNDGLOBALPR algorithm to crawl a total of 2n additional nodes.\nFigure 2 (a) gives the (L1) difference from the PageRank estimate at each iteration to the global PageRank, for the Berkeley local domain.\nThe performance of this dataset was representative of the typical performance across the five computer science sitespecific local domains.\nInitially, the L1 difference between the global and local PageRanks ranged from .0469 (Stanford) to .149 (MIT).\nFor the first several iterations, the\nFigure 2: L1 difference between the estimated and true global PageRanks for (a) Berkeley's computer science website, (b) the site-specific domain, www.enterstageright.com, and (c) the ` politics' topic-specific domain.\nThe stochastic complement method outperforms all other methods across various domains.\nthree link-based methods all outperform the random selection heuristic.\nAfter these initial iterations, the random heuristic tended to be more competitive with (or even outperform, as in the Berkeley local domain) the outlink count and PageRank flow heuristics.\nIn all tests, the stochastic complementation method either outperformed, or was competitive with, the other methods.\nTable 1 gives the average difference between the final estimated global PageRanks and the true global PageRanks for various distance measures.\nTable 1: Average final performance of various node\nselection strategies for the five site-specific computer science local domains.\nNote that Kendall's Tau measures similarity, while the other metrics are dissimilarity measures.\nStochastic Complementation clearly outperforms the other methods in all metrics.\nWithin the ` politics' dataset, we also performed two sitespecific tests for the largest websites in the crawl: www.adamsmith.org, the website for the London based Adam Smith Institute, and www.enterstageright.com, an online conservative journal.\nAs with the ` edu' local domains, we ran our algorithm for 50 iterations, crawling a total of 2n nodes.\nFigure 2 (b) plots the results for the www.enterstageright.com domain.\nIn contrast to the ` edu' local domains, the RANDOM and OUTLINKCOUNT methods were not competitive with either the SC-SELECT or the PF-SELECT methods.\nAmong all datasets and all node selection methods, the stochastic complementation method was most impressive in this dataset, realizing a final estimate that differed only .0279 from the global PageRank, a ten-fold improvement over the initial local PageRank difference of .299.\nFor the Adam Smith local domain, the initial difference between the local and global PageRanks was .148, and the final estimates given by the SC-SELECT, PF-SELECT, OUTLINKCOUNT, and RANDOM methods were .0208, .0193, .0222, and .0356, respectively.\nWithin the ` politics' dataset, we constructed four topicspecific local domains.\nThe first domain consisted of all pages in the dmoz politics category, and also all pages within each of these sites up to two links away.\nThis yielded a local domain of 90,811 pages, and the results are given in figure 2 (c).\nBecause of the larger size of the topic-specific domains, we ran our algorithm for only 25 iterations to crawl a total of n nodes.\nWe also created topic-specific domains from three political sub-topics: liberalism, conservatism, and socialism.\nThe pages in these domains were identified by their corresponding dmoz categories.\nFor each sub-topic, we set the local domain to be all pages within three links from the corresponding dmoz category pages.\nTable 2 summarizes the performance of these three topic-specific domains, and also the larger political domain.\nTo quantify a global page j's effect on the global PageRank values of pages in the local domain, we define page j's impact to be its PageRank value, g [j], normalized by the fraction of its outlinks pointing to the local domain:\nwhere, oL [j] is the number of outlinks from page j to pages in the local domain L, and o [j] is the total number of j's outlinks.\nIn terms of the random surfer model, the impact of page j is the probability that the random surfer (1) is currently at global page j in her random walk and (2) takes an outlink to a local page, given that she has already decided not to jump to a random page.\nFor the politics local domain, we found that many of the pages with high impact were in fact political pages that should have been included in the dmoz politics topic, but were not.\nFor example, the two most influential global pages were the political search engine www.askhenry.com, and the home page of the online political magazine, www.policyreview.com.\nAmong non-political pages, the home page of the journal \"Education Next\" was most influential.\nThe journal is freely available online and contains articles regarding various aspect of K-12 education in America.\nTo provide some anecdotal evidence for the effectiveness of our page selection methods, we note that the SC-SELECT method chose 11 pages within the www.educationnext.org domain, the PF-SELECT method discovered 7 such pages, while the OUTLINKCOUNT and RANDOM methods found only 6 pages each.\nFor the conservative political local domain, the socialist website www.ornery.org had a very high impact score.\nThis · g [j] l\nTable 2: Final performance among node selection strategies for the four political topic-specific crawls.\nNote that Kendall's Tau measures similarity, while the other metrics are dissimilarity measures.\nwas largely due to a link from the front page of this site to an article regarding global warming published by the National Center for Public Policy Research, a conservative research group in Washington, DC.\nNot surprisingly, the global PageRank of this article (which happens to be on the home page of the NCCPR, www.nationalresearch.com), was approximately .002, whereas the local PageRank of this page was only .00158.\nThe SC-SELECT method yielded a global PageRank estimate of approximately .00182, the PFSELECT method estimated a value of .00167, and the RANDOM and OUTLiNKCOUNT methods yielded values of .01522 and .00171, respectively.\n7.\nRELATED WORK\nThe node selection framework we have proposed is similar to the url ordering for crawling problem proposed by Cho et al. in [6].\nWhereas our framework seeks to minimize the difference between the global and local PageRank, the objective used in [6] is to crawl the most highly (globally) ranked pages first.\nThey propose several node selection algorithms, including the outlink count heuristic, as well as a variant of our PF-SELECT algorithm which they refer to as the ` PageRank ordering metric'.\nThey found this method to be most effective in optimizing their objective, as did a recent survey of these methods by Baeza-Yates et al. [1].\nBoldi et al. also experiment within a similar crawling framework in [2], but quantify their results by comparing Kendall's rank correlation between the PageRanks of the current set of crawled pages and those of the entire global graph.\nThey found that node selection strategies that crawled pages with the highest global PageRank first actually performed worse (with respect to Kendall's Tau correlation between the local and global PageRanks) than basic depth first or breadth first strategies.\nHowever, their experiments differ from our work in that our node selection algorithms do not use (or have access to) global PageRank values.\nMany algorithmic improvements for computing exact PageRank values have been proposed [9, 10, 14].\nIf such algorithms are used to compute the global PageRanks of our local domain, they would all require O (N) computation, storage, and bandwidth, where N is the size of the global domain.\nThis is in contrast to our method, which approximates the global PageRank and scales linearly with the size of the local domain.\nWang and Dewitt [22] propose a system where the set of web servers that comprise the global domain communicate with each other to compute their respective global PageRanks.\nFor a given web server hosting n pages, the computational, bandwidth, and storage requirements are also linear in n.\nOne drawback of this system is that the number of distinct web servers that comprise the global domain can be very large.\nFor example, our ` edu' dataset contains websites from over 3,200 different universities; coordinating such a system among a large number of sites can be very difficult.\nGan, Chen, and Suel propose a method for estimating the PageRank of a single page [5] which uses only constant bandwidth, computation, and space.\nTheir approach relies on the availability of a remote connectivity server that can supply the set of inlinks to a given page, an assumption not used in our framework.\nThey experimentally show that a reasonable estimate of the node's PageRank can be obtained by visiting at most a few hundred nodes.\nUsing their algorithm for our problem would require that either the entire global domain first be downloaded or a connectivity server be used, both of which would lead to very large web graphs.\n8.\nCONCLUSIONS AND FUTURE WORK\nThe internet is growing exponentially, and in order to navigate such a large repository as the web, global search engines have established themselves as a necessity.\nAlong with the ubiquity of these large-scale search engines comes an increase in search users' expectations.\nBy providing complete and isolated coverage of a particular web domain, localized search engines are an effective outlet to quickly locate content that could otherwise be difficult to find.\nIn this work, we contend that the use of global PageRank in a localized search engine can improve performance.\nTo estimate the global PageRank, we have proposed an iterative node selection framework where we select which pages from the global frontier to crawl next.\nOur primary contribution is our stochastic complementation page selection algorithm.\nThis method crawls nodes that will most significantly impact the local domain and has running time linear in the number of nodes in the local domain.\nExperimentally, we validate these methods across a diverse set of local domains, including seven site-specific domains and four topic-specific domains.\nWe conclude that by crawling an additional n or 2n pages, our methods find an estimate of the global PageRanks that is up to ten times better than just using the local PageRanks.\nFurthermore, we demonstrate that our algorithm consistently outperforms other existing heuristics.\nResearch Track Paper\nOften times, topic-specific domains are discovered using a focused web crawler which considers a page's content in conjunction with link anchor text to decide which pages to crawl next [4].\nAlthough such crawlers have proven to be quite effective in discovering topic-related content, many irrelevant pages are also crawled in the process.\nTypically, these pages are deleted and not indexed by the localized search engine.\nThese pages can of course provide valuable information regarding the global PageRank of the local domain.\nOne way to integrate these pages into our framework is to start the FINDGLOBALPR algorithm with the current subgraph F equal to the set of pages that were crawled by the focused crawler.\nThe global PageRank estimation framework, along with the node selection algorithms presented, all require O (n) computation per iteration and bandwidth proportional to the number of pages crawled, Tk.\nIf the number of iterations T is relatively small compared to the number of pages crawled per iteration, k, then the bottleneck of the algorithm will be the crawling phase.\nHowever, as the number of iterations increases (relative to k), the bottleneck will reside in the node selection computation.\nIn this case, our algorithms would benefit from constant factor optimizations.\nRecall that the FINDGLOBALPR algorithm (Algorithm 2) requires that the PageRanks of the current expanded local domain be recomputed in each iteration.\nRecent work by Langville and Meyer [12] gives an algorithm to quickly recompute PageRanks of a given webgraph if a small number of nodes are added.\nThis algorithm was shown to give speedup of five to ten times on some datasets.\nWe plan to investigate this and other such optimizations as future work.\nIn this paper, we have objectively evaluated our methods by measuring how close our global PageRank estimates are to the actual global PageRanks.\nTo determine the benefit of using global PageRanks in a localized search engine, we suggest a user study in which users are asked to rate the quality of search results for various search queries.\nFor some queries, only the local PageRanks are used in ranking, and for the remaining queries, local PageRanks and the approximate global PageRanks, as computed by our algorithms, are used.\nThe results of such a study can then be analyzed to determine the added benefit of using the global PageRanks computed by our methods, over just using the local PageRanks.\nAcknowledgements.\nThis research was supported by NSF grant CCF-0431257, NSF Career Award ACI-0093404, and a grant from Sabre, Inc. ."},"lvl-3":{"kind":"string","value":"Estimating the Global PageRank of Web Communities\nABSTRACT\nLocalized search engines are small-scale systems that index a particular community on the web.\nThey offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search capability over their relevant domains.\nOne disadvantage such systems have over large-scale search engines is the lack of global PageRank values.\nSuch information is needed to assess the value of pages in the localized search domain within the context of the web as a whole.\nIn this paper, we present well-motivated algorithms to estimate the global PageRank values of a local domain.\nThe algorithms are all highly scalable in that, given a local domain of size n, they use O (n) resources that include computation time, bandwidth, and storage.\nWe test our methods across a variety of localized domains, including site-specific domains and topic-specific domains.\nWe demonstrate that by crawling as few as n or 2n additional pages, our methods can give excellent global PageRank estimates.\n1.\nINTRODUCTION\nLocalized search engines are small-scale search engines that index only a single community of the web.\nSuch communities can be site-specific domains, such as pages within\nthe cs.utexas.edu domain, or topic-related communities--for example, political websites.\nCompared to the web graph crawled and indexed by large-scale search engines, the size of such local communities is typically orders of magnitude smaller.\nConsequently, the computational resources needed to build such a search engine are also similarly lighter.\nBy restricting themselves to smaller, more manageable sections of the web, localized search engines can also provide more precise and complete search capabilities over their respective domains.\nOne drawback of localized indexes is the lack of global information needed to compute link-based rankings.\nThe PageRank algorithm [3], has proven to be an effective such measure.\nIn general, the PageRank of a given page is dependent on pages throughout the entire web graph.\nIn the context of a localized search engine, if the PageRanks are computed using only the local subgraph, then we would expect the resulting PageRanks to reflect the perceived popularity within the local community and not of the web as a whole.\nFor example, consider a localized search engine that indexes political pages with conservative views.\nA person wishing to research the opinions on global warming within the conservative political community may encounter numerous such opinions across various websites.\nIf only local PageRank values are available, then the search results will reflect only strongly held beliefs within the community.\nHowever, if global PageRanks are also available, then the results can additionally reflect outsiders' views of the conservative community (those documents that liberals most often access within the conservative community).\nThus, for many localized search engines, incorporating global PageRanks can improve the quality of search results.\nHowever, the number of pages a local search engine indexes is typically orders of magnitude smaller than the number of pages indexed by their large-scale counterparts.\nLocalized search engines do not have the bandwidth, storage capacity, or computational power to crawl, download, and compute the global PageRanks of the entire web.\nIn this work, we present a method of approximating the global PageRanks of a local domain while only using resources of the same order as those needed to compute the PageRanks of the local subgraph.\nOur proposed method looks for a supergraph of our local subgraph such that the local PageRanks within this supergraph are close to the true global PageRanks.\nWe construct this supergraph by iteratively crawling global pages on the current web frontier--i.e., global pages with inlinks from pages that have already been crawled.\nIn order to provide\na good approximation to the global PageRanks, care must be taken when choosing which pages to crawl next; in this paper, we present a well-motivated page selection algorithm that also performs well empirically.\nThis algorithm is derived from a well-defined problem objective and has a running time linear in the number of local nodes.\nWe experiment across several types of local subgraphs, including four topic related communities and several sitespecific domains.\nTo evaluate performance, we measure the difference between the current global PageRank estimate and the global PageRank, as a function of the number of pages crawled.\nWe compare our algorithm against several heuristics and also against a baseline algorithm that chooses pages at random, and we show that our method outperforms these other methods.\nFinally, we empirically demonstrate that, given a local domain of size n, we can provide good approximations to the global PageRank values by crawling at most n or 2n additional pages.\nThe paper is organized as follows.\nSection 2 gives an overview of localized search engines and outlines their advantages over global search.\nSection 3 provides background on the PageRank algorithm.\nSection 4 formally defines our problem, and section 5 presents our page selection criteria and derives our algorithms.\nSection 6 provides experimental results, section 7 gives an overview of related work, and, finally, conclusions are given in section 8.\n2.\nLOCALIZED SEARCH ENGINES\nResearch Track Paper\n3.\nPAGERANK OVERVIEW\nResearch Track Paper\n4.\nPROBLEM DEFINITION\nALGORITHM 2: The FINDGLOBALPR algorithm.\n5.\nNODE SELECTION\n5.1 Formulation\nResearch Track Paper\n5.2 Computation\nResearch Track Paper\n5.2.1 Stochastic Complementation\n5.2.2 PageRank Flows\nResearch Track Paper\n6.\nEXPERIMENTS\n6.1 Methodology\n6.2 Results\n7.\nRELATED WORK\nThe node selection framework we have proposed is similar to the url ordering for crawling problem proposed by Cho et al. in [6].\nWhereas our framework seeks to minimize the difference between the global and local PageRank, the objective used in [6] is to crawl the most highly (globally) ranked pages first.\nThey propose several node selection algorithms, including the outlink count heuristic, as well as a variant of our PF-SELECT algorithm which they refer to as the ` PageRank ordering metric'.\nThey found this method to be most effective in optimizing their objective, as did a recent survey of these methods by Baeza-Yates et al. [1].\nBoldi et al. also experiment within a similar crawling framework in [2], but quantify their results by comparing Kendall's rank correlation between the PageRanks of the current set of crawled pages and those of the entire global graph.\nThey found that node selection strategies that crawled pages with the highest global PageRank first actually performed worse (with respect to Kendall's Tau correlation between the local and global PageRanks) than basic depth first or breadth first strategies.\nHowever, their experiments differ from our work in that our node selection algorithms do not use (or have access to) global PageRank values.\nMany algorithmic improvements for computing exact PageRank values have been proposed [9, 10, 14].\nIf such algorithms are used to compute the global PageRanks of our local domain, they would all require O (N) computation, storage, and bandwidth, where N is the size of the global domain.\nThis is in contrast to our method, which approximates the global PageRank and scales linearly with the size of the local domain.\nWang and Dewitt [22] propose a system where the set of web servers that comprise the global domain communicate with each other to compute their respective global PageRanks.\nFor a given web server hosting n pages, the computational, bandwidth, and storage requirements are also linear in n.\nOne drawback of this system is that the number of distinct web servers that comprise the global domain can be very large.\nFor example, our ` edu' dataset contains websites from over 3,200 different universities; coordinating such a system among a large number of sites can be very difficult.\nGan, Chen, and Suel propose a method for estimating the PageRank of a single page [5] which uses only constant bandwidth, computation, and space.\nTheir approach relies on the availability of a remote connectivity server that can supply the set of inlinks to a given page, an assumption not used in our framework.\nThey experimentally show that a reasonable estimate of the node's PageRank can be obtained by visiting at most a few hundred nodes.\nUsing their algorithm for our problem would require that either the entire global domain first be downloaded or a connectivity server be used, both of which would lead to very large web graphs.\n8.\nCONCLUSIONS AND FUTURE WORK\nThe internet is growing exponentially, and in order to navigate such a large repository as the web, global search engines have established themselves as a necessity.\nAlong with the ubiquity of these large-scale search engines comes an increase in search users' expectations.\nBy providing complete and isolated coverage of a particular web domain, localized search engines are an effective outlet to quickly locate content that could otherwise be difficult to find.\nIn this work, we contend that the use of global PageRank in a localized search engine can improve performance.\nTo estimate the global PageRank, we have proposed an iterative node selection framework where we select which pages from the global frontier to crawl next.\nOur primary contribution is our stochastic complementation page selection algorithm.\nThis method crawls nodes that will most significantly impact the local domain and has running time linear in the number of nodes in the local domain.\nExperimentally, we validate these methods across a diverse set of local domains, including seven site-specific domains and four topic-specific domains.\nWe conclude that by crawling an additional n or 2n pages, our methods find an estimate of the global PageRanks that is up to ten times better than just using the local PageRanks.\nFurthermore, we demonstrate that our algorithm consistently outperforms other existing heuristics.\nResearch Track Paper\nOften times, topic-specific domains are discovered using a focused web crawler which considers a page's content in conjunction with link anchor text to decide which pages to crawl next [4].\nAlthough such crawlers have proven to be quite effective in discovering topic-related content, many irrelevant pages are also crawled in the process.\nTypically, these pages are deleted and not indexed by the localized search engine.\nThese pages can of course provide valuable information regarding the global PageRank of the local domain.\nOne way to integrate these pages into our framework is to start the FINDGLOBALPR algorithm with the current subgraph F equal to the set of pages that were crawled by the focused crawler.\nThe global PageRank estimation framework, along with the node selection algorithms presented, all require O (n) computation per iteration and bandwidth proportional to the number of pages crawled, Tk.\nIf the number of iterations T is relatively small compared to the number of pages crawled per iteration, k, then the bottleneck of the algorithm will be the crawling phase.\nHowever, as the number of iterations increases (relative to k), the bottleneck will reside in the node selection computation.\nIn this case, our algorithms would benefit from constant factor optimizations.\nRecall that the FINDGLOBALPR algorithm (Algorithm 2) requires that the PageRanks of the current expanded local domain be recomputed in each iteration.\nRecent work by Langville and Meyer [12] gives an algorithm to quickly recompute PageRanks of a given webgraph if a small number of nodes are added.\nThis algorithm was shown to give speedup of five to ten times on some datasets.\nWe plan to investigate this and other such optimizations as future work.\nIn this paper, we have objectively evaluated our methods by measuring how close our global PageRank estimates are to the actual global PageRanks.\nTo determine the benefit of using global PageRanks in a localized search engine, we suggest a user study in which users are asked to rate the quality of search results for various search queries.\nFor some queries, only the local PageRanks are used in ranking, and for the remaining queries, local PageRanks and the approximate global PageRanks, as computed by our algorithms, are used.\nThe results of such a study can then be analyzed to determine the added benefit of using the global PageRanks computed by our methods, over just using the local PageRanks.\nAcknowledgements.\nThis research was supported by NSF grant CCF-0431257, NSF Career Award ACI-0093404, and a grant from Sabre, Inc. ."},"lvl-4":{"kind":"string","value":"Estimating the Global PageRank of Web Communities\nABSTRACT\nLocalized search engines are small-scale systems that index a particular community on the web.\nThey offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search capability over their relevant domains.\nOne disadvantage such systems have over large-scale search engines is the lack of global PageRank values.\nSuch information is needed to assess the value of pages in the localized search domain within the context of the web as a whole.\nIn this paper, we present well-motivated algorithms to estimate the global PageRank values of a local domain.\nThe algorithms are all highly scalable in that, given a local domain of size n, they use O (n) resources that include computation time, bandwidth, and storage.\nWe test our methods across a variety of localized domains, including site-specific domains and topic-specific domains.\nWe demonstrate that by crawling as few as n or 2n additional pages, our methods can give excellent global PageRank estimates.\n1.\nINTRODUCTION\nLocalized search engines are small-scale search engines that index only a single community of the web.\nSuch communities can be site-specific domains, such as pages within\nthe cs.utexas.edu domain, or topic-related communities--for example, political websites.\nCompared to the web graph crawled and indexed by large-scale search engines, the size of such local communities is typically orders of magnitude smaller.\nConsequently, the computational resources needed to build such a search engine are also similarly lighter.\nBy restricting themselves to smaller, more manageable sections of the web, localized search engines can also provide more precise and complete search capabilities over their respective domains.\nOne drawback of localized indexes is the lack of global information needed to compute link-based rankings.\nThe PageRank algorithm [3], has proven to be an effective such measure.\nIn general, the PageRank of a given page is dependent on pages throughout the entire web graph.\nFor example, consider a localized search engine that indexes political pages with conservative views.\nA person wishing to research the opinions on global warming within the conservative political community may encounter numerous such opinions across various websites.\nIf only local PageRank values are available, then the search results will reflect only strongly held beliefs within the community.\nThus, for many localized search engines, incorporating global PageRanks can improve the quality of search results.\nHowever, the number of pages a local search engine indexes is typically orders of magnitude smaller than the number of pages indexed by their large-scale counterparts.\nLocalized search engines do not have the bandwidth, storage capacity, or computational power to crawl, download, and compute the global PageRanks of the entire web.\nIn this work, we present a method of approximating the global PageRanks of a local domain while only using resources of the same order as those needed to compute the PageRanks of the local subgraph.\nOur proposed method looks for a supergraph of our local subgraph such that the local PageRanks within this supergraph are close to the true global PageRanks.\nWe construct this supergraph by iteratively crawling global pages on the current web frontier--i.e., global pages with inlinks from pages that have already been crawled.\nIn order to provide\na good approximation to the global PageRanks, care must be taken when choosing which pages to crawl next; in this paper, we present a well-motivated page selection algorithm that also performs well empirically.\nThis algorithm is derived from a well-defined problem objective and has a running time linear in the number of local nodes.\nWe experiment across several types of local subgraphs, including four topic related communities and several sitespecific domains.\nTo evaluate performance, we measure the difference between the current global PageRank estimate and the global PageRank, as a function of the number of pages crawled.\nWe compare our algorithm against several heuristics and also against a baseline algorithm that chooses pages at random, and we show that our method outperforms these other methods.\nFinally, we empirically demonstrate that, given a local domain of size n, we can provide good approximations to the global PageRank values by crawling at most n or 2n additional pages.\nThe paper is organized as follows.\nSection 2 gives an overview of localized search engines and outlines their advantages over global search.\nSection 3 provides background on the PageRank algorithm.\nSection 4 formally defines our problem, and section 5 presents our page selection criteria and derives our algorithms.\n7.\nRELATED WORK\nThe node selection framework we have proposed is similar to the url ordering for crawling problem proposed by Cho et al. in [6].\nWhereas our framework seeks to minimize the difference between the global and local PageRank, the objective used in [6] is to crawl the most highly (globally) ranked pages first.\nThey found that node selection strategies that crawled pages with the highest global PageRank first actually performed worse (with respect to Kendall's Tau correlation between the local and global PageRanks) than basic depth first or breadth first strategies.\nHowever, their experiments differ from our work in that our node selection algorithms do not use (or have access to) global PageRank values.\nMany algorithmic improvements for computing exact PageRank values have been proposed [9, 10, 14].\nIf such algorithms are used to compute the global PageRanks of our local domain, they would all require O (N) computation, storage, and bandwidth, where N is the size of the global domain.\nThis is in contrast to our method, which approximates the global PageRank and scales linearly with the size of the local domain.\nWang and Dewitt [22] propose a system where the set of web servers that comprise the global domain communicate with each other to compute their respective global PageRanks.\nFor a given web server hosting n pages, the computational, bandwidth, and storage requirements are also linear in n.\nOne drawback of this system is that the number of distinct web servers that comprise the global domain can be very large.\nGan, Chen, and Suel propose a method for estimating the PageRank of a single page [5] which uses only constant bandwidth, computation, and space.\nUsing their algorithm for our problem would require that either the entire global domain first be downloaded or a connectivity server be used, both of which would lead to very large web graphs.\n8.\nCONCLUSIONS AND FUTURE WORK\nThe internet is growing exponentially, and in order to navigate such a large repository as the web, global search engines have established themselves as a necessity.\nAlong with the ubiquity of these large-scale search engines comes an increase in search users' expectations.\nBy providing complete and isolated coverage of a particular web domain, localized search engines are an effective outlet to quickly locate content that could otherwise be difficult to find.\nIn this work, we contend that the use of global PageRank in a localized search engine can improve performance.\nTo estimate the global PageRank, we have proposed an iterative node selection framework where we select which pages from the global frontier to crawl next.\nOur primary contribution is our stochastic complementation page selection algorithm.\nThis method crawls nodes that will most significantly impact the local domain and has running time linear in the number of nodes in the local domain.\nExperimentally, we validate these methods across a diverse set of local domains, including seven site-specific domains and four topic-specific domains.\nWe conclude that by crawling an additional n or 2n pages, our methods find an estimate of the global PageRanks that is up to ten times better than just using the local PageRanks.\nFurthermore, we demonstrate that our algorithm consistently outperforms other existing heuristics.\nResearch Track Paper\nAlthough such crawlers have proven to be quite effective in discovering topic-related content, many irrelevant pages are also crawled in the process.\nTypically, these pages are deleted and not indexed by the localized search engine.\nThese pages can of course provide valuable information regarding the global PageRank of the local domain.\nOne way to integrate these pages into our framework is to start the FINDGLOBALPR algorithm with the current subgraph F equal to the set of pages that were crawled by the focused crawler.\nThe global PageRank estimation framework, along with the node selection algorithms presented, all require O (n) computation per iteration and bandwidth proportional to the number of pages crawled, Tk.\nIf the number of iterations T is relatively small compared to the number of pages crawled per iteration, k, then the bottleneck of the algorithm will be the crawling phase.\nIn this case, our algorithms would benefit from constant factor optimizations.\nRecall that the FINDGLOBALPR algorithm (Algorithm 2) requires that the PageRanks of the current expanded local domain be recomputed in each iteration.\nRecent work by Langville and Meyer [12] gives an algorithm to quickly recompute PageRanks of a given webgraph if a small number of nodes are added.\nThis algorithm was shown to give speedup of five to ten times on some datasets.\nWe plan to investigate this and other such optimizations as future work.\nIn this paper, we have objectively evaluated our methods by measuring how close our global PageRank estimates are to the actual global PageRanks.\nTo determine the benefit of using global PageRanks in a localized search engine, we suggest a user study in which users are asked to rate the quality of search results for various search queries.\nFor some queries, only the local PageRanks are used in ranking, and for the remaining queries, local PageRanks and the approximate global PageRanks, as computed by our algorithms, are used.\nThe results of such a study can then be analyzed to determine the added benefit of using the global PageRanks computed by our methods, over just using the local PageRanks.\nAcknowledgements."}}},{"rowIdx":89,"cells":{"id":{"kind":"string","value":"H-54"},"title":{"kind":"string","value":"Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature"},"abstract":{"kind":"string","value":"This paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effectiveness in retrieving biomedical literature. The effects of different types of domain-specific knowledge in performance contribution are examined. Based on the TREC platform, we show that appropriate use of domain-specific knowledge in a proposed conceptual retrieval model yields about 23% improvement over the best reported result in passage retrieval in the Genomics Track of TREC 2006."},"keyphrases":{"kind":"list like","value":["passag extract","domain-specif knowledg","retriev model","keyword search","document collect","passag-level inform retriev","queri concept","conceptu ir model","passag map","aspect map","document map","document retriev","biomed document"],"string":"[\n \"passag extract\",\n \"domain-specif knowledg\",\n \"retriev model\",\n \"keyword search\",\n \"document collect\",\n \"passag-level inform retriev\",\n \"queri concept\",\n \"conceptu ir model\",\n \"passag map\",\n \"aspect map\",\n \"document map\",\n \"document retriev\",\n \"biomed document\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","U","U","M","M","R","M","U","U","M","M"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"U\",\n \"U\",\n \"M\",\n \"M\",\n \"R\",\n \"M\",\n \"U\",\n \"U\",\n \"M\",\n \"M\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature Wei Zhou, Clement Yu Department of Computer Science University of Illinois at Chicago wzhou8@uic.edu, yu@cs.uic.edu Neil Smalheiser, Vetle Torvik Department of Psychiatry and Psychiatric Institute (MC912) University of Illinois at Chicago {neils, vtorvik}@uic.\nedu Jie Hong Division of Epidemiology and Biostatistics, School of Public health University of Illinois at Chicago jhong20@uic.edu ABSTRACT This paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effectiveness in retrieving biomedical literature.\nThe effects of different types of domain-specific knowledge in performance contribution are examined.\nBased on the TREC platform, we show that appropriate use of domainspecific knowledge in a proposed conceptual retrieval model yields about 23% improvement over the best reported result in passage retrieval in the Genomics Track of TREC 2006.\nCategories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval - retrieval models, query formulation, information filtering.\nH.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing - thesauruses.\nGeneral Terms Algorithms, Performance, Experimentation.\n1.\nINTRODUCTION Biologists search for literature on a daily basis.\nFor most biologists, PubMed, an online service of U.S. National Library of Medicine (NLM), is the most commonly used tool for searching the biomedical literature.\nPubMed allows for keyword search by using Boolean operators.\nFor example, if one desires documents on the use of the drug propanolol in the disease hypertension, a typical PubMed query might be propanolol AND hypertension, which will return all the documents having the two keywords.\nKeyword search in PubMed is effective if the query is well-crafted by the users using their expertise.\nHowever, information needs of biologists, in some cases, are expressed as complex questions [8][9], which PubMed is not designed to handle.\nWhile NLM does maintain an experimental tool for free-text queries [6], it is still based on PubMed keyword search.\nThe Genomics track of the 2006 Text REtrieval Conference (TREC) provides a common platform to assess the methods and techniques proposed by various groups for biomedical information retrieval.\nThe queries were collected from real biologists and they are expressed as complex questions, such as How do mutations in the Huntingtin gene affect Huntington``s disease?\n.\nThe document collection contains 162,259 Highwire full-text documents in HTML format.\nSystems from participating groups are expected to find relevant passages within the full-text documents.\nA passage is defined as any span of text that does not include the HTML paragraph tag (i.e., <P> or </P>).\nWe approached the problem by utilizing domain-specific knowledge in a conceptual retrieval model.\nDomain-specific knowledge, in this paper, refers to information about concepts and relationships between concepts in a certain domain.\nWe assume that appropriate use of domain-specific knowledge might improve the effectiveness of retrieval.\nFor example, given a query What is the role of gene PRNP in the Mad Cow Disease?\n, expanding the gene symbol PRNP with its synonyms Prp, PrPSc, and prion protein, more relevant documents might be retrieved.\nPubMed and many other biomedical systems [8][9][10][13] also make use of domain-specific knowledge to improve retrieval effectiveness.\nIntuitively, retrieval on the level of concepts should outperform bag-of-words approaches, since the semantic relationships among words in a concept are utilized.\nIn some recent studies [13][15], positive results have been reported for this hypothesis.\nIn this paper, concepts are entry terms of the ontology Medical Subject Headings (MeSH), a controlled vocabulary maintained by NLM for indexing biomedical literature, or gene symbols in the Entrez gene database also from NLM.\nA concept could be a word, such as the gene symbol PRNP, or a phrase, such as Mad cow diseases.\nIn the conceptual retrieval model presented in this paper, the similarity between a query and a document is measured on both concept and word levels.\nThis paper makes two contributions: 1.\nWe propose a conceptual approach to utilize domain-specific knowledge in an IR system to improve its effectiveness in retrieving biomedical literature.\nBased on this approach, our system achieved significant improvement (23%) over the best reported result in passage retrieval in the Genomics track of TREC 2006.\n2.\nWe examine the effects of utilizing concepts and of different types of domain-specific knowledge in performance contribution.\nThis paper is organized as follows: problem statement is given in the next section.\nThe techniques are introduced in section 3.\nIn section 4, we present the experimental results.\nRelated works are given in section 5 and finally, we conclude the paper in section 6.\n2.\nPROBLEM STATEMENT We describe the queries, document collection and the system output in this section.\nThe query set used in the Genomics track of TREC 2006 consists of 28 questions collected from real biologists.\nAs described in [8], these questions all have the following general format: Biological object (1.\n.\nm) Relationship ←⎯⎯⎯⎯→ Biological process (1.\n.\nn) (1) where a biological object might be a gene, protein, or gene mutation and a biological process can be a physiological process or disease.\nA question might involve multiple biological objects (m) and multiple biological processes (n).\nThese questions were derived from four templates (Table 2).\nTable 2 Query templates and examples in the Genomics track of TREC 2006 Template Example What is the role of gene in disease?\nWhat is the role of DRD4 in alcoholism?\nWhat effect does gene have on biological process?\nWhat effect does the insulin receptor gene have on tumorigenesis?\nHow do genes interact in organ function?\nHow do HMG and HMGB1 interact in hepatitis?\nHow does a mutation in gene influence biological process?\nHow does a mutation in Ret influence thyroid function?\nFeatures of the queries: 1) They are different from the typical Web queries and the PubMed queries, both of which usually consist of 1 to 3 keywords; 2) They are generated from structural templates which can be used by a system to identify the query components, the biological object or process.\nThe document collection contains 162,259 Highwire full-text documents in HTML format.\nThe output of the system is a list of passages ranked according to their similarities with the query.\nA passage is defined as any span of text that does not include the HTML paragraph tag (i.e., <P> or </P>).\nA passage could be a part of a sentence, a sentence, a set of consecutive sentences or a paragraph (i.e., the whole span of text that are inside of <P> and </P> HTML tags).\nThis is a passage-level information retrieval problem with the attempt to put biologists in contexts where relevant information is provided.\n3.\nTECHNIQUES AND METHODS We approached the problem by first retrieving the top-k most relevant paragraphs, then extracting passages from these paragraphs, and finally ranking the passages.\nIn this process, we employed several techniques and methods, which will be introduced in this section.\nFirst, we give two definitions: Definition 3.1 A concept is 1) a entry term in the MeSH ontology, or 2) a gene symbol in the Entrez gene database.\nThis definition of concept can be generalized to include other biomedical dictionary terms.\nDefinition 3.2 A semantic type is a category defined in the Semantic Network of the Unified Medical Language System (UMLS) [14].\nThe current release of the UMLS Semantic Network contains 135 semantic types such as Disease or Syndrome.\nEach entry term in the MeSH ontology is assigned one or more semantic types.\nEach gene symbol in the Entrez gene database maps to the semantic type Gene or Genome.\nIn addition, these semantic types are linked by 54 relationships.\nFor example, Antibiotic prevents Disease or Syndrome.\nThese relationships among semantic types represent general biomedical knowledge.\nWe utilized these semantic types and their relationships to identify related concepts.\nThe rest of this section is organized as follows: in section 3.1, we explain how the concepts are identified within a query.\nIn section 3.2, we specify five different types of domain-specific knowledge and introduce how they are compiled.\nIn section 3.3, we present our conceptual IR model.\nFinally, our strategy for passage extraction is described in section 3.4.\n3.1 Identifying concepts within a query A concept, defined in Definition 3.1, is a gene symbol or a MeSH term.\nWe make use of the query templates to identify gene symbols.\nFor example, the query How do HMG and HMGB1 interact in hepatitis?\nis derived from the template How do genes interact in organ function?\n.\nIn this case, HMG and HMGB1 will be identified as gene symbols.\nIn cases where the query templates are not provided, programs for recognition of gene symbols within texts are needed.\nWe use the query translation functionality of PubMed to extract MeSH terms in a query.\nThis is done by submitting the whole query to PubMed, which will then return a file in which the MeSH terms in the query are labeled.\nIn Table 3.1, three MeSH terms within the query What is the role of gene PRNP in the Mad cow disease?\nare found in the PubMed translation: ``encephalopathy, bovine spongiform'' for Mad cow disease, genes for gene, and role for role.\nTable 3.1 The PubMed translation of the query ``What is the role of gene PRNP in the Mad cow disease?''\n.\nTerm PubMed translation Mad cow disease ``bovine spongiform encephalopathy''[Text Word] OR ``encephalopathy, bovine spongiform''[MeSH Terms] OR Mad cow disease[Text Word] gene (``genes''[TIAB] NOT Medline[SB]) OR ``genes''[MeSH Terms] OR gene[Text Word] role ``role''[MeSH Terms] OR role[Text Word] 3.2 Compiling domain-specific knowledge In this paper, domain-specific knowledge refers to information about concepts and their relationships in a certain domain.\nWe used five types of domain-specific knowledge in the domain of genomics: Type 1.\nSynonyms (terms listed in the thesauruses that refer to the same meaning) Type 2.\nHypernyms (more generic terms, one level only) Type 3.\nHyponyms (more specific terms, one level only) Type 4.\nLexical variants (different forms of the same concept, such as abbreviations.\nThey are commonly used in the literature, but might not be listed in the thesauruses) Type 5.\nImplicitly related concepts (terms that are semantically related and also co-occur more frequently than being independent in the biomedical texts) Knowledge of type 1-3 is retrieved from the following two thesauruses: 1) MeSH, a controlled vocabulary maintained by NLM for indexing biomedical literature.\nThe 2007 version of MeSH contains information about 190,000 concepts.\nThese concepts are organized in a tree hierarchy; 2) Entrez Gene, one of the most widely used searchable databases of genes.\nThe current version of Entrez Gene contains information about 1.7 million genes.\nIt does not have a hierarchy.\nOnly synonyms are retrieved from Entrez Gene.\nThe compiling of type 4-5 knowledge is introduced in section 3.2.1 and 3.2.2, respectively.\n3.2.1 Lexical variants Lexical variants of gene symbols New gene symbols and their lexical variants are regularly introduced into the biomedical literature [7].\nHowever, many reference databases, such as UMLS and Entrez Gene, may not be able to keep track of all this kind of variants.\nFor example, for the gene symbol ``NF-kappa B'', at least 5 different lexical variants can be found in the biomedical literature: ``NF-kappaB'', ``NFkappaB'', ``NFkappa B'', ``NF-kB'', and ``NFkB'', three of which are not in the current UMLS and two not in the Entrez Gene.\n[3][21] have shown that expanding gene symbols with their lexical variants improved the retrieval effectiveness of their biomedical IR systems.\nIn our system, we employed the following two strategies to retrieve lexical variants of gene symbols.\nStrategy I: This strategy is to automatically generate lexical variants according to a set of manually crafted heuristics [3][21].\nFor example, given a gene symbol PLA2, a variant PLAII is generated according to the heuristic that Roman numerals and Arabic numerals are convertible when naming gene symbols.\nAnother variant, PLA 2, is also generated since a hyphen or a space could be inserted at the transition between alphabetic and numerical characters in a gene symbol.\nStrategy II: This strategy is to retrieve lexical variants from an abbreviation database.\nADAM [22] is an abbreviation database which covers frequently used abbreviations and their definitions (or long-forms) within MEDLINE, the authoritative repository of citations from the biomedical literature maintained by the NLM.\nGiven a query How does nucleoside diphosphate kinase (NM23) contribute to tumor progression?\n, we first identify the abbreviation NM23 and its long-form nucleoside diphosphate kinase using the abbreviation identification program from [4].\nSearching the long-form nucleoside diphosphate kinase in ADAM, other abbreviations, such as NDPK or NDK, are retrieved.\nThese abbreviations are considered as the lexical variants of NM23.\nLexical variants of MeSH concepts ADAM is used to obtain the lexical variants of MeSH concepts as well.\nAll the abbreviations of a MeSH concept in ADAM are considered as lexical variants to each other.\nIn addition, those long-forms that share the same abbreviation with the MeSH concept and are different by an edit distance of 1 or 2 are also considered as its lexical variants.\nAs an example, ``human papilloma viruses'' and ``human papillomaviruses'' have the same abbreviation HPV in ADAM and their edit distance is 1.\nThus they are considered as lexical variants to each other.\nThe edit distance between two strings is measured by the minimum number of insertions, deletions, and substitutions of a single character required to transform one string into the other [12].\n3.2.2 Implicitly related concepts Motivation: In some cases, there are few documents in the literature that directly answer a given query.\nIn this situation, those documents that implicitly answer their questions or provide supporting information would be very helpful.\nFor example, there are few documents in PubMed that directly answer the query ``What is the role of the genes HNF4 and COUP-tf I in the suppression in the function of the liver?''\n.\nHowever, there exist some documents about the role of ``HNF4'' and ``COUP-tf I'' in regulating ``hepatitis B virus'' transcription.\nIt is very likely that the biologists would be interested in these documents because ``hepatitis B virus'' is known as a virus that could cause serious damage to the function of liver.\nIn the given example, ``hepatitis B virus'' is not a synonym, hypernym, hyponym, nor a lexical variant of any of the query concepts, but it is semantically related to the query concepts according to the UMLS Semantic Network.\nWe call this type of concepts implicitly related concepts of the query.\nThis notion is similar to the B-term used in [19] for relating two disjoint literatures for biomedical hypothesis generation.\nThe difference is that we utilize the semantic relationships among query concepts to exclusively focus on concepts of certain semantic types.\nA query q in format (1) of section 2 can be represented by q = (A, C) where A is the set of biological objects and C is the set of biological processes.\nThose concepts that are semantically related to both A and C according to the UMLS Semantic Network are considered as the implicitly related concepts of the query.\nIn the above example, A = {HNF4, COUP-tf I}, C = {function of liver}, and ``hepatitis B virus'' is one of the implicitly related concepts.\nWe make use of the MEDLINE database to extract the implicitly related concepts.\nThe 2006 version of MEDLINE database contains citations (i.e., abstracts, titles, and etc.) of over 15 million biomedical articles.\nEach document in MEDLINE is manually indexed by a list of MeSH terms to describe the topics covered by that document.\nImplicitly related concepts are extracted and ranked in the following steps: Step 1.\nLet list_A be the set of MeSH terms that are 1) used for indexing those MEDLINE citations having A, and 2) semantically related to A according to the UMLS Semantic Network.\nSimilarly, list_C is created for C. Concepts in B = list_A ∩ list_C are considered as implicitly related concepts of the query.\nStep 2.\nFor each concept b∈B, compute the association between b and A using the mutual information measure [5]: P( , ) ( , ) log P( )P( ) b A I b A b A = where P(x) = n/N, n is the number of MEDLINE citations having x and N is the size of MEDLINE.\nA large value for I(b, A) means that b and A co-occur much more often than being independent.\nI(b, C) is computed similarly.\nStep 3.\nLet r(b) = (I(b, A), I(b, C)), for b∈ B. Given b1, b2 ∈ B, we say r(b1) ≤ r(b2) if I(b1, A) ≤ I(b2, A) and I(b1, C) ≤ I(b2, C).\nThen the association between b and the query q is measured by: { : and ( ) ( )} ( , ) { : and ( ) ( )} x x B r x r b score b q x x B r b r x ∈ ≤ = ∈ ≤ (2) The numerator in Formula 2 is the number of the concepts in B that are associated with both A and C equally with or less than b.\nThe denominator is the number of the concepts in B that are associated with both A and C equally with or more than b. Figure 3.2.2 shows the top 4 implicitly related concepts for the sample query.\nFigure 3.2.2 Top 4 implicitly related concepts for the query ``How do interactions between HNF4 and COUP-TF1 suppress liver function?''\n.\nIn Figure 3.2.2, the top 4 implicitly related concepts are all highly associated with liver: Hepatocytes are liver cells; Hepatoblastoma is a malignant liver neoplasm occurring in young children; the vast majority of Gluconeogenesis takes place in the liver; and Hepatitis B virus is a virus that could cause serious damage to the function of liver.\nThe top-k ranked concepts in B are used for query expansion: if I(b, A) ≥ I(b, C), then b is considered as an implicit related concept of A.\nA document having b but not A will receive a partial weight of A.\nThe expansion is similar for C when I(b, A) < I(b, C).\n3.3 Conceptual IR model We now discuss our conceptual IR model.\nWe first give the basic conceptual IR model in section 3.3.1.\nThen we explain how the domain-specific knowledge is incorporated in the model using query expansion in section 3.3.2.\nA pseudo-feedback strategy is introduced in section 3.3.3.\nIn section 3.3.4, we give a strategy to improve the ranking by avoiding incorrect match of abbreviations.\n3.3.1 Basic model Given a query q and a document d, our model measures two similarities, concept similarity and word similarity: ( , ) ( , )( , ) ( , ) concept word sim q d sim q d sim q d= Concept similarity Two vectors are derived from a query q, 1 2 1 11 12 1 2 21 22 2 ( , ) ( , ,..., ) ( , ,..., ) m n q v v v c c c v c c c = = = where v1 is a vector of concepts describing the biological object(s) and v2 is a vector of concepts describing the biological process(es).\nGiven a vector of concepts v, let s(v) be the set of concepts in v.\nThe weight of vi is then measured by: ( ) max{log : ( ) ( ) and 0}i i v v N w v s v s v n n = ⊆ > where v is a vector that contains a subset of concepts in vi and nv is the number of documents having all the concepts in v.\nThe concept similarity between q and d is then computed by 2 1 ( )( , ) i i concept i w vsim q d α = = ×∑ where αi is a parameter to indicate the completeness of vi that document d has covered.\nαi is measured by: and i i i c c d c v c c v idf idf α ∈ ∈ ∈ = ∑ ∑ (3) where idfc is the inverse document frequency of concept c.\nAn example: suppose we have a query How does Nurr-77 delete T cells before they migrate to the spleen or lymph nodes and how does this impact autoimmunity?\n.\nAfter identifying the concepts in the query, we have: 1 2 (`Nurr-77') ('T cells', `spleen', `autoimmunity', `lymph nodes') v v = = Suppose that some document frequencies of different combinations of concepts are as follows: 25 df(`Nurr-77') 0 df('T cells', `spleen', `autoimmunity', `lymph nodes') 326 df('T cells', `spleen', `autoimmunity') 82 df(`spleen', `autoimmunity', `lymph nodes') 147 df('T cells', `autoimmunity', `lymph nodes') 2332 df('T cells', `spleen', `lymph nodes') The weight of vi is then computed by (note that there does not exist a document having all the concepts in v2): 1 2 ( ) log( / 25) ( ) log( /82) w v N w v N = = .\nNow suppose a document d contains concepts `Nurr-77'', 'T cells', `spleen', and `lymph nodes', but not `autoimmunity'', then the value of parameter αi is computed as follows: 1 2 1 ('T cells')+ (`spleen')+ (`lymph nodes') ('T cells')+ (`spleen')+ (`lymph nodes')+ (`autoimmunity') idf idf idf idf idf idf idf α α = = Word similarity The similarity between q and d on the word level is computed using Okapi [17]: 10.5 ( 1) log( )( , ) 0.5word w q N n k tf sim q d n K tf∈ − + + = + + ∑ (4) where N is the size of the document collection; n is the number of documents containing w; K=k1 × ((1-b)+b × dl/avdl) and k1=1.2, C Function of Liver Implicitly related concepts (B) Hepatocytes Hepatoblastoma Gluconeogenesis Hepatitis B virus HNF4 and COUP-tf I A b=0.75 are constants.\ndl is the document length of d and avdl is the average document length; tf is the term frequency of w within d.\nThe model Given two documents d1 and d2, we say 1 2( , ) ( , )sim q d sim q d> or d1 will be ranked higher than d2, with respect to the same query q, if either 1) 1 2( , ) ( , ) concept concept sim q d sim q d> or 2) 1 2 1 2and( , ) ( , ) ( , ) ( , ) concept concept word word sim q d sim q d sim q d sim q d= > This conceptual IR model emphasizes the similarity on the concept level.\nA similar model but applied to non-biomedical domain has been given in [15].\n3.3.2 Incorporating domain-specific knowledge Given a concept c, a vector u is derived by incorporating its domain-specific knowledge: 1 2 3( , , , )u c u u u= where u1 is a vector of its synonyms, hyponyms, and lexical variants; u2 is a vector of its hypernyms; and u3 is a vector of its implicitly related concepts.\nAn occurrence of any term in u1 will be counted as an occurrence of c. idfc in Formula 3 is updated as: 1, logc c u N D idf = 1,c uD is the set of documents having c or any term in 1u .\nThe weight that a document d receives from u is given by: max{ : and }tw t u t d∈ ∈ where wt = β .\ncidf× The weighting factor β is an empirical tuning parameter determined as: 1.\nβ = 1 if t is the original concept, its synonym, its hyponym, or its lexical variant; 2.\nβ = 0.95 if t is a hypernym; 3.\nβ = 0.90× (k-i+1)/k if t is an implicitly related concept.\nk is the number of selected top ranked implicitly related concepts (see section 3.2.2); i is the position of t in the ranking of implicitly related concepts.\n3.3.3 Pseudo-feedback Pseudo-feedback is a technique commonly used to improve retrieval performance by adding new terms into the original query.\nWe used a modified pseudo-feedback strategy described in [2].\nStep 1.\nLet C be the set of concepts in the top 15 ranked documents.\nFor each concept c in C, compute the similarity between c and the query q, the computation of sim(q,c) can be found in [2].\nStep 2.\nThe top-k ranked concepts by sim(q,c) are selected.\nStep 3.\nAssociate each selected concept c' with the concept cq in q that 1) has the same semantic type as c', and 2) is most related to c' among all the concepts in q.\nThe association between c' and cq is computed by: P( ', ) ( ', ) log P( ')P( ) q q q c c I c c c c = where P(x) = n/N, n is the number of documents having x and N is the size of the document collection.\nA document having c' but not cq receives a weight given by: (0.5× (k-i+1)/k) ,qcidf× where i is the position of c' in the ranking of step 2.\n3.3.4 Avoid incorrect match of abbreviations Some gene symbols are very short and thus ambiguous.\nFor example, the gene symbol APC could be the abbreviation for many non-gene long-forms, such as air pollution control, aerobic plate count, or argon plasma coagulation.\nThis step is to avoid incorrect match of abbreviations in the top ranked documents.\nGiven an abbreviation X with the long-form L in the query, we scan the top-k ranked (k=1000) documents and when a document is found with X, we compare L with all the long-forms of X in that document.\nIf none of these long-forms is equal or close to L (i.e., the edit distance between L and the long-form of X in that document is 1 or 2), then the concept similarity of X is subtracted.\n3.4 Passage extraction The goal of passage extraction is to highlight the most relevant fragments of text in paragraphs.\nA passage is defined as any span of text that does not include the HTML paragraph tag (i.e., <P> or </P>).\nA passage could be a part of a sentence, a sentence, a set of consecutive sentences or a paragraph (i.e., the whole span of text that are inside of <P> and </P> HTML tags).\nIt is also possible to have more than one relevant passage in a single paragraph.\nOur strategy for passage extraction assumes that the optimal passage(s) in a paragraph should have all the query concepts that the whole paragraph has.\nAlso they should have higher density of query concepts than other fragments of text in the paragraph.\nSuppose we have a query q and a paragraph p represented by a sequence of sentences 1 2... .\nnp s s s= Let C be the set of concepts in q that occur in p and S = Φ.\nStep 1.\nFor each sequence of consecutive sentences 1... ,i i js s s+ 1 ≤ i ≤ j ≤ n, let S = S 1{ ... }i i js s s+∪ if 1...i i js s s+ satisfies that: 1) Every query concept in C occurs in 1...i i js s s+ and 2) There does not exist k, such that i < k < j and every query concept in C occurs in 1...i i ks s s+ or 1 2... .\nk k js s s+ + Condition 1 requires 1...i i js s s+ having all the query concepts in p and condition 2 requires 1...i i js s s+ be the minimal.\nStep 2.\nLet 1min{ 1: ... }i i jL j i s s s S+= − + ∈ .\nFor every 1...i i js s s+ in S, let 1{ ... }i i jS S s s s+= − if (j - i + 1) > L.\nThis step is to remove those sequences of sentences in S that have lower density of query concepts.\nStep 3.\nFor every two sequences of consecutive sentences 1 1 1 2 2 21 1... , and ...i i j i i js s s S s s s S+ +∈ ∈ , if 1 2 1 2 2 1 , and 1 i i j j i j ≤ ≤ ≤ + (5) then do Repeat this step until for every two sequences of consecutive sentences in S, condition (5) does not apply.\nThis step is to merge those sequences of sentences in S that are adjacent or overlapped.\nFinally the remaining sequences of sentences in S are returned as the optimal passages in the paragraph p with respect to the query.\n1 1 2 1 1 2 2 2 1 1 1 1 { ... } { ... } { ... } i i j i i j i i j S S s s s S S s s s S S s s s + + + = ∪ = − = − 4.\nEXPERIMENTAL RESULTS The evaluation of our techniques and the experimental results are given in this section.\nWe first describe the datasets and evaluation metrics used in our experiments and then present the results.\n4.1 Data sets and evaluation metrics Our experiments were performed on the platform of the Genomics track of TREC 2006.\nThe document collection contains 162,259 full-text documents from 49 Highwire biomedical journals.\nThe set of queries consists of 28 queries collected from real biologists.\nThe performance is measured on three different levels (passage, aspect, and document) to provide better insight on how the question is answered from different perspectives.\nPassage MAP: As described in [8], this is a character-based precision calculated as follows: At each relevant retrieved passage, precision will be computed as the fraction of characters overlapping with the gold standard passages divided by the total number of characters included in all nominated passages from this system for the topic up until that point.\nSimilar to regular MAP, relevant passages that were not retrieved will be added into the calculation as well, with precision set to 0 for relevant passages not retrieved.\nThen the mean of these average precisions over all topics will be calculated to compute the mean average passage precision.\nAspect MAP: A question could be addressed from different aspects.\nFor example, the question what is the role of gene PRNP in the Mad cow disease?\ncould be answered from aspects like Diagnosis, Neurologic manifestations, or Prions/Genetics.\nThis measure indicates how comprehensive the question is answered.\nDocument MAP: This is the standard IR measure.\nThe precision is measured at every point where a relevant document is obtained and then averaged over all relevant documents to obtain the average precision for a given query.\nFor a set of queries, the mean of the average precision for all queries is the MAP of that IR system.\nThe output of the system is a list of passages ranked according to their similarities with the query.\nThe performances on the three levels are then calculated based on the ranking of the passages.\n4.2 Results The Wilcoxon signed-rank test was employed to determine the statistical significance of the results.\nIn the tables of the following sections, statistically significant improvements (at the 5% level) are marked with an asterisk.\n4.2.1 Conceptual IR model vs. term-based model The initial baseline was established using word similarity only computed by the Okapi (Formula 4).\nAnother run based on our basic conceptual IR model was performed without using query expansion, pseudo-feedback, or abbreviation correction.\nThe experimental result is shown in Table 4.2.1.\nOur basic conceptual IR model significantly outperforms the Okapi on all three levels, which suggests that, although it requires additional efforts to identify concepts, retrieval on the concept level can achieve substantial improvements over purely term-based retrieval model.\n4.2.2 Contribution of different types of knowledge A series of experiments were performed to examine how each type of domain-specific knowledge contributes to the retrieval performance.\nA new baseline was established using the basic conceptual IR model without incorporating any type of domainspecific knowledge.\nThen five runs were conducted by adding each individual type of domain-specific knowledge.\nWe also conducted a run by adding all types of domain-specific knowledge.\nResults of these experiments are shown in Table 4.2.2.\nWe found that any available type of domain-specific knowledge improved the performance in passage retrieval.\nThe biggest improvement comes from the lexical variants, which is consistent with the result reported in [3].\nThis result also indicates that biologists are likely to use different variants of the same concept according to their own writing preferences and these variants might not be collected in the existing biomedical thesauruses.\nIt also suggests that the biomedical IR systems can benefit from the domain-specific knowledge extracted from the literature by text mining systems.\nSynonyms provided the second biggest improvement.\nHypernyms, hyponyms, and implicitly related concepts provided similar degrees of improvement.\nThe overall performance is an accumulative result of adding different types of domain-specific knowledge and it is better than any individual addition.\nIt is clearly shown that the performance is significantly improved (107% on passage level, 63.1% on aspect level, and 49.6% on document level) when the domain-specific knowledge is appropriately incorporated.\nAlthough it is not explicitly shown in Table 4.2.3, different types of domain-specific knowledge affect different subsets of queries.\nMore specifically, each of these types (with the exception of the lexical variants which affects a large number of queries) affects only a few queries.\nBut for those affected queries, their improvement is significant.\nAs a consequence, the accumulative improvement is very significant.\n4.2.3 Pseudo-feedback and abbreviation correction Using the Baseline+All in Table 4.2.2 as a new baseline, the contribution of abbreviation correction and pseudo-feedback is given in Table 4.2.3.\nThere is little improvement by avoiding incorrect matching of abbreviations.\nThe pseudo-feedback contributed about 4.6% improvement in passage retrieval.\n4.2.4 Performance compared with best-reported results We compared our result with the results reported in the Genomics track of TREC 2006 [8] on the conditions that 1) systems are automatic systems and 2) passages are extracted from paragraphs.\nThe performance of our system relative to the best reported results is shown in Table 4.2.4 (in TREC 2006, some systems returned the whole paragraphs as passages.\nAs a consequence, excellent retrieval results were obtained on document and aspect levels at the expense of performance on the passage level.\nWe do not include the results of such systems here).\nTable 4.2.4 Performance compared with best-reported results.\nPassage MAP Aspect MAP Document MAP Best reported results 0.1486 0.3492 0.5320 Our results 0.1823 0.3811 0.5391 Improvement 22.68% 9.14% 1.33% The best reported results in the first row of Table 4.2.4 on three levels (passage, aspect, and document) are from different systems.\nOur result is from a single run on passage retrieval in which it is better than the best reported result by 22.68% in passage retrieval and at the same time, 9.14% better in aspect retrieval, and 1.33% better in document retrieval (Since the average precision of each individual query was not reported, we can not apply the Wilcoxon signed-rank test to calculate the significance of difference between our performance and the best reported result.)\n.\nTable 4.2.1 Basic conceptual IR model vs. term-based model Run Passage Aspect Document MAP Imprvd qs # (%) MAP Imprvd qs # (%) MAP Imprvd qs # (%) Okapi 0.064 N/A 0.175 N/A 0.285 N/A Basic conceptual IR model 0.084* (+31.3%) 17 (65.4%) 0.233* (+33.1%) 12 (46.2%) 0.359* (+26.0%) 15 (57.7%) Table 4.2.2 Contribution of different types of domain-specific knowledge Run Passage Aspect Document MAP Imprvd qs # (%) MAP Imprvd qs # (%) MAP Imprvd qs # (%) Baseline = Basic conceptual IR model 0.084 N/A 0.233 N/A 0.359 N/A Baseline+Synonyms 0.105 (+25%) 11 (42.3%) 0.246 (+5.6%) 9 (34.6%) 0.420 (+17%) 13 (50%) Baseline+Hypernyms 0.088 (+4.8%) 11 (42.3%) 0.225 (-3.4%) 9 (34.6%) 0.390 (+8.6%) 16 (61.5%) Baseline+Hyponyms 0.087 (+3.6%) 10 (38.5%) 0.217 (-6.9%) 7 (26.9%) 0.389 (+8.4%) 10 (38.5%) Baseline+Variants 0.150* (+78.6%) 16 (61.5%) 0.348* (+49.4%) 13 (50%) 0.495* (+37.9%) 10 (38.5%) Baseline+Related 0.086 (+2.4%) 9 (34.6%) 0.220 (-5.6%) 9 (34.6%) 0.387 (+7.8%) 13 (50%) Baseline+All 0.174* (107%) 25 (96.2%) 0.380* (+63.1%) 19 (73.1%) 0.537* (+49.6%) 14 (53.8%) Table 4.2.3 Contribution of abbreviation correction and pseudo-feedback Run Passage Aspect Document MAP Imprvd qs # (%) MAP Imprvd qs # (%) MAP Imprvd qs # (%) Baseline+All 0.174 N/A 0.380 N/A 0.537 N/A Baseline+All+Abbr 0.175 (+0.6%) 5 (19.2%) 0.375 (-1.3%) 4 (15.4%) 0.535 (-0.4%) 4 (15.4%) Baseline+All+Abbr+PF 0.182 (+4.6%) 10 (38.5%) 0.381 (+0.3%) 6 (23.1%) 0.539 (+0.4%) 9 (34.6%) A separate experiment has been done using a second testbed, the ad-hoc Task of TREC Genomics 2005, to evaluate our knowledge-intensive conceptual IR model for document retrieval of biomedical literature.\nThe overall performance in terms of MAP is 35.50%, which is about 22.92% above the best reported result [9].\nNotice that the performance was only measured on the document level for the ad-hoc Task of TREC Genomics 2005.\n5.\nRELATED WORKS Many studies used manually-crafted thesauruses or knowledge databases created by text mining systems to improve retrieval effectiveness based on either word-statistical retrieval systems or conceptual retrieval systems.\n[11][1] assessed query expansion using the UMLS Metathesaurus.\nBased on a word-statistical retrieval system, [11] used definitions and different types of thesaurus relationships for query expansion and a deteriorated performance was reported.\n[1] expanded queries with phrases and UMLS concepts determined by the MetaMap, a program which maps biomedical text to UMLS concepts, and no significant improvement was shown.\nWe used MeSH, Entrez gene, and other non-thesaurus knowledge resources such as an abbreviation database for query expansion.\nA critical difference between our work and those in [11][1] is that our retrieval model is based on concepts, not on individual words.\nThe Genomics track in TREC provides a common platform to evaluate methods and techniques proposed by various groups for biomedical information retrieval.\nAs summarized in [8][9][10], many groups utilized domain-specific knowledge to improve retrieval effectiveness.\nAmong these groups, [3] assessed both thesaurus-based knowledge, such as gene information, and non thesaurus-based knowledge, such as lexical variants of gene symbols, for query expansion.\nThey have shown that query expansion with acronyms and lexical variants of gene symbols produced the biggest improvement, whereas, the query expansion with gene information from gene databases deteriorated the performance.\n[21] used a similar approach for generating lexical variants of gene symbols and reported significant improvements.\nOur system utilized more types of domain-specific knowledge, including hyponyms, hypernyms and implicitly related concepts.\nIn addition, under the conceptual retrieval framework, we examined more comprehensively the effects of different types of domain-specific knowledge in performance contribution.\n[20][15] utilized WordNet, a database of English words and their lexical relationships developed by Princeton University, for query expansion in the non-biomedical domain.\nIn their studies, queries were expanded using the lexical semantic relations such as synonyms, hypernyms, or hyponyms.\nLittle benefit has been shown in [20].\nThis has been due to ambiguity of the query terms which have different meanings in different contexts.\nWhen these synonyms having multiple meanings are added to the query, substantial irrelevant documents are retrieved.\nIn the biomedical domain, this kind of ambiguity of query terms is relatively less frequent, because, although the abbreviations are highly ambiguous, general biomedical concepts usually have only one meaning in the thesaurus, such as UMLS, whereas a term in WordNet usually have multiple meanings (represented as synsets in WordNet).\nBesides, we have implemented a post-ranking step to reduce the number of incorrect matches of abbreviations, which will hopefully decrease the negative impact caused by the abbreviation ambiguity.\nBesides, we have implemented a postranking step to reduce the number of incorrect matches of abbreviations, which will hopefully decrease the negative impact caused by the abbreviation ambiguity.\nThe retrieval model in [15] emphasized the similarity between a query and a document on the phrase level assuming that phrases are more important than individual words when retrieving documents.\nAlthough the assumption is similar, our conceptual model is based on the biomedical concepts, not phrases.\n[13] presented a good study of the role of knowledge in the document retrieval of clinical medicine.\nThey have shown that appropriate use of semantic knowledge in a conceptual retrieval framework can yield substantial improvements.\nAlthough the retrieval model is similar, we made a study in the domain of genomics, in which the problem structure and task knowledge is not as well-defined as in the domain of clinical medicine [18].\nAlso, our similarity function is very different from that in [13].\nIn summary, our approach differs from previous works in four important ways: First, we present a case study of conceptual retrieval in the domain of genomics, where many knowledge resources can be used to improve the performance of biomedical IR systems.\nSecond, we have studied more types of domainspecific knowledge than previous researchers and carried out more comprehensive experiments to look into the effects of different types of domain-specific knowledge in performance contribution.\nThird, although some of the techniques seem similar to previously published ones, they are actually quite different in details.\nFor example, in our pseudo-feedback process, we require that the unit of feedback is a concept and the concept has to be of the same semantic type as a query concept.\nThis is to ensure that our conceptual model of retrieval can be applied.\nAs another example, the way in which implicitly related concepts are extracted in this paper is significantly different from that given in [19].\nFinally, our conceptual IR model is actually based on complex concepts because some biomedical meanings, such as biological processes, are represented by multiple simple concepts.\n6.\nCONCLUSION This paper proposed a conceptual approach to utilize domainspecific knowledge in an IR system to improve its effectiveness in retrieving biomedical literature.\nWe specified five different types of domain-specific knowledge (i.e., synonyms, hyponyms, hypernyms, lexical variants, and implicitly related concepts) and examined their effects in performance contribution.\nWe also evaluated other two techniques, pseudo-feedback and abbreviation correction.\nExperimental results have shown that appropriate use of domain-specific knowledge in a conceptual IR model yields significant improvements (23%) in passage retrieval over the best known results.\nIn our future work, we will explore the use of other existing knowledge resources, such as UMLS and the Wikipedia, and evaluate techniques such as disambiguation of gene symbols for improving retrieval effectiveness.\nThe application of our conceptual IR model in other domains such as clinical medicine will be investigated.\n7.\nACKNOWLEDGMENTS insightful discussion.\n8.\nREFERENCES [1] Aronson A.R., Rindflesch T.C. Query expansion using the UMLS Metathesaurus.\nProc AMIA Annu Fall Symp.\n1997.\n485-9.\n[2] Baeza-Yates R., Ribeiro-Neto B. Modern Information Retrieval.\nAddison-Wesley, 1999, 129-131.\n[3] Buttcher S., Clarke C.L.A., Cormack G.V. Domain-specific synonym expansion and validation for biomedical information retrieval (MultiText experiments for TREC 2004).\nTREC``04.\n[4] Chang J.T., Schutze H., Altman R.B. Creating an online dictionary of abbreviations from MEDLINE.\nJournal of the American Medical Informatics Association.\n2002 9(6).\n[5] Church K.W., Hanks P. Word association norms, mutual information and lexicography.\nComputational Linguistics.\n1990;16:22, C29.\n[6] Fontelo P., Liu F., Ackerman M. askMEDLINE: a free-text, natural language query tool for MEDLINE/PubMed.\nBMC Med Inform Decis Mak.\n2005 Mar 10;5(1):5.\n[7] Fukuda K., Tamura A., Tsunoda T., Takagi T. Toward information extraction: identifying protein names from biological papers.\nPac Symp Biocomput.\n1998;:707-18.\n[8] Hersh W.R., and etc..\nTREC 2006 Genomics Track Overview.\nTREC``06.\n[9] Hersh W.R., and etc..\nTREC 2005 Genomics Track Overview.\nIn TREC``05.\n[10] Hersh W.R., and etc..\nTREC 2004 Genomics Track Overview.\nIn TREC``04.\n[11] Hersh W.R., Price S., Donohoe L. Assessing thesaurus-based query expansion using the UMLS Metathesaurus.\nProc AMIA Symp.\n344-8.\n2000.\n[12] Levenshtein, V. Binary codes capable of correcting deletions, insertions, and reversals.\nSoviet Physics - Doklady 10, 10 (1996), 707-710.\n[13] Lin J., Demner-Fushman D.\nThe Role of Knowledge in Conceptual Retrieval: A Study in the Domain of Clinical Medicine.\nSIGIR``06.\n99-06.\n[14] Lindberg D., Humphreys B., and McCray A.\nThe Unified Medical Language System.\nMethods of Information in Medicine.\n32(4):281-291, 1993.\n[15] Liu S., Liu F., Yu C., and Meng W.Y..\nAn Effective Approach to Document Retrieval via Utilizing WordNet and Recognizing Phrases.\nSIGIR``04.\n266-272 [16] Proux D., Rechenmann F., Julliard L., Pillet V.V., Jacq B. Detecting Gene Symbols and Names in Biological Texts: A First Step toward Pertinent Information Extraction.\nGenome Inform Ser Workshop Genome Inform.\n1998;9:72-80.\n[17] Robertson S.E., Walker S. Okapi/Keenbow at TREC-8.\nNIST Special Publication 500-246: TREC 8.\n[18] Sackett D.L., and etc..\nEvidence-Based Medicine: How to Practice and Teach EBM.\nChurchill Livingstone.\nSecond edition, 2000.\n[19] Swanson,D.R., Smalheiser,N.R..\nAn interactive system for finding complemen-tary literatures: a stimulus to scientific discovery.\nArtificial Intelligence, 1997; 91,183-203.\n[20] Voorhees E. Query expansion using lexical-semantic relations.\nSIGIR 1994.\n61-9 [21] Zhong M., Huang X.J. Concept-based biomedical text retrieval.\nSIGIR``06.\n723-4 [22] Zhou W., Torvik V.I., Smalheiser N.R. ADAM: Another Database of Abbreviations in MEDLINE.\nBioinformatics.\n2006; 22(22): 2813-2818."},"lvl-2":{"kind":"string","value":"Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature\nABSTRACT\nThis paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effectiveness in retrieving biomedical literature.\nThe effects of different types of domain-specific knowledge in performance contribution are examined.\nBased on the TREC platform, we show that appropriate use of domainspecific knowledge in a proposed conceptual retrieval model yields about 23% improvement over the best reported result in passage retrieval in the Genomics Track of TREC 2006.\n1.\nINTRODUCTION\nBiologists search for literature on a daily basis.\nFor most biologists, PubMed, an online service of U.S. National Library of Medicine (NLM), is the most commonly used tool for searching the biomedical literature.\nPubMed allows for keyword search by using Boolean operators.\nFor example, if one desires documents on the use of the drug propanolol in the disease hypertension, a typical PubMed query might be \"propanolol AND hypertension\", which will return all the documents having the two keywords.\nKeyword search in PubMed is effective if the query is well-crafted\nby the users using their expertise.\nHowever, information needs of biologists, in some cases, are expressed as complex questions [8] [9], which PubMed is not designed to handle.\nWhile NLM does maintain an experimental tool for free-text queries [6], it is still based on PubMed keyword search.\nThe Genomics track of the 2006 Text REtrieval Conference (TREC) provides a common platform to assess the methods and techniques proposed by various groups for biomedical information retrieval.\nThe queries were collected from real biologists and they are expressed as complex questions, such as \"How do mutations in the Huntingtin gene affect Huntington's disease?\"\n.\nThe document collection contains 162,259 Highwire full-text documents in HTML format.\nSystems from participating groups are expected to find relevant passages within the full-text documents.\nA passage is defined as any span of text that does not include the HTML paragraph tag (i.e., <P> or </P>).\nWe approached the problem by utilizing domain-specific knowledge in a conceptual retrieval model.\nDomain-specific knowledge, in this paper, refers to information about concepts and relationships between concepts in a certain domain.\nWe assume that appropriate use of domain-specific knowledge might improve the effectiveness of retrieval.\nFor example, given a query \"What is the role of gene PRNP in the Mad Cow Disease?\"\n, expanding the gene symbol \"PRNP\" with its synonyms \"Prp\", \"PrPSc\", and \"prion protein\", more relevant documents might be retrieved.\nPubMed and many other biomedical systems [8] [9] [10] [13] also make use of domain-specific knowledge to improve retrieval effectiveness.\nIntuitively, retrieval on the level of concepts should outperform \"bag-of-words\" approaches, since the semantic relationships among words in a concept are utilized.\nIn some recent studies [13] [15], positive results have been reported for this hypothesis.\nIn this paper, concepts are entry terms of the ontology Medical Subject Headings (MeSH), a controlled vocabulary maintained by NLM for indexing biomedical literature, or gene symbols in the Entrez gene database also from NLM.\nA concept could be a word, such as the gene symbol \"PRNP\", or a phrase, such as \"Mad cow diseases\".\nIn the conceptual retrieval model presented in this paper, the similarity between a query and a document is measured on both concept and word levels.\nThis paper makes two contributions:\nretrieving biomedical literature.\nBased on this approach, our system achieved significant improvement (23%) over the best reported result in passage retrieval in the Genomics track of TREC 2006.\n2.\nWe examine the effects of utilizing concepts and of different types of domain-specific knowledge in performance contribution.\nThis paper is organized as follows: problem statement is given in the next section.\nThe techniques are introduced in section 3.\nIn section 4, we present the experimental results.\nRelated works are given in section 5 and finally, we conclude the paper in section 6.\n2.\nPROBLEM STATEMENT\nWe describe the queries, document collection and the system output in this section.\nThe query set used in the Genomics track of TREC 2006 consists of 28 questions collected from real biologists.\nAs described in [8], these questions all have the following general format:\nwhere a biological object might be a gene, protein, or gene mutation and a biological process can be a physiological process or disease.\nA question might involve multiple biological objects (m) and multiple biological processes (n).\nThese questions were derived from four templates (Table 2).\nTable 2 Query templates and examples in the Genomics track of TREC 2006\nTemplate Example What is the role of gene in What is the role of DRD4 in disease?\nalcoholism?\nWhat effect does gene have What effect does the insulin on biological process?\nreceptor gene have on tumorigenesis?\nHow do genes interact in How do HMG and HMGB1 organ function?\ninteract in hepatitis?\nHow does a mutation in How does a mutation in Ret gene influence biological influence thyroid function?\nprocess?\nFeatures of the queries: 1) They are different from the typical Web queries and the PubMed queries, both of which usually consist of 1 to 3 keywords; 2) They are generated from structural templates which can be used by a system to identify the query components, the biological object or process.\nThe document collection contains 162,259 Highwire full-text documents in HTML format.\nThe output of the system is a list of passages ranked according to their similarities with the query.\nA passage is defined as any span of text that does not include the HTML paragraph tag (i.e., <P> or </P>).\nA passage could be a part of a sentence, a sentence, a set of consecutive sentences or a paragraph (i.e., the whole span of text that are inside of <P> and </P> HTML tags).\nThis is a passage-level information retrieval problem with the attempt to put biologists in contexts where relevant information is provided.\n3.\nTECHNIQUES AND METHODS\nWe approached the problem by first retrieving the top-k most relevant paragraphs, then extracting passages from these paragraphs, and finally ranking the passages.\nIn this process, we employed several techniques and methods, which will be introduced in this section.\nFirst, we give two definitions: Definition 3.1 A concept is 1) a entry term in the MeSH ontology, or 2) a gene symbol in the Entrez gene database.\nThis definition of concept can be generalized to include other biomedical dictionary terms.\nDefinition 3.2 A semantic type is a category defined in the Semantic Network of the Unified Medical Language System (UMLS) [14].\nThe current release of the UMLS Semantic Network contains 135 semantic types such as \"Disease or Syndrome\".\nEach entry term in the MeSH ontology is assigned one or more semantic types.\nEach gene symbol in the Entrez gene database maps to the semantic type \"Gene or Genome\".\nIn addition, these semantic types are linked by 54 relationships.\nFor example, \"Antibiotic\" prevents \"Disease or Syndrome\".\nThese relationships among semantic types represent general biomedical knowledge.\nWe utilized these semantic types and their relationships to identify related concepts.\nThe rest of this section is organized as follows: in section 3.1, we explain how the concepts are identified within a query.\nIn section 3.2, we specify five different types of domain-specific knowledge and introduce how they are compiled.\nIn section 3.3, we present our conceptual IR model.\nFinally, our strategy for passage extraction is described in section 3.4.\n3.1 Identifying concepts within a query\nA concept, defined in Definition 3.1, is a gene symbol or a MeSH term.\nWe make use of the query templates to identify gene symbols.\nFor example, the query \"How do HMG and HMGB1 interact in hepatitis?\"\nis derived from the template \"How do genes interact in organ function?\"\n.\nIn this case, \"HMG\" and \"HMGB1\" will be identified as gene symbols.\nIn cases where the query templates are not provided, programs for recognition of gene symbols within texts are needed.\nWe use the query translation functionality of PubMed to extract MeSH terms in a query.\nThis is done by submitting the whole query to PubMed, which will then return a file in which the MeSH terms in the query are labeled.\nIn Table 3.1, three MeSH terms within the query \"What is the role of gene PRNP in the Mad cow disease?\"\nare found in the PubMed translation: \"encephalopathy, bovine spongiform\" for \"Mad cow disease\", \"genes\" for \"gene\", and \"role\" for \"role\".\nTable 3.1 The PubMed translation of the query \"What is the role of gene PRNP in the Mad cow disease?\"\n.\n3.2 Compiling domain-specific knowledge\nIn this paper, domain-specific knowledge refers to information about concepts and their relationships in a certain domain.\nWe used five types of domain-specific knowledge in the domain of genomics: Type 1.\nSynonyms (terms listed in the thesauruses that refer to the same meaning) Type 2.\nHypernyms (more generic terms, one level only) Type 3.\nHyponyms (more specific terms, one level only) Type 4.\nLexical variants (different forms of the same concept, such as abbreviations.\nThey are commonly used in the literature, but might not be listed in the thesauruses) Type 5.\nImplicitly related concepts (terms that are semantically related and also co-occur more frequently than being independent in the biomedical texts) Knowledge of type 1-3 is retrieved from the following two thesauruses: 1) MeSH, a controlled vocabulary maintained by NLM for indexing biomedical literature.\nThe 2007 version of MeSH contains information about 190,000 concepts.\nThese concepts are organized in a tree hierarchy; 2) Entrez Gene, one of the most widely used searchable databases of genes.\nThe current version of Entrez Gene contains information about 1.7 million genes.\nIt does not have a hierarchy.\nOnly synonyms are retrieved from Entrez Gene.\nThe compiling of type 4-5 knowledge is introduced in section 3.2.1 and 3.2.2, respectively.\n3.2.1 Lexical variants\nLexical variants of gene symbols\nNew gene symbols and their lexical variants are regularly introduced into the biomedical literature [7].\nHowever, many reference databases, such as UMLS and Entrez Gene, may not be able to keep track of all this kind of variants.\nFor example, for the gene symbol \"NF-kappa B\", at least 5 different lexical variants can be found in the biomedical literature: \"NF-kappaB\", \"NFkappaB\", \"NFkappa B\", \"NF-kB\", and \"NFkB\", three of which are not in the current UMLS and two not in the Entrez Gene.\n[3] [21] have shown that expanding gene symbols with their lexical variants improved the retrieval effectiveness of their biomedical IR systems.\nIn our system, we employed the following two strategies to retrieve lexical variants of gene symbols.\nStrategy I: This strategy is to automatically generate lexical variants according to a set of manually crafted heuristics [3] [21].\nFor example, given a gene symbol \"PLA2\", a variant \"PLAII\" is generated according to the heuristic that Roman numerals and Arabic numerals are convertible when naming gene symbols.\nAnother variant, \"PLA 2\", is also generated since a hyphen or a space could be inserted at the transition between alphabetic and numerical characters in a gene symbol.\nStrategy II: This strategy is to retrieve lexical variants from an abbreviation database.\nADAM [22] is an abbreviation database which covers frequently used abbreviations and their definitions (or long-forms) within MEDLINE, the authoritative repository of citations from the biomedical literature maintained by the NLM.\nGiven a query \"How does nucleoside diphosphate kinase (NM23) contribute to tumor progression?\"\n, we first identify the abbreviation \"NM23\" and its long-form \"nucleoside diphosphate kinase\" using the abbreviation identification program from [4].\nSearching the long-form \"nucleoside diphosphate kinase\" in ADAM, other abbreviations, such as \"NDPK\" or \"NDK\", are retrieved.\nThese abbreviations are considered as the lexical variants of \"NM23\".\nLexical variants of MeSH concepts\nADAM is used to obtain the lexical variants of MeSH concepts as well.\nAll the abbreviations of a MeSH concept in ADAM are considered as lexical variants to each other.\nIn addition, those long-forms that share the same abbreviation with the MeSH concept and are different by an edit distance of 1 or 2 are also considered as its lexical variants.\nAs an example, \"human papilloma viruses\" and \"human papillomaviruses\" have the same abbreviation \"HPV\" in ADAM and their edit distance is 1.\nThus they are considered as lexical variants to each other.\nThe edit distance between two strings is measured by the minimum number of insertions, deletions, and substitutions of a single character required to transform one string into the other [12].\n3.2.2 Implicitly related concepts\nMotivation: In some cases, there are few documents in the literature that directly answer a given query.\nIn this situation, those documents that implicitly answer their questions or provide supporting information would be very helpful.\nFor example, there are few documents in PubMed that directly answer the query \"What is the role of the genes HNF4 and COUP-tf I in the suppression in the function of the liver?\"\n.\nHowever, there exist some documents about the role of \"HNF4\" and \"COUP-tf I\" in regulating \"hepatitis B virus\" transcription.\nIt is very likely that the biologists would be interested in these documents because \"hepatitis B virus\" is known as a virus that could cause serious damage to the function of liver.\nIn the given example, \"hepatitis B virus\" is not a synonym, hypernym, hyponym, nor a lexical variant of any of the query concepts, but it is semantically related to the query concepts according to the UMLS Semantic Network.\nWe call this type of concepts \"implicitly related concepts\" of the query.\nThis notion is similar to the \"B-term\" used in [19] for relating two disjoint literatures for biomedical hypothesis generation.\nThe difference is that we utilize the semantic relationships among query concepts to exclusively focus on concepts of certain semantic types.\nA query q in format (1) of section 2 can be represented by q = (A, C) where A is the set of biological objects and C is the set of biological processes.\nThose concepts that are semantically related to both A and C according to the UMLS Semantic Network are considered as the implicitly related concepts of the query.\nIn the above example, A = {\"HNF4\", \"COUP-tf I\"}, C = {\"function of liver\"}, and \"hepatitis B virus\" is one of the implicitly related concepts.\nWe make use of the MEDLINE database to extract the implicitly related concepts.\nThe 2006 version of MEDLINE database contains citations (i.e., abstracts, titles, and etc.) of over 15 million biomedical articles.\nEach document in MEDLINE is manually indexed by a list of MeSH terms to describe the topics covered by that document.\nImplicitly related concepts are extracted and ranked in the following steps: Step 1.\nLet list_A be the set of MeSH terms that are 1) used for indexing those MEDLINE citations having A, and 2) semantically related to A according to the UMLS Semantic Network.\nSimilarly, list_C is created for C. Concepts in B = list_A ∩ list_C are considered as implicitly related concepts of the query.\nStep 2.\nFor each concept b ∈ B, compute the association between b and A using the mutual information measure [5]: where P (x) = n/N, n is the number of MEDLINE citations having x and N is the size of MEDLINE.\nA large value for I (b, A) means that b and A co-occur much more often than being independent.\nI (b, C) is computed similarly.\nStep 3.\nLet r (b) = (I (b, A), I (b, C)), for b ∈ B. Given b1, b2 ∈ B, we say r (b1) <r (b2) if I (b1, A) <I (b2, A) and I (b1, C) <I (b2, C).\nThen the association between b and the query q is measured by:\nThe numerator in Formula 2 is the number of the concepts in B that are associated with both A and C equally with or less than b.\nThe denominator is the number of the concepts in B that are associated with both A and C equally with or more than b. Figure 3.2.2 shows the top 4 implicitly related concepts for the sample query.\nImplicitly related concepts (B) where v1 is a vector of concepts describing the biological object (s) and v2 is a vector of concepts describing the biological process (es).\nGiven a vector of concepts v, let s (v) be the set of concepts in v.\nThe weight of vi is then measured by:\nwhere v is a vector that contains a subset of concepts in vi and nv is the number of documents having all the concepts in v.\nThe concept similarity between q and d is then computed by\nwhere αi is a parameter to indicate the completeness of vi that document d has covered.\nαi is measured by:\nc ∈ d and c ∈ vi where idfc is the inverse document frequency of concept c.\nAn example: suppose we have a query \"How does Nurr-77 delete T cells before they migrate to the spleen or lymph nodes and how does this impact autoimmunity?\"\n.\nAfter identifying the concepts in the query, we have:\nFigure 3.2.2 Top 4 implicitly related concepts for the query \"How do interactions between HNF4 and COUP-TF1 suppress liver function?\"\n.\nIn Figure 3.2.2, the top 4 implicitly related concepts are all highly associated with \"liver\": \"Hepatocytes\" are liver cells; \"Hepatoblastoma\" is a malignant liver neoplasm occurring in young children; the vast majority of \"Gluconeogenesis\" takes place in the liver; and \"Hepatitis B virus\" is a virus that could cause serious damage to the function of liver.\nThe top-k ranked concepts in B are used for query expansion: if I (b, A) ≥ I (b, C), then b is considered as an implicit related concept of A.\nA document having b but not A will receive a partial weight of A.\nThe expansion is similar for C when I (b, A) <I (b, C).\n3.3 Conceptual IR model\nWe now discuss our conceptual IR model.\nWe first give the basic conceptual IR model in section 3.3.1.\nThen we explain how the domain-specific knowledge is incorporated in the model using query expansion in section 3.3.2.\nA pseudo-feedback strategy is introduced in section 3.3.3.\nIn section 3.3.4, we give a strategy to improve the ranking by avoiding incorrect match of abbreviations.\n3.3.1 Basic model\nGiven a query q and a document d, our model measures two similarities, concept similarity and word similarity:\nconcept word\nConcept similarity\nTwo vectors are derived from a query q,\nSuppose that some document frequencies of different combinations of concepts are as follows:\nThe weight of vi is then computed by (note that there does not exist a document having all the concepts in v2):\nNow suppose a document d contains concepts ` Nurr-77',' T cells','s pleen', and' lymph nodes', but not ` autoimmunity', then the value of parameter αi is computed as follows: α1 idf (' T cells') + idf ('s pleen') + idf (' lymph nodes') idf (' T cells') + idf ('s pleen') + idf (' lymph nodes') + idf (' autoimmunity')\nWord similarity\nThe similarity between q and d on the word level is computed using Okapi [17]:\nwhere N is the size of the document collection; n is the number of documents containing w; K = k1 × ((1-b) + b × dl/avdl) and k1 = 1.2,\nb = 0.75 are constants.\ndl is the document length of d and avdl is the average document length; tf is the term frequency of w within d.\nThe model\nGiven two documents d1 and d2, we say sim (q, d1)> sim (q, d2) or d1 will be ranked higher than d2, with respect to the same query q, if either\nThis conceptual IR model emphasizes the similarity on the concept level.\nA similar model but applied to non-biomedical domain has been given in [15].\n3.3.2 Incorporating domain-specific knowledge\nGiven a concept c, a vector u is derived by incorporating its domain-specific knowledge:\nwhere u1 is a vector of its synonyms, hyponyms, and lexical variants; u2 is a vector of its hypernyms; and u3 is a vector of its implicitly related concepts.\nAn occurrence of any term in u1 will be counted as an occurrence of c. idfc in Formula 3 is updated as:\nwhere wt = 8 × idfc.\nThe weighting factor 8 is an empirical tuning parameter determined as:\n1.\n8 = 1 if t is the original concept, its synonym, its hyponym, or its lexical variant; 2.\n8 = 0.95 if t is a hypernym; 3.\n8 = 0.90 × (k--i +1) / k if t is an implicitly related concept.\nk is the number of selected top ranked implicitly related concepts (see section 3.2.2); i is the position of t in the ranking of implicitly related concepts.\n3.3.3 Pseudo-feedback\nPseudo-feedback is a technique commonly used to improve retrieval performance by adding new terms into the original query.\nWe used a modified pseudo-feedback strategy described in [2].\nStep 1.\nLet C be the set of concepts in the top 15 ranked documents.\nFor each concept c in C, compute the similarity between c and the query q, the computation of sim (q, c) can be found in [2].\nStep 2.\nThe top-k ranked concepts by sim (q, c) are selected.\nStep 3.\nAssociate each selected concept c' with the concept cq in q that 1) has the same semantic type as c', and 2) is most related to c' among all the concepts in q.\nThe association between c' and cq is computed by:\nwhere P (x) = n/N, n is the number of documents having x and N is the size of the document collection.\nA document having c' but not cq receives a weight given by: (0.5 × (k-i +1) / k) × idfcq, where i is the position of c' in the ranking of step 2.\n3.3.4 Avoid incorrect match of abbreviations\nSome gene symbols are very short and thus ambiguous.\nFor example, the gene symbol \"APC\" could be the abbreviation for many non-gene long-forms, such as \"air pollution control\", \"aerobic plate count\", or \"argon plasma coagulation\".\nThis step is to avoid incorrect match of abbreviations in the top ranked documents.\nGiven an abbreviation X with the long-form L in the query, we scan the top-k ranked (k = 1000) documents and when a document is found with X, we compare L with all the long-forms of X in that document.\nIf none of these long-forms is equal or close to L (i.e., the edit distance between L and the long-form of X in that document is 1 or 2), then the concept similarity of X is subtracted.\n3.4 Passage extraction\nThe goal of passage extraction is to highlight the most relevant fragments of text in paragraphs.\nA passage is defined as any span of text that does not include the HTML paragraph tag (i.e., <P> or </P>).\nA passage could be a part of a sentence, a sentence, a set of consecutive sentences or a paragraph (i.e., the whole span of text that are inside of <P> and </P> HTML tags).\nIt is also possible to have more than one relevant passage in a single paragraph.\nOur strategy for passage extraction assumes that the optimal passage (s) in a paragraph should have all the query concepts that the whole paragraph has.\nAlso they should have higher density of query concepts than other fragments of text in the paragraph.\nSuppose we have a query q and a paragraph p represented by a sequence of sentences p = s1s2...sn.\nLet C be the set of concepts in q that occur in p and S = 0.\nStep 1.\nFor each sequence of consecutive sentences sisi +1...s j, 1 <i <j <n, let S = S ∪ {sisi +1...s j} if sisi +1...s j satisfies that:\n1) Every query concept in C occurs in sisi +1...s j and 2) There does not exist k, such that i <k <j and every query concept in C occurs in sisi +1...sk orsk +1 sk +2...s j.\nCondition 1 requires sisi +1...sj having all the query concepts in p and condition 2 requires sisi +1...sj be the minimal.\nStep 2.\nLet L = min {j − i + 1: sisi +1...s j ∈ S}.\nFor every sisi +1...sj in S, let S = S − {sisi +1...s j} if (j--i + 1)> L.\nThis step is to remove those sequences of sentences in S that have lower density of query concepts.\nStep 3.\nFor every two sequences of consecutive\nRepeat this step until for every two sequences of consecutive sentences in S, condition (5) does not apply.\nThis step is to merge those sequences of sentences in S that are adjacent or overlapped.\nFinally the remaining sequences of sentences in S are returned as the optimal passages in the paragraph p with respect to the query.\n4.\nEXPERIMENTAL RESULTS\nThe evaluation of our techniques and the experimental results are given in this section.\nWe first describe the datasets and evaluation metrics used in our experiments and then present the results.\n4.1 Data sets and evaluation metrics\nOur experiments were performed on the platform of the Genomics track of TREC 2006.\nThe document collection contains 162,259 full-text documents from 49 Highwire biomedical journals.\nThe set of queries consists of 28 queries collected from real biologists.\nThe performance is measured on three different levels (passage, aspect, and document) to provide better insight on how the question is answered from different perspectives.\nPassage MAP: As described in [8], this is a character-based precision calculated as follows: \"At each relevant retrieved passage, precision will be computed as the fraction of characters overlapping with the gold standard passages divided by the total number of characters included in all nominated passages from this system for the topic up until that point.\nSimilar to regular MAP, relevant passages that were not retrieved will be added into the calculation as well, with precision set to 0 for relevant passages not retrieved.\nThen the mean of these average precisions over all topics will be calculated to compute the mean average passage precision\".\nAspect MAP: A question could be addressed from different aspects.\nFor example, the question \"what is the role of gene PRNP in the Mad cow disease?\"\ncould be answered from aspects like \"Diagnosis\", \"Neurologic manifestations\", or \"Prions/Genetics\".\nThis measure indicates how comprehensive the question is answered.\nDocument MAP: This is the standard IR measure.\nThe precision is measured at every point where a relevant document is obtained and then averaged over all relevant documents to obtain the average precision for a given query.\nFor a set of queries, the mean of the average precision for all queries is the MAP of that IR system.\nThe output of the system is a list of passages ranked according to their similarities with the query.\nThe performances on the three levels are then calculated based on the ranking of the passages.\n4.2 Results\nThe Wilcoxon signed-rank test was employed to determine the statistical significance of the results.\nIn the tables of the following sections, statistically significant improvements (at the 5% level) are marked with an asterisk.\n4.2.1 Conceptual IR model vs. term-based model\nThe initial baseline was established using word similarity only computed by the Okapi (Formula 4).\nAnother run based on our basic conceptual IR model was performed without using query expansion, pseudo-feedback, or abbreviation correction.\nThe experimental result is shown in Table 4.2.1.\nOur basic conceptual IR model significantly outperforms the Okapi on all three levels, which suggests that, although it requires additional efforts to identify concepts, retrieval on the concept level can achieve substantial improvements over purely term-based retrieval model.\n4.2.2 Contribution of different types of knowledge\nA series of experiments were performed to examine how each type of domain-specific knowledge contributes to the retrieval performance.\nA new baseline was established using the basic conceptual IR model without incorporating any type of domainspecific knowledge.\nThen five runs were conducted by adding each individual type of domain-specific knowledge.\nWe also conducted a run by adding all types of domain-specific knowledge.\nResults of these experiments are shown in Table 4.2.2.\nWe found that any available type of domain-specific knowledge improved the performance in passage retrieval.\nThe biggest improvement comes from the lexical variants, which is consistent with the result reported in [3].\nThis result also indicates that biologists are likely to use different variants of the same concept according to their own writing preferences and these variants might not be collected in the existing biomedical thesauruses.\nIt also suggests that the biomedical IR systems can benefit from the domain-specific knowledge extracted from the literature by text mining systems.\nSynonyms provided the second biggest improvement.\nHypernyms, hyponyms, and implicitly related concepts provided similar degrees of improvement.\nThe overall performance is an accumulative result of adding different types of domain-specific knowledge and it is better than any individual addition.\nIt is clearly shown that the performance is significantly improved (107% on passage level, 63.1% on aspect level, and 49.6% on document level) when the domain-specific knowledge is appropriately incorporated.\nAlthough it is not explicitly shown in Table 4.2.3, different types of domain-specific knowledge affect different subsets of queries.\nMore specifically, each of these types (with the exception of \"the lexical variants\" which affects a large number of queries) affects only a few queries.\nBut for those affected queries, their improvement is significant.\nAs a consequence, the accumulative improvement is very significant.\n4.2.3 Pseudo-feedback and abbreviation correction\nUsing the \"Baseline + All\" in Table 4.2.2 as a new baseline, the contribution of abbreviation correction and pseudo-feedback is given in Table 4.2.3.\nThere is little improvement by avoiding incorrect matching of abbreviations.\nThe pseudo-feedback contributed about 4.6% improvement in passage retrieval.\n4.2.4 Performance compared with best-reported results\nWe compared our result with the results reported in the Genomics track of TREC 2006 [8] on the conditions that 1) systems are automatic systems and 2) passages are extracted from paragraphs.\nThe performance of our system relative to the best reported results is shown in Table 4.2.4 (in TREC 2006, some systems returned the whole paragraphs as passages.\nAs a consequence, excellent retrieval results were obtained on document and aspect levels at the expense of performance on the passage level.\nWe do not include the results of such systems here).\nTable 4.2.4 Performance compared with best-reported results.\nThe best reported results in the first row of Table 4.2.4 on three levels (passage, aspect, and document) are from different systems.\nOur result is from a single run on passage retrieval in which it is better than the best reported result by 22.68% in passage retrieval and at the same time, 9.14% better in aspect retrieval, and 1.33% better in document retrieval (Since the average precision of each individual query was not reported, we cannot apply the Wilcoxon signed-rank test to calculate the significance of difference between our performance and the best reported result.)\n.\nTable 4.2.1 Basic conceptual IR model vs. term-based model\nTable 4.2.2 Contribution of different types of domain-specific knowledge\nTable 4.2.3 Contribution of abbreviation correction and pseudo-feedback\nA separate experiment has been done using a second testbed, the Ad Hoc Task of TREC Genomics 2005, to evaluate our knowledge-intensive conceptual IR model for document retrieval of biomedical literature.\nThe overall performance in terms of MAP is 35.50%, which is about 22.92% above the best reported result [9].\nNotice that the performance was only measured on the document level for the Ad Hoc Task of TREC Genomics 2005.\n5.\nRELATED WORKS\nMany studies used manually-crafted thesauruses or knowledge databases created by text mining systems to improve retrieval effectiveness based on either word-statistical retrieval systems or conceptual retrieval systems.\n[11] [1] assessed query expansion using the UMLS Metathesaurus.\nBased on a word-statistical retrieval system, [11] used definitions and different types of thesaurus relationships for query expansion and a deteriorated performance was reported.\n[1] expanded queries with phrases and UMLS concepts determined by the MetaMap, a program which maps biomedical text to UMLS concepts, and no significant improvement was shown.\nWe used MeSH, Entrez gene, and other non-thesaurus knowledge resources such as an abbreviation database for query expansion.\nA critical difference between our work and those in [11] [1] is that our retrieval model is based on concepts, not on individual words.\nThe Genomics track in TREC provides a common platform to evaluate methods and techniques proposed by various groups for biomedical information retrieval.\nAs summarized in [8] [9] [10], many groups utilized domain-specific knowledge to improve retrieval effectiveness.\nAmong these groups, [3] assessed both thesaurus-based knowledge, such as gene information, and non thesaurus-based knowledge, such as lexical variants of gene symbols, for query expansion.\nThey have shown that query expansion with acronyms and lexical variants of gene symbols produced the biggest improvement, whereas, the query expansion with gene information from gene databases deteriorated the performance.\n[21] used a similar approach for generating lexical variants of gene symbols and reported significant improvements.\nOur system utilized more types of domain-specific knowledge, including hyponyms, hypernyms and implicitly related concepts.\nIn addition, under the conceptual retrieval framework, we examined more comprehensively the effects of different types of domain-specific knowledge in performance contribution.\n[20] [15] utilized WordNet, a database of English words and their lexical relationships developed by Princeton University, for query expansion in the non-biomedical domain.\nIn their studies, queries were expanded using the lexical semantic relations such as synonyms, hypernyms, or hyponyms.\nLittle benefit has been shown in [20].\nThis has been due to ambiguity of the query terms which have different meanings in different contexts.\nWhen these synonyms having multiple meanings are added to the query, substantial irrelevant documents are retrieved.\nIn the biomedical domain, this kind of ambiguity of query terms is relatively less frequent, because, although the abbreviations are highly ambiguous, general biomedical concepts usually have only one meaning in the thesaurus, such as UMLS, whereas a term in WordNet usually have multiple meanings (represented as synsets in WordNet).\nBesides, we have implemented a post-ranking step to reduce the number of incorrect matches of abbreviations, which will hopefully decrease the negative impact caused by the abbreviation ambiguity.\nBesides, we have implemented a postranking step to reduce the number of incorrect matches of abbreviations, which will hopefully decrease the negative impact caused by the abbreviation ambiguity.\nThe retrieval model in [15] emphasized the similarity between a query and a document on the phrase level assuming that phrases are more important than individual words when retrieving documents.\nAlthough the assumption is similar, our conceptual model is based on the biomedical concepts, not phrases.\n[13] presented a good study of the role of knowledge in the document retrieval of clinical medicine.\nThey have shown that appropriate use of semantic knowledge in a conceptual retrieval framework can yield substantial improvements.\nAlthough the retrieval model is similar, we made a study in the domain of genomics, in which the problem structure and task knowledge is not as well-defined as in the domain of clinical medicine [18].\nAlso, our similarity function is very different from that in [13].\nIn summary, our approach differs from previous works in four important ways: First, we present a case study of conceptual retrieval in the domain of genomics, where many knowledge resources can be used to improve the performance of biomedical IR systems.\nSecond, we have studied more types of domainspecific knowledge than previous researchers and carried out more comprehensive experiments to look into the effects of different types of domain-specific knowledge in performance contribution.\nThird, although some of the techniques seem similar to previously published ones, they are actually quite different in details.\nFor example, in our pseudo-feedback process, we require that the unit of feedback is a concept and the concept has to be of the same semantic type as a query concept.\nThis is to ensure that our conceptual model of retrieval can be applied.\nAs another example, the way in which implicitly related concepts are extracted in this paper is significantly different from that given in [19].\nFinally, our conceptual IR model is actually based on complex concepts because some biomedical meanings, such as biological processes, are represented by multiple simple concepts.\n6.\nCONCLUSION\nThis paper proposed a conceptual approach to utilize domainspecific knowledge in an IR system to improve its effectiveness in retrieving biomedical literature.\nWe specified five different types of domain-specific knowledge (i.e., synonyms, hyponyms, hypernyms, lexical variants, and implicitly related concepts) and examined their effects in performance contribution.\nWe also evaluated other two techniques, pseudo-feedback and abbreviation correction.\nExperimental results have shown that appropriate use of domain-specific knowledge in a conceptual IR model yields significant improvements (23%) in passage retrieval over the best known results.\nIn our future work, we will explore the use of other existing knowledge resources, such as UMLS and the Wikipedia, and evaluate techniques such as disambiguation of gene symbols for improving retrieval effectiveness.\nThe application of our conceptual IR model in other domains such as clinical medicine will be investigated."},"lvl-3":{"kind":"string","value":"Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature\nABSTRACT\nThis paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effectiveness in retrieving biomedical literature.\nThe effects of different types of domain-specific knowledge in performance contribution are examined.\nBased on the TREC platform, we show that appropriate use of domainspecific knowledge in a proposed conceptual retrieval model yields about 23% improvement over the best reported result in passage retrieval in the Genomics Track of TREC 2006.\n1.\nINTRODUCTION\nBiologists search for literature on a daily basis.\nFor most biologists, PubMed, an online service of U.S. National Library of Medicine (NLM), is the most commonly used tool for searching the biomedical literature.\nPubMed allows for keyword search by using Boolean operators.\nFor example, if one desires documents on the use of the drug propanolol in the disease hypertension, a typical PubMed query might be \"propanolol AND hypertension\", which will return all the documents having the two keywords.\nKeyword search in PubMed is effective if the query is well-crafted\nby the users using their expertise.\nHowever, information needs of biologists, in some cases, are expressed as complex questions [8] [9], which PubMed is not designed to handle.\nWhile NLM does maintain an experimental tool for free-text queries [6], it is still based on PubMed keyword search.\nThe Genomics track of the 2006 Text REtrieval Conference (TREC) provides a common platform to assess the methods and techniques proposed by various groups for biomedical information retrieval.\nThe queries were collected from real biologists and they are expressed as complex questions, such as \"How do mutations in the Huntingtin gene affect Huntington's disease?\"\n.\nThe document collection contains 162,259 Highwire full-text documents in HTML format.\nSystems from participating groups are expected to find relevant passages within the full-text documents.\nA passage is defined as any span of text that does not include the HTML paragraph tag (i.e., <P> or </P>).\nWe approached the problem by utilizing domain-specific knowledge in a conceptual retrieval model.\nDomain-specific knowledge, in this paper, refers to information about concepts and relationships between concepts in a certain domain.\nWe assume that appropriate use of domain-specific knowledge might improve the effectiveness of retrieval.\nFor example, given a query \"What is the role of gene PRNP in the Mad Cow Disease?\"\n, expanding the gene symbol \"PRNP\" with its synonyms \"Prp\", \"PrPSc\", and \"prion protein\", more relevant documents might be retrieved.\nPubMed and many other biomedical systems [8] [9] [10] [13] also make use of domain-specific knowledge to improve retrieval effectiveness.\nIntuitively, retrieval on the level of concepts should outperform \"bag-of-words\" approaches, since the semantic relationships among words in a concept are utilized.\nIn some recent studies [13] [15], positive results have been reported for this hypothesis.\nIn this paper, concepts are entry terms of the ontology Medical Subject Headings (MeSH), a controlled vocabulary maintained by NLM for indexing biomedical literature, or gene symbols in the Entrez gene database also from NLM.\nA concept could be a word, such as the gene symbol \"PRNP\", or a phrase, such as \"Mad cow diseases\".\nIn the conceptual retrieval model presented in this paper, the similarity between a query and a document is measured on both concept and word levels.\nThis paper makes two contributions:\nretrieving biomedical literature.\nBased on this approach, our system achieved significant improvement (23%) over the best reported result in passage retrieval in the Genomics track of TREC 2006.\n2.\nWe examine the effects of utilizing concepts and of different types of domain-specific knowledge in performance contribution.\nThis paper is organized as follows: problem statement is given in the next section.\nThe techniques are introduced in section 3.\nIn section 4, we present the experimental results.\nRelated works are given in section 5 and finally, we conclude the paper in section 6.\n2.\nPROBLEM STATEMENT\n3.\nTECHNIQUES AND METHODS\n3.1 Identifying concepts within a query\n3.2 Compiling domain-specific knowledge\n3.2.1 Lexical variants\nLexical variants of gene symbols\nLexical variants of MeSH concepts\n3.2.2 Implicitly related concepts\n3.3 Conceptual IR model\n3.3.1 Basic model\nConcept similarity\nWord similarity\nThe model\n3.3.2 Incorporating domain-specific knowledge\n3.3.3 Pseudo-feedback\n3.3.4 Avoid incorrect match of abbreviations\n3.4 Passage extraction\n4.\nEXPERIMENTAL RESULTS\n4.1 Data sets and evaluation metrics\n4.2 Results\n4.2.1 Conceptual IR model vs. term-based model\n4.2.2 Contribution of different types of knowledge\n4.2.3 Pseudo-feedback and abbreviation correction\n4.2.4 Performance compared with best-reported results\n5.\nRELATED WORKS\nMany studies used manually-crafted thesauruses or knowledge databases created by text mining systems to improve retrieval effectiveness based on either word-statistical retrieval systems or conceptual retrieval systems.\n[11] [1] assessed query expansion using the UMLS Metathesaurus.\nBased on a word-statistical retrieval system, [11] used definitions and different types of thesaurus relationships for query expansion and a deteriorated performance was reported.\n[1] expanded queries with phrases and UMLS concepts determined by the MetaMap, a program which maps biomedical text to UMLS concepts, and no significant improvement was shown.\nWe used MeSH, Entrez gene, and other non-thesaurus knowledge resources such as an abbreviation database for query expansion.\nA critical difference between our work and those in [11] [1] is that our retrieval model is based on concepts, not on individual words.\nThe Genomics track in TREC provides a common platform to evaluate methods and techniques proposed by various groups for biomedical information retrieval.\nAs summarized in [8] [9] [10], many groups utilized domain-specific knowledge to improve retrieval effectiveness.\nAmong these groups, [3] assessed both thesaurus-based knowledge, such as gene information, and non thesaurus-based knowledge, such as lexical variants of gene symbols, for query expansion.\nThey have shown that query expansion with acronyms and lexical variants of gene symbols produced the biggest improvement, whereas, the query expansion with gene information from gene databases deteriorated the performance.\n[21] used a similar approach for generating lexical variants of gene symbols and reported significant improvements.\nOur system utilized more types of domain-specific knowledge, including hyponyms, hypernyms and implicitly related concepts.\nIn addition, under the conceptual retrieval framework, we examined more comprehensively the effects of different types of domain-specific knowledge in performance contribution.\n[20] [15] utilized WordNet, a database of English words and their lexical relationships developed by Princeton University, for query expansion in the non-biomedical domain.\nIn their studies, queries were expanded using the lexical semantic relations such as synonyms, hypernyms, or hyponyms.\nLittle benefit has been shown in [20].\nThis has been due to ambiguity of the query terms which have different meanings in different contexts.\nWhen these synonyms having multiple meanings are added to the query, substantial irrelevant documents are retrieved.\nIn the biomedical domain, this kind of ambiguity of query terms is relatively less frequent, because, although the abbreviations are highly ambiguous, general biomedical concepts usually have only one meaning in the thesaurus, such as UMLS, whereas a term in WordNet usually have multiple meanings (represented as synsets in WordNet).\nBesides, we have implemented a post-ranking step to reduce the number of incorrect matches of abbreviations, which will hopefully decrease the negative impact caused by the abbreviation ambiguity.\nBesides, we have implemented a postranking step to reduce the number of incorrect matches of abbreviations, which will hopefully decrease the negative impact caused by the abbreviation ambiguity.\nThe retrieval model in [15] emphasized the similarity between a query and a document on the phrase level assuming that phrases are more important than individual words when retrieving documents.\nAlthough the assumption is similar, our conceptual model is based on the biomedical concepts, not phrases.\n[13] presented a good study of the role of knowledge in the document retrieval of clinical medicine.\nThey have shown that appropriate use of semantic knowledge in a conceptual retrieval framework can yield substantial improvements.\nAlthough the retrieval model is similar, we made a study in the domain of genomics, in which the problem structure and task knowledge is not as well-defined as in the domain of clinical medicine [18].\nAlso, our similarity function is very different from that in [13].\nIn summary, our approach differs from previous works in four important ways: First, we present a case study of conceptual retrieval in the domain of genomics, where many knowledge resources can be used to improve the performance of biomedical IR systems.\nSecond, we have studied more types of domainspecific knowledge than previous researchers and carried out more comprehensive experiments to look into the effects of different types of domain-specific knowledge in performance contribution.\nThird, although some of the techniques seem similar to previously published ones, they are actually quite different in details.\nFor example, in our pseudo-feedback process, we require that the unit of feedback is a concept and the concept has to be of the same semantic type as a query concept.\nThis is to ensure that our conceptual model of retrieval can be applied.\nAs another example, the way in which implicitly related concepts are extracted in this paper is significantly different from that given in [19].\nFinally, our conceptual IR model is actually based on complex concepts because some biomedical meanings, such as biological processes, are represented by multiple simple concepts.\n6.\nCONCLUSION\nThis paper proposed a conceptual approach to utilize domainspecific knowledge in an IR system to improve its effectiveness in retrieving biomedical literature.\nWe specified five different types of domain-specific knowledge (i.e., synonyms, hyponyms, hypernyms, lexical variants, and implicitly related concepts) and examined their effects in performance contribution.\nWe also evaluated other two techniques, pseudo-feedback and abbreviation correction.\nExperimental results have shown that appropriate use of domain-specific knowledge in a conceptual IR model yields significant improvements (23%) in passage retrieval over the best known results.\nIn our future work, we will explore the use of other existing knowledge resources, such as UMLS and the Wikipedia, and evaluate techniques such as disambiguation of gene symbols for improving retrieval effectiveness.\nThe application of our conceptual IR model in other domains such as clinical medicine will be investigated."},"lvl-4":{"kind":"string","value":"Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature\nABSTRACT\nThis paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effectiveness in retrieving biomedical literature.\nThe effects of different types of domain-specific knowledge in performance contribution are examined.\nBased on the TREC platform, we show that appropriate use of domainspecific knowledge in a proposed conceptual retrieval model yields about 23% improvement over the best reported result in passage retrieval in the Genomics Track of TREC 2006.\n1.\nINTRODUCTION\nBiologists search for literature on a daily basis.\nFor most biologists, PubMed, an online service of U.S. National Library of Medicine (NLM), is the most commonly used tool for searching the biomedical literature.\nPubMed allows for keyword search by using Boolean operators.\nKeyword search in PubMed is effective if the query is well-crafted\nby the users using their expertise.\nWhile NLM does maintain an experimental tool for free-text queries [6], it is still based on PubMed keyword search.\nThe Genomics track of the 2006 Text REtrieval Conference (TREC) provides a common platform to assess the methods and techniques proposed by various groups for biomedical information retrieval.\nThe queries were collected from real biologists and they are expressed as complex questions, such as \"How do mutations in the Huntingtin gene affect Huntington's disease?\"\n.\nThe document collection contains 162,259 Highwire full-text documents in HTML format.\nSystems from participating groups are expected to find relevant passages within the full-text documents.\nWe approached the problem by utilizing domain-specific knowledge in a conceptual retrieval model.\nDomain-specific knowledge, in this paper, refers to information about concepts and relationships between concepts in a certain domain.\nWe assume that appropriate use of domain-specific knowledge might improve the effectiveness of retrieval.\nFor example, given a query \"What is the role of gene PRNP in the Mad Cow Disease?\"\nPubMed and many other biomedical systems [8] [9] [10] [13] also make use of domain-specific knowledge to improve retrieval effectiveness.\nIntuitively, retrieval on the level of concepts should outperform \"bag-of-words\" approaches, since the semantic relationships among words in a concept are utilized.\nA concept could be a word, such as the gene symbol \"PRNP\", or a phrase, such as \"Mad cow diseases\".\nIn the conceptual retrieval model presented in this paper, the similarity between a query and a document is measured on both concept and word levels.\nThis paper makes two contributions:\nretrieving biomedical literature.\nBased on this approach, our system achieved significant improvement (23%) over the best reported result in passage retrieval in the Genomics track of TREC 2006.\n2.\nWe examine the effects of utilizing concepts and of different types of domain-specific knowledge in performance contribution.\nThis paper is organized as follows: problem statement is given in the next section.\nThe techniques are introduced in section 3.\nIn section 4, we present the experimental results.\nRelated works are given in section 5 and finally, we conclude the paper in section 6.\n5.\nRELATED WORKS\nMany studies used manually-crafted thesauruses or knowledge databases created by text mining systems to improve retrieval effectiveness based on either word-statistical retrieval systems or conceptual retrieval systems.\n[11] [1] assessed query expansion using the UMLS Metathesaurus.\nBased on a word-statistical retrieval system, [11] used definitions and different types of thesaurus relationships for query expansion and a deteriorated performance was reported.\n[1] expanded queries with phrases and UMLS concepts determined by the MetaMap, a program which maps biomedical text to UMLS concepts, and no significant improvement was shown.\nWe used MeSH, Entrez gene, and other non-thesaurus knowledge resources such as an abbreviation database for query expansion.\nA critical difference between our work and those in [11] [1] is that our retrieval model is based on concepts, not on individual words.\nThe Genomics track in TREC provides a common platform to evaluate methods and techniques proposed by various groups for biomedical information retrieval.\nAs summarized in [8] [9] [10], many groups utilized domain-specific knowledge to improve retrieval effectiveness.\nAmong these groups, [3] assessed both thesaurus-based knowledge, such as gene information, and non thesaurus-based knowledge, such as lexical variants of gene symbols, for query expansion.\nThey have shown that query expansion with acronyms and lexical variants of gene symbols produced the biggest improvement, whereas, the query expansion with gene information from gene databases deteriorated the performance.\n[21] used a similar approach for generating lexical variants of gene symbols and reported significant improvements.\nOur system utilized more types of domain-specific knowledge, including hyponyms, hypernyms and implicitly related concepts.\nIn addition, under the conceptual retrieval framework, we examined more comprehensively the effects of different types of domain-specific knowledge in performance contribution.\n[20] [15] utilized WordNet, a database of English words and their lexical relationships developed by Princeton University, for query expansion in the non-biomedical domain.\nIn their studies, queries were expanded using the lexical semantic relations such as synonyms, hypernyms, or hyponyms.\nThis has been due to ambiguity of the query terms which have different meanings in different contexts.\nWhen these synonyms having multiple meanings are added to the query, substantial irrelevant documents are retrieved.\nThe retrieval model in [15] emphasized the similarity between a query and a document on the phrase level assuming that phrases are more important than individual words when retrieving documents.\nAlthough the assumption is similar, our conceptual model is based on the biomedical concepts, not phrases.\n[13] presented a good study of the role of knowledge in the document retrieval of clinical medicine.\nThey have shown that appropriate use of semantic knowledge in a conceptual retrieval framework can yield substantial improvements.\nAlthough the retrieval model is similar, we made a study in the domain of genomics, in which the problem structure and task knowledge is not as well-defined as in the domain of clinical medicine [18].\nAlso, our similarity function is very different from that in [13].\nIn summary, our approach differs from previous works in four important ways: First, we present a case study of conceptual retrieval in the domain of genomics, where many knowledge resources can be used to improve the performance of biomedical IR systems.\nSecond, we have studied more types of domainspecific knowledge than previous researchers and carried out more comprehensive experiments to look into the effects of different types of domain-specific knowledge in performance contribution.\nFor example, in our pseudo-feedback process, we require that the unit of feedback is a concept and the concept has to be of the same semantic type as a query concept.\nThis is to ensure that our conceptual model of retrieval can be applied.\nAs another example, the way in which implicitly related concepts are extracted in this paper is significantly different from that given in [19].\nFinally, our conceptual IR model is actually based on complex concepts because some biomedical meanings, such as biological processes, are represented by multiple simple concepts.\n6.\nCONCLUSION\nThis paper proposed a conceptual approach to utilize domainspecific knowledge in an IR system to improve its effectiveness in retrieving biomedical literature.\nWe specified five different types of domain-specific knowledge (i.e., synonyms, hyponyms, hypernyms, lexical variants, and implicitly related concepts) and examined their effects in performance contribution.\nWe also evaluated other two techniques, pseudo-feedback and abbreviation correction.\nExperimental results have shown that appropriate use of domain-specific knowledge in a conceptual IR model yields significant improvements (23%) in passage retrieval over the best known results.\nIn our future work, we will explore the use of other existing knowledge resources, such as UMLS and the Wikipedia, and evaluate techniques such as disambiguation of gene symbols for improving retrieval effectiveness.\nThe application of our conceptual IR model in other domains such as clinical medicine will be investigated."}}},{"rowIdx":90,"cells":{"id":{"kind":"string","value":"H-40"},"title":{"kind":"string","value":"Cross-Lingual Query Suggestion Using Query Logs of Different Languages"},"abstract":{"kind":"string","value":"Query suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs. Previously, the suggested terms are mostly in the same language of the input query. In this paper, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages. This is very important to scenarios of cross-language information retrieval (CLIR) and cross-lingual keyword bidding for search engine advertisement. Instead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of the other language in the query log. Important monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics, etc. are used to estimate the cross-lingual query similarity with a discriminative model. Benchmarks show that the resulting CLQS system significantly outperforms a baseline system based on dictionary-based query translation. Besides, the resulting CLQS is tested with French to English CLIR tasks on TREC collections. The results demonstrate higher effectiveness than the traditional query translation methods."},"keyphrases":{"kind":"list like","value":["queri suggest","queri log","cross-languag inform retriev","keyword bid","search engin advertis","search engin","queri translat","map","benchmark","queri expans","bid term","monolingu queri suggest","target languag queri log"],"string":"[\n \"queri suggest\",\n \"queri log\",\n \"cross-languag inform retriev\",\n \"keyword bid\",\n \"search engin advertis\",\n \"search engin\",\n \"queri translat\",\n \"map\",\n \"benchmark\",\n \"queri expans\",\n \"bid term\",\n \"monolingu queri suggest\",\n \"target languag queri log\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","P","P","P","P","P","P","M","R","R","M"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"M\",\n \"R\",\n \"R\",\n \"M\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"Cross-Lingual Query Suggestion Using Query Logs of Different Languages Wei Gao1* , Cheng Niu2 , Jian-Yun Nie3 , Ming Zhou2 , Jian Hu2 , Kam-Fai Wong1 , Hsiao-Wuen Hon2 1 The Chinese University of Hong Kong, Hong Kong, China {wgao, kfwong}@se.\ncuhk.edu.hk 2 Microsoft Research Asia, Beijing, China {chengniu, mingzhou, jianh, hon}@microsoft.\ncom 3 Université de Montréal, Montréal, QC, Canada nie@iro.umontreal.ca ABSTRACT Query suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs.\nPreviously, the suggested terms are mostly in the same language of the input query.\nIn this paper, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages.\nThis is very important to scenarios of cross-language information retrieval (CLIR) and cross-lingual keyword bidding for search engine advertisement.\nInstead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of the other language in the query log.\nImportant monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics, etc. are used to estimate the cross-lingual query similarity with a discriminative model.\nBenchmarks show that the resulting CLQS system significantly outperforms a baseline system based on dictionary-based query translation.\nBesides, the resulting CLQS is tested with French to English CLIR tasks on TREC collections.\nThe results demonstrate higher effectiveness than the traditional query translation methods.\nCategories and Subject Descriptors H.3.3 [Information storage and retrieval]: Information Search and Retrieval - Query formulation General Terms Algorithms, Performance, Experimentation, Theory.\n1.\nINTRODUCTION Query suggestion is a functionality to help users of a search engine to better specify their information need, by narrowing down or expanding the scope of the search with synonymous queries and relevant queries, or by suggesting related queries that have been frequently used by other users.\nSearch engines, such as Google, Yahoo!, MSN, Ask Jeeves, all have implemented query suggestion functionality as a valuable addition to their core search method.\nIn addition, the same technology has been leveraged to recommend bidding terms to online advertiser in the pay-forperformance search market [12].\nQuery suggestion is closely related to query expansion which extends the original query with new search terms to narrow the scope of the search.\nBut different from query expansion, query suggestion aims to suggest full queries that have been formulated by users so that the query integrity and coherence are preserved in the suggested queries.\nTypical methods for query suggestion exploit query logs and document collections, by assuming that in the same period of time, many users share the same or similar interests, which can be expressed in different manners [12, 14, 26].\nBy suggesting the related and frequently used formulations, it is hoped that the new query can cover more relevant documents.\nHowever, all of the existing studies dealt with monolingual query suggestion and to our knowledge, there is no published study on cross-lingual query suggestion (CLQS).\nCLQS aims to suggest related queries but in a different language.\nIt has wide applications on World Wide Web: for cross-language search or for suggesting relevant bidding terms in a different language.\n1 CLQS can be approached as a query translation problem, i.e., to suggest the queries that are translations of the original query.\nDictionaries, large size of parallel corpora and existing commercial machine translation systems can be used for translation.\nHowever, these kinds of approaches usually rely on static knowledge and data.\nIt cannot effectively reflect the quickly shifting interests of Web users.\nMoreover, there are some problems with translated queries in target language.\nFor instance, the translated terms can be reasonable translations, but they are not popularly used in the target language.\nFor example, the French query aliment biologique is translated into biologic food by Google translation tool2 , yet the correct formulation nowadays should be organic food.\nTherefore, there exist many mismatch cases between the translated terms and the really used terms in target language.\nThis mismatch makes the suggested terms in the target language ineffective.\nA natural thinking of solving this mismatch is to map the queries in the source language and the queries in the target language, by using the query log of a search engine.\nWe exploit the fact that the users of search engines in the same period of time have similar interests, and they submit queries on similar topics in different languages.\nAs a result, a query written in a source language likely has an equivalent in a query log in the target language.\nIn particular, if the user intends to perform CLIR, then original query is even more likely to have its correspondent included in the target language query log.\nTherefore, if a candidate for CLQS appears often in the query log, then it is more likely the appropriate one to be suggested.\nIn this paper, we propose a method of calculating the similarity between source language query and the target language query by exploiting, in addition to the translation information, a wide spectrum of bilingual and monolingual information, such as term co-occurrences, query logs with click-through data, etc..\nA discriminative model is used to learn the cross-lingual query similarity based on a set of manually translated queries.\nThe model is trained by optimizing the cross-lingual similarity to best fit the monolingual similarity between one query and the other query``s translation.\nBesides being benchmarked as an independent module, the resulting CLQS system is tested as a new means of query translation in CLIR task on TREC collections.\nThe results show that this new translation method is more effective than the traditional query translation method.\nThe remainder of this paper is organized as follows: Section 2 introduces the related work; Section 3 describes in detail the discriminative model for estimating cross-lingual query similarity; Section 4 presents a new CLIR approach using cross-lingual query suggestion as a bridge across language boundaries.\nSection 5 discusses the experiments and benchmarks; finally, the paper is concluded in Section 6.\n2.\nRELATED WORK Most approaches to CLIR perform a query translation followed by a monolingual IR.\nTypically, queries are translated either using a bilingual dictionary [22], a machine translation software [9] or a parallel corpus [20].\nDespite the various types of resources used, out-of-vocabulary (OOV) words and translation disambiguation are the two major bottlenecks for CLIR [20].\nIn [7, 27], OOV term translations are mined from the Web using a search engine.\nIn [17], bilingual knowledge is acquired based on anchor text analysis.\nIn addition, word co-occurrence statistics in the target language has been leveraged for translation disambiguation [3, 10, 11, 19].\n2 http://www.google.com/language_tools Nevertheless, it is arguable that accurate query translation may not be necessary for CLIR.\nIndeed, in many cases, it is helpful to introduce words even if they are not direct translations of any query word, but are closely related to the meaning of the query.\nThis observation has led to the development of cross-lingual query expansion (CLQE) techniques [2, 16, 18].\n[2] reports the enhancement on CLIR by post-translation expansion.\n[16] develops a cross-lingual relevancy model by leveraging the crosslingual co-occurrence statistics in parallel texts.\n[18] makes performance comparison on multiple CLQE techniques, including pre-translation expansion and post-translation expansion.\nHowever, there is lack of a unified framework to combine the wide spectrum of resources and recent advances of mining techniques for CLQE.\nCLQS is different from CLQE in that it aims to suggest full queries that have been formulated by users in another language.\nAs CLQS exploits up-to-date query logs, it is expected that for most user queries, we can find common formulations on these topics in the query log in the target language.\nTherefore, CLQS also plays a role of adapting the original query formulation to the common formulations of similar topics in the target language.\nQuery logs have been successfully used for monolingual IR [8, 12, 15, 26], especially in monolingual query suggestions [12] and relating the semantically relevant terms for query expansion [8, 15].\nIn [1], the target language query log has been exploited to help query translation in CLIR.\n3.\nESTIMATING CROSS-LINGUAL QUERY SIMILARITY A search engine has a query log containing user queries in different languages within a certain period of time.\nIn addition to query terms, click-through information is also recorded.\nTherefore, we know which documents have been selected by users for each query.\nGiven a query in the source language, our CLQS task is to determine one or several similar queries in the target language from the query log.\nThe key problem with cross-lingual query suggestion is how to learn a similarity measure between two queries in different languages.\nAlthough various statistical similarity measures have been studied for monolingual terms [8, 26], most of them are based on term co-occurrence statistics, and can hardly be applied directly in cross-lingual settings.\nIn order to define a similarity measure across languages, one has to use at least one translation tool or resource.\nSo the measure is based on both translation relation and monolingual similarity.\nIn this paper, as our purpose is to provide up-to-date query similarity measure, it may not be sufficient to use only a static translation resource.\nTherefore, we also integrate a method to mine possible translations on the Web.\nThis method is particularly useful for dealing with OOV terms.\nGiven a set of resources of different natures, the next question is how to integrate them in a principled manner.\nIn this paper, we propose a discriminative model to learn the appropriate similarity measure.\nThe principle is as follows: we assume that we have a reasonable monolingual query similarity measure.\nFor any training query example for which a translation exists, its similarity measure (with any other query) is transposed to its translation.\nTherefore, we have the desired cross-language similarity value for this example.\nThen we use a discriminative model to learn the cross-language similarity function which fits the best these examples.\nIn the following sections, let us first describe the detail of the discriminative model for cross-lingual query similarity estimation.\nThen we introduce all the features (monolingual and cross-lingual information) that we will use in the discriminative model.\n3.1 Discriminative Model for Estimating Cross-Lingual Query Similarity In this section, we propose a discriminative model to learn crosslingual query similarities in a principled manner.\nThe principle is as follows: for a reasonable monolingual query similarity between two queries, a cross-lingual correspondent can be deduced between one query and another query``s translation.\nIn other words, for a pair of queries in different languages, their crosslingual similarity should fit the monolingual similarity between one query and the other query``s translation.\nFor example, the similarity between French query pages jaunes (i.e., yellow page in English) and English query telephone directory should be equal to the monolingual similarity between the translation of the French query yellow page and telephone directory.\nThere are many ways to obtain a monolingual similarity measure between terms, e.g., term co-occurrence based mutual information and 2 χ .\nAny of them can be used as the target for the cross-lingual similarity function to fit.\nIn this way, cross-lingual query similarity estimation is formulated as a regression task as follows: Given a source language query fq , a target language query eq , and a monolingual query similarity MLsim , the corresponding cross-lingual query similarity CLsim is defined as follows: ),(),( eqMLefCL qTsimqqsim f = (1) where fqT is the translation of fq in the target language.\nBased on Equation (1), it would be relatively easy to create a training corpus.\nAll it requires is a list of query translations.\nThen an existing monolingual query suggestion system can be used to automatically produce similar query to each translation, and create the training corpus for cross-lingual similarity estimation.\nAnother advantage is that it is fairly easy to make use of arbitrary information sources within a discriminative modeling framework to achieve optimal performance.\nIn this paper, support vector machine (SVM) regression algorithm [25] is used to learn the cross-lingual term similarity function.\nGiven a vector of feature functions f between fq and eq , ),( efCL ttsim is represented as an inner product between a weight vector and the feature vector in a kernel space as follows: )),((),( efefCL ttfwttsim φ•= (2) where φ is the mapping from the input feature space onto the kernel space, and wis the weight vector in the kernel space which will be learned by the SVM regression training.\nOnce the weight vector is learned, the Equation (2) can be used to estimate the similarity between queries of different languages.\nWe want to point out that instead of regression, one can definitely simplify the task as a binary or ordinal classification, in which case CLQS can be categorized according to discontinuous class labels, e.g., relevant and irrelevant, or a series of levels of relevancies, e.g., strongly relevant, weakly relevant, and irrelevant.\nIn either case, one can resort to discriminative classification approaches, such as an SVM or maximum entropy model, in a straightforward way.\nHowever, the regression formalism enables us to fully rank the suggested queries based on the similarity score given by Equation (1).\nThe Equations (1) and (2) construct a regression model for cross-lingual query similarity estimation.\nIn the following sections, the monolingual query similarity measure (see Section 3.2) and the feature functions used for SVM regression (see Section 3.3) will be presented.\n3.2 Monolingual Query Similarity Measure Based on Click-through Information Any monolingual term similarity measure can be used as the regression target.\nIn this paper, we select the monolingual query similarity measure presented in [26] which reports good performance by using search users'' click-through information in query logs.\nThe reason to choose this monolingual similarity is that it is defined in a similar context as ours − according to a user log that reflects users'' intention and behavior.\nTherefore, we can expect that the cross-language term similarity learned from it can also reflect users'' intention and expectation.\nFollowing [26], our monolingual query similarity is defined by combining both query content-based similarity and click-through commonality in the query log.\nFirst the content similarity between two queries p and q is defined as follows: ))(),(( ),( ),( qknpknMax qpKN qpsimilarity content = (3) where )( xkn is the number of keywords in a query x, ),( qpKN is the number of common keywords in the two queries.\nSecondly, the click-through based similarity is defined as follows, ))(),(( ),( ),( qrdprdMax qpRD qpsimilarity throughclick =− (4) where )(xrd is the number of clicked URLs for a query x, and ),( qpRD is the number of common URLs clicked for two queries.\nFinally, the similarity between two queries is a linear combination of the content-based and click-through-based similarities, and is presented as follows: ),(* ),(*),( qpsimilarity qpsimilarityqpsimilarity throughclick content − += β α (5) where α and β are the relative importance of the two similarity measures.\nIn this paper, we set ,4.0=α and 6.0=β following the practice in [26].\nQueries with similarity measure higher than a threshold with another query will be regarded as relevant monolingual query suggestions (MLQS) for the latter.\nIn this paper, the threshold is set as 0.9 empirically.\n3.3 Features Used for Learning Cross-Lingual Query Similarity Measure This section presents the extraction of candidate relevant queries from the log with the assistance of various monolingual and bilingual resources.\nMeanwhile, feature functions over source query and the cross-lingual relevant candidates are defined.\nSome of the resources being used here, such as bilingual lexicon and parallel corpora, were for query translation in previous work.\nBut note that we employ them here as an assistant means for finding relevant candidates in the log rather than for acquiring accurate translations.\n3.3.1 Bilingual Dictionary In this subsection, a built-in-house bilingual dictionary containing 120,000 unique entries is used to retrieve candidate queries.\nSince multiple translations may be associated with each source word, co-occurrence based translation disambiguation is performed [3, 10].\nThe process is presented as follows: Given an input query }{ ,2,1 fnfff wwwq K= in the source language, for each query term fiw , a set of unique translations are provided by the bilingual dictionary D : },,{)( ,2,1 imiifi tttwD K= .\nThen the cohesion between the translations of two query terms is measured using mutual information which is computed as follows: )()( ),( log),()( , klij klij klijklij tPtP ttP ttPttMI = (6) where . )\n( )(, ),( ),( N tC tP N ttC ttP klij klij == Here ),( yxC is the number of queries in the log containing both x and y , )(xC is the number of queries containing term x , and N is the total number of queries in the log.\nBased on the term-term cohesion defined in Equation (6), all the possible query translations are ranked using the summation of the term-term cohesion ∑≠ = kiki klijqdict ttMITS f ,, ),()( .\nThe set of top-4 query translations is denoted as )( fqTS .\nFor each possible query translation )( fqTST∈ , we retrieve all the queries containing the same keywords as T from the target language log.\nThe retrieved queries are candidate target queries, and are assigned )(TSdict as the value of the feature Dictionary-based Translation Score.\n3.3.2 Parallel Corpora Parallel corpora are precious resources for bilingual knowledge acquisition.\nDifferent from the bilingual dictionary, the bilingual knowledge learned from parallel corpora assigns probability for each translation candidate which is useful in acquiring dominant query translations.\nIn this paper, the Europarl corpus (a set of parallel French and English texts from the proceedings of the European Parliament) is used.\nThe corpus is first sentence aligned.\nThen word alignments are derived by training an IBM translation model 1 [4] using GIZA++ [21].\nThe learned bilingual knowledge is used to extract candidate queries from the query log.\nThe process is presented as follows: Given a pair of queries, fq in the source language and eq in the target language, the Bi-Directional Translation Score is defined as follows: )|()|(),( 111 feIBMefIBMefIBM qqpqqpqqS = (7) where )|(1 xypIBM is the word sequence translation probability given by IBM model 1 which has the following form: ∏∑= =+ = || 1 || 0 ||1 )|( )1|(| 1 )|( y j x i ijyIBM xyp x xyp (8) where )|( ij xyp is the word to word translation probability derived from the word-aligned corpora.\nThe reason to use bidirectional translation probability is to deal with the fact that common words can be considered as possible translations of many words.\nBy using bidirectional translation, we test whether the translation words can be translated back to the source words.\nThis is helpful to focus on the translation probability onto the most specific translation candidates.\nNow, given an input query fq , the top 10 queries }{ eq with the highest bidirectional translation scores with fq are retrieved from the query log, and ),(1 efIBM qqS in Equation (7) is assigned as the value for the feature Bi-Directional Translation Score.\n3.3.3 Online Mining for Related Queries OOV word translation is a major knowledge bottleneck for query translation and CLIR.\nTo overcome this knowledge bottleneck, web mining has been exploited in [7, 27] to acquire EnglishChinese term translations based on the observation that Chinese terms may co-occur with their English translations in the same web page.\nIn this section, this web mining approach is adapted to acquire not only translations but semantically related queries in the target language.\nIt is assumed that if a query in the target language co-occurs with the source query in many web pages, they are probably semantically related.\nTherefore, a simple method is to send the source query to a search engine (Google in our case) for Web pages in the target language in order to find related queries in the target language.\nFor instance, by sending a French query pages jaunes to search for English pages, the English snippets containing the key words yellow pages or telephone directory will be returned.\nHowever, this simple approach may induce significant amount of noise due to the non-relevant returns from the search engine.\nIn order to improve the relevancy of the bilingual snippets, we extend the simple approach by the following query modification: the original query is used to search with the dictionary-based query keyword translations, which are unified by the ∧ (and) ∨ (OR) operators into a single Boolean query.\nFor example, for a given query abcq = where the set of translation entries in the dictionary of for a is },,{ 321 aaa , b is },{ 21 bb and c is }{ 1c , we issue 121321 )()( cbbaaaq ∧∨∧∨∨∧ as one web query.\nFrom the returned top 700 snippets, the most frequent 10 target queries are identified, and are associated with the feature Frequency in the Snippets.\nFurthermore, we use Co-Occurrence Double-Check (CODC) Measure to weight the association between the source and target queries.\nCODC Measure is proposed in [6] as an association measure based on snippet analysis, named Web Search with Double Checking (WSDC) model.\nIn WSDC model, two objects a and b are considered to have an association if b can be found by using a as query (forward process), and a can be found by using b as query (backward process) by web search.\nThe forward process counts the frequency of b in the top N snippets of query a, denoted as )@( abfreq .\nSimilarly, the backward process count the frequency of a in the top N snippets of query b, denoted as )@( bafreq .\nThen the CODC association score is defined as follows: ⎪ ⎩ ⎪ ⎨ ⎧ =× = ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎣ ⎡ × otherwise, 0)@()@(if,0 ),( )( )@( )( )@( log α e ef f fe qfreq qqfreq qfreq qqfreq effe efCODC e qqfreqqqfreq qqS (9) CODC measures the association of two terms in the range between 0 and 1, where under the two extreme cases, eq and fq are of no association when 0)@( =fe qqfreq or 0)@( =ef qqfreq , and are of the strongest association when )()@( ffe qfreqqqfreq = and )()@( eef qfreqqqfreq = .\nIn our experiment, α is set at 0.15 following the practice in [6].\nAny query eq mined from the Web will be associated with a feature CODC Measure with ),( efCODC qqS as its value.\n3.3.4 Monolingual Query Suggestion For all the candidate queries 0Q being retrieved using dictionary (see Section 3.3.1), parallel data (see Section 3.3.2) and web mining (see Section 3.3.3), monolingual query suggestion system (described in Section 3.1) is called to produce more related queries in the target language.\nFor each target query eq , its monolingual source query )( eML qSQ is defined as the query in 0Q with the highest monolingual similarity with eq , i.e., ),(maxarg)( 0 eeMLQqeML qqsimqSQ e ′= ∈′ (10) Then the monolingual similarity between eq and )( eML qSQ is used as the value of the eq ``s Monolingual Query Suggestion Feature.\nFor any target query 0Qq∈ , its Monolingual Query Suggestion Feature is set as 1.\nFor any query 0Qqe ∉ , its values of Dictionary-based Translation Score, Bi-Directional Translation Score, Frequency in the Snippet, and CODC Measure are set to be equal to the feature values of )( eML qSQ .\n3.4 Estimating Cross-lingual Query Similarity In summary, four categories of features are used to learn the crosslingual query similarity.\nSVM regression algorithm [25] is used to learn the weights in Equation (2).\nIn this paper, LibSVM toolkit [5] is used for the regression training.\nIn the prediction stage, the candidate queries will be ranked using the cross-lingual query similarity score computed in terms of )),((),( efefCL ttfwttsim φ•= , and the queries with similarity score lower than a threshold will be regarded as nonrelevant.\nThe threshold is learned using a development data set by fitting MLQS``s output.\n4.\nCLIR BASED ON CROSS-LINGUAL QUERY SUGGESTION In Section 3, we presented a discriminative model for cross lingual query suggestion.\nHowever, objectively benchmarking a query suggestion system is not a trivial task.\nIn this paper, we propose to use CLQS as an alternative to query translation, and test its effectiveness in CLIR tasks.\nThe resulting good performance of CLIR corresponds to the high quality of the suggested queries.\nGiven a source query fq , a set of relevant queries }{ eq in the target language are recommended using the cross-lingual query suggestion system.\nThen a monolingual IR system based on the BM25 model [23] is called using each }{ eqq∈ as queries to retrieve documents.\nThen the retrieved documents are re-ranked based on the sum of the BM25 scores associated with each monolingual retrieval.\n5.\nPERFORMACNCE EVALUATION In this section, we will benchmark the cross-lingual query suggestion system, comparing its performance with monolingual query suggestion, studying the contribution of various information sources, and testing its effectiveness when being used in CLIR tasks.\n5.1 Data Resources In our experiments, French and English are selected as the source and target language respectively.\nSuch selection is due to the fact that large scale query logs are readily available for these two languages.\nA one-month English query log (containing 7 million unique English queries with occurrence frequency more than 5) of MSN search engine is used as the target language log.\nAnd a monolingual query suggestion system is built based on it.\nIn addition, 5,000 French queries are selected randomly from a French query log (containing around 3 million queries), and are manually translated into English by professional French-English translators.\nAmong the 5,000 French queries, 4,171 queries have their translations in the English query log, and are used for CLQS training and testing.\nFurthermore, among the 4,171 French queries, 70% are used for cross-lingual query similarity training, 10% are used as the development data to determine the relevancy threshold, and 20% are used for testing.\nTo retrieve the crosslingual related queries, a built-in-house French-English bilingual lexicon (containing 120,000 unique entries) and the Europarl corpus are used.\nBesides benchmarking CLQS as an independent system, the CLQS is also tested as a query translation system for CLIR tasks.\nBased on the observation that the CLIR performance heavily relies on the quality of the suggested queries, this benchmark measures the quality of CLQS in terms of its effectiveness in helping CLIR.\nTo perform such benchmark, we use the documents of TREC6 CLIR data (AP88-90 newswire, 750MB) with officially provided 25 short French-English queries pairs (CL1-CL25).\nThe selection of this data set is due to the fact that the average length of the queries are 3.3 words long, which matches the web query logs we use to train CLQS.\n5.2 Performance of Cross-lingual Query Suggestion Mean-square-error (MSE) is used to measure the regression error and it is defined as follows: ( )2 ),(),( 1 ∑ −= i eiqMLeifiCL qTsimqqsim l MSE fi where l is the total number of cross-lingual query pairs in the testing data.\nAs described in Section 3.4, a relevancy threshold is learned using the development data, and only CLQS with similarity value above the threshold is regarded as truly relevant to the input query.\nIn this way, CLQS can also be benchmarked as a classification task using precision (P) and recall (R) which are defined as follows: CLQS MLQSCLQS P S SS I = , MLQS MLQSCLQS R S SS I = where CLQSS is the set of relevant queries suggested by CLQS, MLQSS is the set of relevant queries suggested by MLQS (see Section 3.2).\nThe benchmarking results with various feature configurations are shown in Table 1.\nRegression Classification Features MSE P R DD 0.274 0.723 0.098 DD+PC 0.224 0.713 0.125 DD+PC+ Web 0.115 0.808 0.192 DD+PC+ Web+ML QS 0.174 0.796 0.421 Table 1.\nCLQS performance with different feature settings (DD: dictionary only; DD+PC: dictionary and parallel corpora; DD+PC+Web: dictionary, parallel corpora, and web mining; DD+PC+Web+MLQS: dictionary, parallel corpora, web mining and monolingual query suggestion) Table 1 reports the performance comparison with various feature settings.\nThe baseline system (DD) uses a conventional query translation approach, i.e., a bilingual dictionary with cooccurrence-based translation disambiguation.\nThe baseline system only covers less than 10% of the suggestions made by MLQS.\nUsing additional features obviously enables CLQS to generate more relevant queries.\nThe most significant improvement on recall is achieved by exploiting MLQS.\nThe final CLQS system is able to generate 42% of the queries suggested by MLQS.\nAmong all the feature combinations, there is no significant change in precision.\nThis indicates that our methods can improve the recall by effectively leveraging various information sources without losing the accuracy of the suggestions.\nBesides benchmarking CLQS by comparing its output with MLQS output, 200 French queries are randomly selected from the French query log.\nThese queries are double-checked to make sure that they are not in the CLQS training corpus.\nThen CLQS system is used to suggest relevant English queries for them.\nOn average, for each French query, 8.7 relevant English queries are suggested.\nThen the total 1,740 suggested English queries are manually checked by two professional English/French translators with cross-validation.\nAmong the 1,747 suggested queries, 1,407 queries are recognized as relevant to the original ones, hence the accuracy is 80.9%.\nFigure 1 shows an example of CLQS of the French query terrorisme international (international terrorism in English).\n5.3 CLIR Performance In this section, CLQS is tested with French to English CLIR tasks.\nWe conduct CLIR experiments using the TREC 6 CLIR dataset described in Section 5.1.\nThe CLIR is performed using a query translation system followed by a BM25-based [23] monolingual IR module.\nThe following three different systems have been used to perform query translation: (1) CLQS: our CLQS system; (2) MT: Google French to English machine translation system; (3) DT: a dictionary based query translation system using cooccurrence statistics for translation disambiguation.\nThe translation disambiguation algorithm is presented in Section 3.3.1.\nBesides, the monolingual IR performance is also reported as a reference.\nThe average precision of the four IR systems are reported in Table 2, and the 11-point precision-recall curves are shown in Figure 2.\nTable 2.\nAverage precision of CLIR on TREC 6 Dataset (Monolingual: monolingual IR system; MT: CLIR based on machine translation; DT: CLIR based on dictionary translation; CLQS: CLQS-based CLIR) IR System Average Precision % of Monolingual IR Monolingual 0.266 100% MT 0.217 81.6% DT 0.186 69.9% CLQS 0.233 87.6% Figure 1.\nAn example of CLQS of the French query terrorisme international international terrorism (0.991); what is terrorism (0.943); counter terrorism (0.920); terrorist (0.911); terrorist attacks (0.898); international terrorist (0.853); world terrorism (0.845); global terrorism (0.833); transnational terrorism (0.821); human rights (0.811); terrorist groups (0.\n777); patterns of global terrorism (0.762) september 11 (0.734) 11-point P-R curves (TREC6) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Recall Precison Monolingual MT DT CLQS The benchmark shows that using CLQS as a query translation tool outperforms CLIR based on machine translation by 7.4%, outperforms CLIR based on dictionary translation by 25.2%, and achieves 87.6% of the monolingual IR performance.\nThe effectiveness of CLQS lies in its ability in suggesting closely related queries besides accurate translations.\nFor example, for the query CL14 terrorisme international (international terrorism), although the machine translation tool translates the query correctly, CLQS system still achieves higher score by recommending many additional related terms such as global terrorism, world terrorism, etc. (as shown in Figure 1).\nAnother example is the query La pollution causée par l'automobile (air pollution due to automobile) of CL6.\nThe MT tool provides the translation the pollution caused by the car, while CLQS system enumerates all the possible synonyms of car, and suggest the following queries car pollution, auto pollution, automobile pollution.\nBesides, other related queries such as global warming are also suggested.\nFor the query CL12 La culture écologique (organic farming), the MT tool fails to generate the correct translation.\nAlthough the correct translation is neither in our French-English dictionary, CLQS system generates organic farm as a relevant query due to successful web mining.\nThe above experiment demonstrates the effectiveness of using CLQS to suggest relevant queries for CLIR enhancement.\nA related research is to perform query expansion to enhance CLIR [2, 18].\nSo it is very interesting to compare the CLQS approach with the conventional query expansion approaches.\nFollowing [18], post-translation expansion is performed based on pseudorelevance feedback (PRF) techniques.\nWe first perform CLIR in the same way as before.\nThen we use the traditional PRF algorithm described in [24] to select expansion terms.\nIn our experiments, the top 10 terms are selected to expand the original query, and the new query is used to search the collection for the second time.\nThe new CLIR performance in terms of average precision is shown in Table 3.\nThe 11-point P-R curves are drawn in Figure 3.\nAlthough being enhanced by pseudo-relevance feedback, the CLIR using either machine translation or dictionary-based query translation still does not perform as well as CLQS-based approach.\nStatistical t-test [13] is conducted to indicate whether the CLQS-based CLIR performs significantly better.\nPair-wise pvalues are shown in Table 4.\nClearly, CLQS significantly outperforms MT and DT without PRF as well as DT+PRF, but its superiority over MT+PRF is not significant.\nHowever, when combined with PRF, CLQS significant outperforms all the other methods.\nThis indicates the higher effectiveness of CLQS in related term identification by leveraging a wide spectrum of resources.\nFurthermore, post-translation expansion is capable of improving CLQS-based CLIR.\nThis is due to the fact that CLQS and pseudo-relevance feedback are leveraging different categories of resources, and both approaches can be complementary.\nIR System AP without PRF AP with PRF Monolingual 0.266 (100%) 0.288 (100%) MT 0.217 (81.6%) 0.222 (77.1%) DT 0.186 (69.9%) 0.220 (76.4%) CLQS 0.233 (87.6%) 0.259 (89.9%) 11-point P-R curves with pseudo relevance feedback (TREC6) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Recall Precison Monolingual MT DT CLQS MT DT MT+PRF DT+PRF CLQS 0.0298 3.84e-05 0.1472 0.0282 CLQS+PR F 0.0026 2.63e-05 0.0094 0.0016 6.\nCONCLUSIONS In this paper, we proposed a new approach to cross-lingual query suggestion by mining relevant queries in different languages from query logs.\nThe key solution to this problem is to learn a crosslingual query similarity measure by a discriminative model exploiting multiple monolingual and bilingual resources.\nThe model is trained based on the principle that cross-lingual similarity should best fit the monolingual similarity between one query and the other query``s translation.\nFigure 2.\n11 points precision-recall on TREC6 CLIR data set Figure 3.\n11 points precision-recall on TREC6 CLIR dataset with pseudo relevance feedback Table 3.\nComparison of average precision (AP) on TREC 6 without and with post-translation expansion.\n(%) are the relative percentages over the monolingual IR performance Table 4.\nThe results of pair-wise significance t-test.\nHere pvalue < 0.05 is considered statistically significant The baseline CLQS system applies a typical query translation approach, using a bilingual dictionary with co-occurrence-based translation disambiguation.\nThis approach only covers 10% of the relevant queries suggested by an MLQS system (when the exact translation of the original query is given).\nBy leveraging additional resources such as parallel corpora, web mining and logbased monolingual query expansion, the final system is able to cover 42% of the relevant queries suggested by an MLQS system with precision as high as 79.6%.\nTo further test the quality of the suggested queries, CLQS system is used as a query translation system in CLIR tasks.\nBenchmarked using TREC 6 French to English CLIR task, CLQS demonstrates higher effectiveness than the traditional query translation methods using either bilingual dictionary or commercial machine translation tools.\nThe improvement on TREC French to English CLIR task by using CLQS demonstrates the high quality of the suggested queries.\nThis also shows the strong correspondence between the input French queries and English queries in the log.\nIn the future, we will build CLQS system between languages which may be more loosely correlated, e.g., English and Chinese, and study the CLQS performance change due to the less strong correspondence among queries in such languages.\n7.\nREFERENCES [1] Ambati, V. and Rohini., U. 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Dictionary-Based Cross-Language Information Retrieval: Problems, Methods, and Research Findings.\nInformation Retrieval, 4(3/4):209-230, 2001.\n[23] Robertson, S. E., Walker, S., Hancock-Beaulieu, M. M., and Gatford, M. OKAPI at TREC-3.\nIn Proc.TREC-3, pp.\n200225, 1995.\n[24] Robertson, S. E. and Jones, K. S. Relevance Weighting of Search Terms.\nJournal of the American Society of Information Science, 27(3):129-146, 1976.\n[25] Smola, A. J. and Schölkopf, B. A. Tutorial on Support Vector Regression.\nStatistics and Computing, 14(3):199-222, 2004.\n[26] Wen, J. R., Nie, J.-Y., and Zhang, H. J. Query Clustering Using User Logs.\nACM Trans.\nInformation Systems, 20(1):59-81, 2002.\n[27] Zhang, Y. and Vines, P. Using the Web for Automated Translation Extraction in Cross-Language Information Retrieval.\nIn Proc.\nSIGIR 2004, pp. 162-169."},"lvl-2":{"kind":"string","value":"Cross-Lingual Query Suggestion Using Query Logs of Different Languages\nABSTRACT\nQuery suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs.\nPreviously, the suggested terms are mostly in the same language of the input query.\nIn this paper, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages.\nThis is very important to scenarios of cross-language information retrieval (CLIR) and cross-lingual keyword bidding for search engine advertisement.\nInstead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of the other language in the query log.\nImportant monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics, etc. are used to estimate the cross-lingual query similarity with a discriminative model.\nBenchmarks show that the resulting CLQS system significantly outperforms a baseline system based on dictionary-based query translation.\nBesides, the resulting CLQS is tested with French to English CLIR tasks on TREC collections.\nThe results demonstrate higher effectiveness than the traditional query translation methods.\n1.\nINTRODUCTION\nQuery suggestion is a functionality to help users of a search engine to better specify their information need, by narrowing down or expanding the scope of the search with synonymous queries and relevant queries, or by suggesting related queries that have been frequently used by other users.\nSearch engines, such as Google, Yahoo!, MSN, Ask Jeeves, all have implemented query suggestion functionality as a valuable addition to their core search method.\nIn addition, the same technology has been leveraged to recommend bidding terms to online advertiser in the pay-forperformance search market [12].\nQuery suggestion is closely related to query expansion which extends the original query with new search terms to narrow the scope of the search.\nBut different from query expansion, query suggestion aims to suggest full queries that have been formulated by users so that the query integrity and coherence are preserved in the suggested queries.\nTypical methods for query suggestion exploit query logs and document collections, by assuming that in the same period of time, many users share the same or similar interests, which can be expressed in different manners [12, 14, 26].\nBy suggesting the related and frequently used formulations, it is hoped that the new query can cover more relevant documents.\nHowever, all of the existing studies dealt with monolingual query suggestion and to our knowledge, there is no published study on cross-lingual query suggestion (CLQS).\nCLQS aims to suggest related queries but in a different language.\nIt has wide applications on World Wide Web: for cross-language search or for suggesting relevant bidding terms in a different language.\nCLQS can be approached as a query translation problem, i.e., to suggest the queries that are translations of the original query.\nDictionaries, large size of parallel corpora and existing commercial machine translation systems can be used for translation.\nHowever, these kinds of approaches usually rely on static knowledge and data.\nIt cannot effectively reflect the quickly shifting interests of Web users.\nMoreover, there are some problems with translated queries in target language.\nFor instance,\nthe translated terms can be reasonable translations, but they are not popularly used in the target language.\nFor example, the French query \"aliment biologique\" is translated into \"biologic food\" by Google translation tool2, yet the correct formulation nowadays should be \"organic food\".\nTherefore, there exist many mismatch cases between the translated terms and the really used terms in target language.\nThis mismatch makes the suggested terms in the target language ineffective.\nA natural thinking of solving this mismatch is to map the queries in the source language and the queries in the target language, by using the query log of a search engine.\nWe exploit the fact that the users of search engines in the same period of time have similar interests, and they submit queries on similar topics in different languages.\nAs a result, a query written in a source language likely has an equivalent in a query log in the target language.\nIn particular, if the user intends to perform CLIR, then original query is even more likely to have its correspondent included in the target language query log.\nTherefore, if a candidate for CLQS appears often in the query log, then it is more likely the appropriate one to be suggested.\nIn this paper, we propose a method of calculating the similarity between source language query and the target language query by exploiting, in addition to the translation information, a wide spectrum of bilingual and monolingual information, such as term co-occurrences, query logs with click-through data, etc. .\nA discriminative model is used to learn the cross-lingual query similarity based on a set of manually translated queries.\nThe model is trained by optimizing the cross-lingual similarity to best fit the monolingual similarity between one query and the other query's translation.\nBesides being benchmarked as an independent module, the resulting CLQS system is tested as a new means of query \"translation\" in CLIR task on TREC collections.\nThe results show that this new \"translation\" method is more effective than the traditional query translation method.\nThe remainder of this paper is organized as follows: Section 2 introduces the related work; Section 3 describes in detail the discriminative model for estimating cross-lingual query similarity; Section 4 presents a new CLIR approach using cross-lingual query suggestion as a bridge across language boundaries.\nSection 5 discusses the experiments and benchmarks; finally, the paper is concluded in Section 6.\n2.\nRELATED WORK\nMost approaches to CLIR perform a query translation followed by a monolingual IR.\nTypically, queries are translated either using a bilingual dictionary [22], a machine translation software [9] or a parallel corpus [20].\nDespite the various types of resources used, out-of-vocabulary (OOV) words and translation disambiguation are the two major bottlenecks for CLIR [20].\nIn [7, 27], OOV term translations are mined from the Web using a search engine.\nIn [17], bilingual knowledge is acquired based on anchor text analysis.\nIn addition, word co-occurrence statistics in the target language has been leveraged for translation disambiguation [3, 10, 11, 19].\nNevertheless, it is arguable that accurate query translation may not be necessary for CLIR.\nIndeed, in many cases, it is helpful to introduce words even if they are not direct translations of any query word, but are closely related to the meaning of the query.\nThis observation has led to the development of cross-lingual query expansion (CLQE) techniques [2, 16, 18].\n[2] reports the enhancement on CLIR by post-translation expansion.\n[16] develops a cross-lingual relevancy model by leveraging the crosslingual co-occurrence statistics in parallel texts.\n[18] makes performance comparison on multiple CLQE techniques, including pre-translation expansion and post-translation expansion.\nHowever, there is lack of a unified framework to combine the wide spectrum of resources and recent advances of mining techniques for CLQE.\nCLQS is different from CLQE in that it aims to suggest full queries that have been formulated by users in another language.\nAs CLQS exploits up-to-date query logs, it is expected that for most user queries, we can find common formulations on these topics in the query log in the target language.\nTherefore, CLQS also plays a role of adapting the original query formulation to the common formulations of similar topics in the target language.\nQuery logs have been successfully used for monolingual IR [8, 12, 15, 26], especially in monolingual query suggestions [12] and relating the semantically relevant terms for query expansion [8, 15].\nIn [1], the target language query log has been exploited to help query translation in CLIR.\n3.\nESTIMATING CROSS-LINGUAL QUERY SIMILARITY\nA search engine has a query log containing user queries in different languages within a certain period of time.\nIn addition to query terms, click-through information is also recorded.\nTherefore, we know which documents have been selected by users for each query.\nGiven a query in the source language, our CLQS task is to determine one or several similar queries in the target language from the query log.\nThe key problem with cross-lingual query suggestion is how to learn a similarity measure between two queries in different languages.\nAlthough various statistical similarity measures have been studied for monolingual terms [8, 26], most of them are based on term co-occurrence statistics, and can hardly be applied directly in cross-lingual settings.\nIn order to define a similarity measure across languages, one has to use at least one translation tool or resource.\nSo the measure is based on both translation relation and monolingual similarity.\nIn this paper, as our purpose is to provide up-to-date query similarity measure, it may not be sufficient to use only a static translation resource.\nTherefore, we also integrate a method to mine possible translations on the Web.\nThis method is particularly useful for dealing with OOV terms.\nGiven a set of resources of different natures, the next question is how to integrate them in a principled manner.\nIn this paper, we propose a discriminative model to learn the appropriate similarity measure.\nThe principle is as follows: we assume that we have a reasonable monolingual query similarity measure.\nFor any training query example for which a translation exists, its similarity measure (with any other query) is transposed to its translation.\nTherefore, we have the desired cross-language similarity value for this example.\nThen we use a discriminative model to learn the cross-language similarity function which fits the best these examples.\nIn the following sections, let us first describe the detail of the discriminative model for cross-lingual query similarity estimation.\nThen we introduce all the features (monolingual and cross-lingual information) that we will use in the discriminative model.\n3.1 Discriminative Model for Estimating Cross-Lingual Query Similarity\nIn this section, we propose a discriminative model to learn crosslingual query similarities in a principled manner.\nThe principle is as follows: for a reasonable monolingual query similarity between two queries, a cross-lingual correspondent can be deduced between one query and another query's translation.\nIn other words, for a pair of queries in different languages, their crosslingual similarity should fit the monolingual similarity between one query and the other query's translation.\nFor example, the similarity between French query \"pages jaunes\" (i.e., \"yellow page\" in English) and English query \"telephone directory\" should be equal to the monolingual similarity between the translation of the French query \"yellow page\" and \"telephone directory\".\nThere are many ways to obtain a monolingual similarity measure between terms, e.g., term co-occurrence based mutual information and 2 χ.\nAny of them can be used as the target for the cross-lingual similarity function to fit.\nIn this way, cross-lingual query similarity estimation is formulated as a regression task as follows: Given a source language query q f, a target language query qe, and a monolingual query similarity simML, the corresponding cross-lingual query similarity simCL is defined as follows: sim CL (q f, qe) sim ML (T q f, q e = where Tqf is the translation of q f in the target language.\nBased on Equation (1), it would be relatively easy to create a training corpus.\nAll it requires is a list of query translations.\nThen an existing monolingual query suggestion system can be used to automatically produce similar query to each translation, and create the training corpus for cross-lingual similarity estimation.\nAnother advantage is that it is fairly easy to make use of arbitrary information sources within a discriminative modeling framework to achieve optimal performance.\nIn this paper, support vector machine (SVM) regression algorithm [25] is used to learn the cross-lingual term similarity function.\nGiven a vector of feature functions f between q f and qe, simCL (t f, te) is represented as an inner product between a weight vector and the feature vector in a kernel space as follows: sim CL (t f, te) = w • φ (f (tf, te)) (2) where φ is the mapping from the input feature space onto the kernel space, and w is the weight vector in the kernel space which will be learned by the SVM regression training.\nOnce the weight vector is learned, the Equation (2) can be used to estimate the similarity between queries of different languages.\nWe want to point out that instead of regression, one can definitely simplify the task as a binary or ordinal classification, in which case CLQS can be categorized according to discontinuous class labels, e.g., relevant and irrelevant, or a series of levels of relevancies, e.g., strongly relevant, weakly relevant, and irrelevant.\nIn either case, one can resort to discriminative classification approaches, such as an SVM or maximum entropy model, in a straightforward way.\nHowever, the regression formalism enables us to fully rank the suggested queries based on the similarity score given by Equation (1).\nThe Equations (1) and (2) construct a regression model for cross-lingual query similarity estimation.\nIn the following sections, the monolingual query similarity measure (see Section 3.2) and the feature functions used for SVM regression (see Section 3.3) will be presented.\n3.2 Monolingual Query Similarity Measure Based on Click-through Information\nAny monolingual term similarity measure can be used as the regression target.\nIn this paper, we select the monolingual query similarity measure presented in [26] which reports good performance by using search users' click-through information in query logs.\nThe reason to choose this monolingual similarity is that it is defined in a similar context as ours − according to a user log that reflects users' intention and behavior.\nTherefore, we can expect that the cross-language term similarity learned from it can also reflect users' intention and expectation.\nFollowing [26], our monolingual query similarity is defined by combining both query content-based similarity and click-through commonality in the query log.\nFirst the content similarity between two queries p and q is defined as follows:\nwhere kn (x) is the number of keywords in a query x, KN (p, q) is the number of common keywords in the two queries.\nSecondly, the click-through based similarity is defined as follows, similarity click − through p q =\nwhere rd (x) is the number of clicked URLs for a query x, and RD (p, q) is the number of common URLs clicked for two queries.\nFinally, the similarity between two queries is a linear combination of the content-based and click-through-based similarities, and is presented as follows:\nwhere α and β are the relative importance of the two similarity measures.\nIn this paper, we set α = 0.4, and β = 0.6 following the\npractice in [26].\nQueries with similarity measure higher than a threshold with another query will be regarded as relevant monolingual query suggestions (MLQS) for the latter.\nIn this paper, the threshold is set as 0.9 empirically.\n3.3 Features Used for Learning Cross-Lingual Query Similarity Measure\nThis section presents the extraction of candidate relevant queries from the log with the assistance of various monolingual and bilingual resources.\nMeanwhile, feature functions over source query and the cross-lingual relevant candidates are defined.\nSome of the resources being used here, such as bilingual lexicon and parallel corpora, were for query translation in previous work.\nBut note that we employ them here as an assistant means for finding relevant candidates in the log rather than for acquiring accurate translations.\n3.3.1 Bilingual Dictionary\nIn this subsection, a built-in-house bilingual dictionary containing 120,000 unique entries is used to retrieve candidate queries.\nSince multiple translations may be associated with each source word, co-occurrence based translation disambiguation is performed [3, 10].\nThe process is presented as follows: Given an input query q f = {w f1, w f2,...wfn} in the source language, for each query term wfi, a set of unique translations are provided by the bilingual dictionaryD: D (wfi) = {ti1, ti2,,..., tim}.\nThen the cohesion between the translations of two query terms is measured using mutual information which is computed as follows:\nHere C (x, y) is the number of queries in the log containing both x and y, C (x) is the number of queries containing term x, and N is the total number of queries in the log.\nBased on the term-term cohesion defined in Equation (6), all the possible query translations are ranked using the summation of the term-term cohesion Sdict Tq f\ntop-4 query translations is denoted as S (Tqf).\nFor each possible query translationT ∈ S (Tqf), we retrieve all the queries containing the same keywords as T from the target language log.\nThe retrieved queries are candidate target queries, and are assigned Sdict (T) as the value of the feature Dictionary-based Translation Score.\n3.3.2 Parallel Corpora\nParallel corpora are precious resources for bilingual knowledge acquisition.\nDifferent from the bilingual dictionary, the bilingual knowledge learned from parallel corpora assigns probability for each translation candidate which is useful in acquiring dominant query translations.\nIn this paper, the Europarl corpus (a set of parallel French and English texts from the proceedings of the European Parliament) is used.\nThe corpus is first sentence aligned.\nThen word alignments are derived by training an IBM translation model 1 [4] using GIZA + + [21].\nThe learned bilingual knowledge is used to extract candidate queries from the query log.\nThe process is presented as follows: Given a pair of queries, q f in the source language and qe in the target language, the Bi-Directional Translation Score is defined as follows:\nwhere pIBM1 (y | x) is the word sequence translation probability given by IBM model 1 which has the following form:\nwhere p (yj | xi) is the word to word translation probability derived from the word-aligned corpora.\nThe reason to use bidirectional translation probability is to deal with the fact that common words can be considered as possible translations of many words.\nBy using bidirectional translation, we test whether the translation words can be translated back to the source words.\nThis is helpful to focus on the translation probability onto the most specific translation candidates.\nNow, given an input query qf, the top 10 queries {qe} with the highest bidirectional translation scores with qf are retrieved from the query log, and S IBM1 (q f, qe) in Equation (7) is assigned as the value for the feature Bi-Directional Translation Score.\n3.3.3 Online Mining for Related Queries\nOOV word translation is a major knowledge bottleneck for query translation and CLIR.\nTo overcome this knowledge bottleneck, web mining has been exploited in [7, 27] to acquire EnglishChinese term translations based on the observation that Chinese terms may co-occur with their English translations in the same web page.\nIn this section, this web mining approach is adapted to acquire not only translations but semantically related queries in the target language.\nIt is assumed that if a query in the target language co-occurs with the source query in many web pages, they are probably semantically related.\nTherefore, a simple method is to send the source query to a search engine (Google in our case) for Web pages in the target language in order to find related queries in the target language.\nFor instance, by sending a French query \"pages jaunes\" to search for English pages, the English snippets containing the key words \"yellow pages\" or \"telephone directory\" will be returned.\nHowever, this simple approach may induce significant amount of noise due to the non-relevant returns from the search engine.\nIn order to improve the relevancy of the bilingual snippets, we extend the simple approach by the following query modification: the original query is used to search with the dictionary-based query keyword translations, which are unified by the n (and) ∨ (OR) operators into a single Boolean query.\nFor example, for a given query q = abc where the set of where MI tij t).\nThe set of (, kl\none web query.\nFrom the returned top 700 snippets, the most frequent 10 target queries are identified, and are associated with the feature Frequency in the Snippets.\nFurthermore, we use Co-Occurrence Double-Check (CODC) Measure to weight the association between the source and target queries.\nCODC Measure is proposed in [6] as an association measure based on snippet analysis, named Web Search with Double Checking (WSDC) model.\nIn WSDC model, two objects a and b are considered to have an association if b can be found by using a as query (forward process), and a can be found by using b as query (backward process) by web search.\nThe forward process counts the frequency of b in the top N snippets of query a, denoted as freq (b @a).\nSimilarly, the backward process count the frequency of a in the top N snippets of query b, denoted as freq (a@b).\nThen the CODC association score is defined as follows: CODC measures the association of two terms in the range between 0 and 1, where under the two extreme cases, qe and q f are of no association when freq (qe @ q f) = 0 or freq (q f @qe) = 0, and are of the strongest association when freq (qe @ q f) = freq (q f) and freq (q f @ qe) = freq (qe).\nIn our experiment, α is set at 0.15 following the practice in [6].\nAny query qe mined from the Web will be associated with a feature CODC Measure with S CODC (qf, qe) as its value.\n3.3.4 Monolingual Query Suggestion\nFor all the candidate queries Q0 being retrieved using dictionary (see Section 3.3.1), parallel data (see Section 3.3.2) and web mining (see Section 3.3.3), monolingual query suggestion system (described in Section 3.1) is called to produce more related queries in the target language.\nFor each target query qe, its monolingual source query SQML (qe) is defined as the query in Q0 with the highest monolingual similarity with qe, i.e.,\nThen the monolingual similarity between qe and SQML (qe) is used as the value of the qe's Monolingual Query Suggestion Feature.\nFor any target query q ∈ Q0, its Monolingual Query Suggestion Feature is set as 1.\nFor any query qe ∉ Q0, its values of Dictionary-based Translation Score, Bi-Directional Translation Score, Frequency in the Snippet, and CODC Measure are set to be equal to the feature values of SQML (qe).\n3.4 Estimating Cross-lingual Query Similarity\nIn summary, four categories of features are used to learn the crosslingual query similarity.\nSVM regression algorithm [25] is used to learn the weights in Equation (2).\nIn this paper, LibSVM toolkit [5] is used for the regression training.\nIn the prediction stage, the candidate queries will be ranked using the cross-lingual query similarity score computed in terms of simCL (tf, te) = w • φ (f (tf, te)), and the queries with similarity score lower than a threshold will be regarded as nonrelevant.\nThe threshold is learned using a development data set by fitting MLQS's output.\n4.\nCLIR BASED ON CROSS-LINGUAL QUERY SUGGESTION\nIn Section 3, we presented a discriminative model for cross lingual query suggestion.\nHowever, objectively benchmarking a query suggestion system is not a trivial task.\nIn this paper, we propose to use CLQS as an alternative to query translation, and test its effectiveness in CLIR tasks.\nThe resulting good performance of CLIR corresponds to the high quality of the suggested queries.\nGiven a source query q f, a set of relevant queries {qe} in the target language are recommended using the cross-lingual query suggestion system.\nThen a monolingual IR system based on the BM25 model [23] is called using each q ∈ {qe} as queries to retrieve documents.\nThen the retrieved documents are re-ranked based on the sum of the BM25 scores associated with each monolingual retrieval.\n5.\nPERFORMACNCE EVALUATION\nIn this section, we will benchmark the cross-lingual query suggestion system, comparing its performance with monolingual query suggestion, studying the contribution of various information sources, and testing its effectiveness when being used in CLIR tasks.\n5.1 Data Resources\nIn our experiments, French and English are selected as the source and target language respectively.\nSuch selection is due to the fact that large scale query logs are readily available for these two languages.\nA one-month English query log (containing 7 million unique English queries with occurrence frequency more than 5) of MSN search engine is used as the target language log.\nAnd a monolingual query suggestion system is built based on it.\nIn addition, 5,000 French queries are selected randomly from a French query log (containing around 3 million queries), and are manually translated into English by professional French-English translators.\nAmong the 5,000 French queries, 4,171 queries have their translations in the English query log, and are used for CLQS training and testing.\nFurthermore, among the 4,171 French queries, 70% are used for cross-lingual query similarity training, 10% are used as the development data to determine the relevancy threshold, and 20% are used for testing.\nTo retrieve the crosslingual related queries, a built-in-house French-English bilingual lexicon (containing 120,000 unique entries) and the Europarl corpus are used.\nBesides benchmarking CLQS as an independent system, the CLQS is also tested as a query \"translation\" system for CLIR\ntasks.\nBased on the observation that the CLIR performance heavily relies on the quality of the suggested queries, this benchmark measures the quality of CLQS in terms of its effectiveness in helping CLIR.\nTo perform such benchmark, we use the documents of TREC6 CLIR data (AP88-90 newswire, 750MB) with officially provided 25 short French-English queries pairs (CL1-CL25).\nThe selection of this data set is due to the fact that the average length of the queries are 3.3 words long, which matches the web query logs we use to train CLQS.\n5.2 Performance of Cross-lingual Query Suggestion\nMean-square-error (MSE) is used to measure the regression error and it is defined as follows:\nwhere l is the total number of cross-lingual query pairs in the testing data.\nAs described in Section 3.4, a relevancy threshold is learned using the development data, and only CLQS with similarity value above the threshold is regarded as truly relevant to the input query.\nIn this way, CLQS can also be benchmarked as a classification task using precision (P) and recall (R) which are defined as follows: where S CLQS is the set of relevant queries suggested by CLQS, S MLQS is the set of relevant queries suggested by MLQS (see Section 3.2).\nThe benchmarking results with various feature configurations are shown in Table 1.\nTable 1.\nCLQS performance with different feature settings\n(DD: dictionary only; DD+PC: dictionary and parallel corpora; DD+PC+W eb: dictionary, parallel corpora, and web mining; DD+PC+W eb + MLQS: dictionary, parallel corpora, web mining and monolingual query suggestion) Table 1 reports the performance comparison with various feature settings.\nThe baseline system (DD) uses a conventional query translation approach, i.e., a bilingual dictionary with cooccurrence-based translation disambiguation.\nThe baseline system only covers less than 10% of the suggestions made by MLQS.\nUsing additional features obviously enables CLQS to generate more relevant queries.\nThe most significant improvement on recall is achieved by exploiting MLQS.\nThe final CLQS system is able to generate 42% of the queries suggested by MLQS.\nAmong all the feature combinations, there is no significant change in precision.\nThis indicates that our methods can improve the recall by effectively leveraging various information sources without losing the accuracy of the suggestions.\nBesides benchmarking CLQS by comparing its output with MLQS output, 200 French queries are randomly selected from the French query log.\nThese queries are double-checked to make sure that they are not in the CLQS training corpus.\nThen CLQS system is used to suggest relevant English queries for them.\nOn average, for each French query, 8.7 relevant English queries are suggested.\nThen the total 1,740 suggested English queries are manually checked by two professional English/French translators with cross-validation.\nAmong the 1,747 suggested queries, 1,407 queries are recognized as relevant to the original ones, hence the accuracy is 80.9%.\nFigure 1 shows an example of CLQS of the French query \"terrorisme international\" (\"international terrorism\" in English).\ninternational terrorism (0.991); what is terrorism (0.943); counter terrorism (0.920); terrorist (0.911); terrorist attacks (0.898); international terrorist (0.853); world terrorism (0.845); global terrorism (0.833); transnational terrorism (0.821); human rights (0.811); terrorist groups (0.\n777); patterns of global terrorism (0.762) september 11 (0.734)\nFigure 1.\nAn example of CLQS of the French query \"terrorisme international\"\n5.3 CLIR Performance\nIn this section, CLQS is tested with French to English CLIR tasks.\nWe conduct CLIR experiments using the TREC 6 CLIR dataset described in Section 5.1.\nThe CLIR is performed using a query translation system followed by a BM25-based [23] monolingual IR module.\nThe following three different systems have been used to perform query translation: (1) CLQS: our CLQS system; (2) MT: Google French to English machine translation system; (3) DT: a dictionary based query translation system using cooccurrence statistics for translation disambiguation.\nThe translation disambiguation algorithm is presented in Section 3.3.1.\nBesides, the monolingual IR performance is also reported as a reference.\nThe average precision of the four IR systems are reported in Table 2, and the 11-point precision-recall curves are shown in Figure 2.\nTable 2.\nAverage precision of CLIR on TREC 6 Dataset\n(Monolingual: monolingual IR system; MT: CLIR based on machine translation; DT: CLIR based on dictionary translation; CLQS: CLQS-based CLIR)\nFigure 2.\n11 points precision-recall on TREC6 CLIR data set\nThe benchmark shows that using CLQS as a query translation tool outperforms CLIR based on machine translation by 7.4%, outperforms CLIR based on dictionary translation by 25.2%, and achieves 87.6% of the monolingual IR performance.\nThe effectiveness of CLQS lies in its ability in suggesting closely related queries besides accurate translations.\nFor example, for the query CL14 \"terrorisme international\" (\"international terrorism\"), although the machine translation tool translates the query correctly, CLQS system still achieves higher score by recommending many additional related terms such as \"global terrorism\", \"world terrorism\", etc. (as shown in Figure 1).\nAnother example is the query \"La pollution causée par l'automobile\" (\"air pollution due to automobile\") of CL6.\nThe MT tool provides the translation \"the pollution caused by the car\", while CLQS system enumerates all the possible synonyms of \"car\", and suggest the following queries \"car pollution\", \"auto pollution\", \"automobile pollution\".\nBesides, other related queries such as \"global warming\" are also suggested.\nFor the query CL12 \"La culture écologique\" (\"organic farming\"), the MT tool fails to generate the correct translation.\nAlthough the correct translation is neither in our French-English dictionary, CLQS system generates \"organic farm\" as a relevant query due to successful web mining.\nThe above experiment demonstrates the effectiveness of using CLQS to suggest relevant queries for CLIR enhancement.\nA related research is to perform query expansion to enhance CLIR [2, 18].\nSo it is very interesting to compare the CLQS approach with the conventional query expansion approaches.\nFollowing [18], post-translation expansion is performed based on pseudorelevance feedback (PRF) techniques.\nWe first perform CLIR in the same way as before.\nThen we use the traditional PRF algorithm described in [24] to select expansion terms.\nIn our experiments, the top 10 terms are selected to expand the original query, and the new query is used to search the collection for the second time.\nThe new CLIR performance in terms of average precision is shown in Table 3.\nThe 11-point P-R curves are drawn in Figure 3.\nAlthough being enhanced by pseudo-relevance feedback, the CLIR using either machine translation or dictionary-based query translation still does not perform as well as CLQS-based approach.\nStatistical t-test [13] is conducted to indicate whether the CLQS-based CLIR performs significantly better.\nPair-wise pvalues are shown in Table 4.\nClearly, CLQS significantly outperforms MT and DT without PRF as well as DT+PRF, but its superiority over MT+PRF is not significant.\nHowever, when combined with PRF, CLQS significant outperforms all the other methods.\nThis indicates the higher effectiveness of CLQS in related term identification by leveraging a wide spectrum of resources.\nFurthermore, post-translation expansion is capable of improving CLQS-based CLIR.\nThis is due to the fact that CLQS and pseudo-relevance feedback are leveraging different categories of resources, and both approaches can be complementary.\nTable 3.\nComparison of average precision (AP) on TREC 6 without and with post-translation expansion.\n(%) are the relative percentages over the monolingual IR performance\nFigure 3.\n11 points precision-recall on TREC6 CLIR dataset with pseudo relevance feedback\nTable 4.\nThe results of pair-wise significance t-test.\nHere pvalue <0.05 is considered statistically significant\n6.\nCONCLUSIONS\nIn this paper, we proposed a new approach to cross-lingual query suggestion by mining relevant queries in different languages from query logs.\nThe key solution to this problem is to learn a crosslingual query similarity measure by a discriminative model exploiting multiple monolingual and bilingual resources.\nThe model is trained based on the principle that cross-lingual similarity should best fit the monolingual similarity between one query and the other query's translation."},"lvl-3":{"kind":"string","value":"Cross-Lingual Query Suggestion Using Query Logs of Different Languages\nABSTRACT\nQuery suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs.\nPreviously, the suggested terms are mostly in the same language of the input query.\nIn this paper, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages.\nThis is very important to scenarios of cross-language information retrieval (CLIR) and cross-lingual keyword bidding for search engine advertisement.\nInstead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of the other language in the query log.\nImportant monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics, etc. are used to estimate the cross-lingual query similarity with a discriminative model.\nBenchmarks show that the resulting CLQS system significantly outperforms a baseline system based on dictionary-based query translation.\nBesides, the resulting CLQS is tested with French to English CLIR tasks on TREC collections.\nThe results demonstrate higher effectiveness than the traditional query translation methods.\n1.\nINTRODUCTION\nQuery suggestion is a functionality to help users of a search engine to better specify their information need, by narrowing down or expanding the scope of the search with synonymous queries and relevant queries, or by suggesting related queries that have been frequently used by other users.\nSearch engines, such as Google, Yahoo!, MSN, Ask Jeeves, all have implemented query suggestion functionality as a valuable addition to their core search method.\nIn addition, the same technology has been leveraged to recommend bidding terms to online advertiser in the pay-forperformance search market [12].\nQuery suggestion is closely related to query expansion which extends the original query with new search terms to narrow the scope of the search.\nBut different from query expansion, query suggestion aims to suggest full queries that have been formulated by users so that the query integrity and coherence are preserved in the suggested queries.\nTypical methods for query suggestion exploit query logs and document collections, by assuming that in the same period of time, many users share the same or similar interests, which can be expressed in different manners [12, 14, 26].\nBy suggesting the related and frequently used formulations, it is hoped that the new query can cover more relevant documents.\nHowever, all of the existing studies dealt with monolingual query suggestion and to our knowledge, there is no published study on cross-lingual query suggestion (CLQS).\nCLQS aims to suggest related queries but in a different language.\nIt has wide applications on World Wide Web: for cross-language search or for suggesting relevant bidding terms in a different language.\nCLQS can be approached as a query translation problem, i.e., to suggest the queries that are translations of the original query.\nDictionaries, large size of parallel corpora and existing commercial machine translation systems can be used for translation.\nHowever, these kinds of approaches usually rely on static knowledge and data.\nIt cannot effectively reflect the quickly shifting interests of Web users.\nMoreover, there are some problems with translated queries in target language.\nFor instance,\nthe translated terms can be reasonable translations, but they are not popularly used in the target language.\nFor example, the French query \"aliment biologique\" is translated into \"biologic food\" by Google translation tool2, yet the correct formulation nowadays should be \"organic food\".\nTherefore, there exist many mismatch cases between the translated terms and the really used terms in target language.\nThis mismatch makes the suggested terms in the target language ineffective.\nA natural thinking of solving this mismatch is to map the queries in the source language and the queries in the target language, by using the query log of a search engine.\nWe exploit the fact that the users of search engines in the same period of time have similar interests, and they submit queries on similar topics in different languages.\nAs a result, a query written in a source language likely has an equivalent in a query log in the target language.\nIn particular, if the user intends to perform CLIR, then original query is even more likely to have its correspondent included in the target language query log.\nTherefore, if a candidate for CLQS appears often in the query log, then it is more likely the appropriate one to be suggested.\nIn this paper, we propose a method of calculating the similarity between source language query and the target language query by exploiting, in addition to the translation information, a wide spectrum of bilingual and monolingual information, such as term co-occurrences, query logs with click-through data, etc. .\nA discriminative model is used to learn the cross-lingual query similarity based on a set of manually translated queries.\nThe model is trained by optimizing the cross-lingual similarity to best fit the monolingual similarity between one query and the other query's translation.\nBesides being benchmarked as an independent module, the resulting CLQS system is tested as a new means of query \"translation\" in CLIR task on TREC collections.\nThe results show that this new \"translation\" method is more effective than the traditional query translation method.\nThe remainder of this paper is organized as follows: Section 2 introduces the related work; Section 3 describes in detail the discriminative model for estimating cross-lingual query similarity; Section 4 presents a new CLIR approach using cross-lingual query suggestion as a bridge across language boundaries.\nSection 5 discusses the experiments and benchmarks; finally, the paper is concluded in Section 6.\n2.\nRELATED WORK\nMost approaches to CLIR perform a query translation followed by a monolingual IR.\nTypically, queries are translated either using a bilingual dictionary [22], a machine translation software [9] or a parallel corpus [20].\nDespite the various types of resources used, out-of-vocabulary (OOV) words and translation disambiguation are the two major bottlenecks for CLIR [20].\nIn [7, 27], OOV term translations are mined from the Web using a search engine.\nIn [17], bilingual knowledge is acquired based on anchor text analysis.\nIn addition, word co-occurrence statistics in the target language has been leveraged for translation disambiguation [3, 10, 11, 19].\nNevertheless, it is arguable that accurate query translation may not be necessary for CLIR.\nIndeed, in many cases, it is helpful to introduce words even if they are not direct translations of any query word, but are closely related to the meaning of the query.\nThis observation has led to the development of cross-lingual query expansion (CLQE) techniques [2, 16, 18].\n[2] reports the enhancement on CLIR by post-translation expansion.\n[16] develops a cross-lingual relevancy model by leveraging the crosslingual co-occurrence statistics in parallel texts.\n[18] makes performance comparison on multiple CLQE techniques, including pre-translation expansion and post-translation expansion.\nHowever, there is lack of a unified framework to combine the wide spectrum of resources and recent advances of mining techniques for CLQE.\nCLQS is different from CLQE in that it aims to suggest full queries that have been formulated by users in another language.\nAs CLQS exploits up-to-date query logs, it is expected that for most user queries, we can find common formulations on these topics in the query log in the target language.\nTherefore, CLQS also plays a role of adapting the original query formulation to the common formulations of similar topics in the target language.\nQuery logs have been successfully used for monolingual IR [8, 12, 15, 26], especially in monolingual query suggestions [12] and relating the semantically relevant terms for query expansion [8, 15].\nIn [1], the target language query log has been exploited to help query translation in CLIR.\n3.\nESTIMATING CROSS-LINGUAL QUERY SIMILARITY\n3.1 Discriminative Model for Estimating Cross-Lingual Query Similarity\n3.2 Monolingual Query Similarity Measure Based on Click-through Information\n3.3 Features Used for Learning Cross-Lingual Query Similarity Measure\n3.3.1 Bilingual Dictionary\n3.3.2 Parallel Corpora\n3.3.3 Online Mining for Related Queries\n3.3.4 Monolingual Query Suggestion\n3.4 Estimating Cross-lingual Query Similarity\n4.\nCLIR BASED ON CROSS-LINGUAL QUERY SUGGESTION\n5.\nPERFORMACNCE EVALUATION\n5.1 Data Resources\n5.2 Performance of Cross-lingual Query Suggestion\n5.3 CLIR Performance\n6.\nCONCLUSIONS\nIn this paper, we proposed a new approach to cross-lingual query suggestion by mining relevant queries in different languages from query logs.\nThe key solution to this problem is to learn a crosslingual query similarity measure by a discriminative model exploiting multiple monolingual and bilingual resources.\nThe model is trained based on the principle that cross-lingual similarity should best fit the monolingual similarity between one query and the other query's translation."},"lvl-4":{"kind":"string","value":"Cross-Lingual Query Suggestion Using Query Logs of Different Languages\nABSTRACT\nQuery suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs.\nPreviously, the suggested terms are mostly in the same language of the input query.\nIn this paper, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest similar or relevant queries in other languages.\nThis is very important to scenarios of cross-language information retrieval (CLIR) and cross-lingual keyword bidding for search engine advertisement.\nInstead of relying on existing query translation technologies for CLQS, we present an effective means to map the input query of one language to queries of the other language in the query log.\nImportant monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics, etc. are used to estimate the cross-lingual query similarity with a discriminative model.\nBenchmarks show that the resulting CLQS system significantly outperforms a baseline system based on dictionary-based query translation.\nBesides, the resulting CLQS is tested with French to English CLIR tasks on TREC collections.\nThe results demonstrate higher effectiveness than the traditional query translation methods.\n1.\nINTRODUCTION\nQuery suggestion is a functionality to help users of a search engine to better specify their information need, by narrowing down or expanding the scope of the search with synonymous queries and relevant queries, or by suggesting related queries that have been frequently used by other users.\nSearch engines, such as Google, Yahoo!, MSN, Ask Jeeves, all have implemented query suggestion functionality as a valuable addition to their core search method.\nQuery suggestion is closely related to query expansion which extends the original query with new search terms to narrow the scope of the search.\nBut different from query expansion, query suggestion aims to suggest full queries that have been formulated by users so that the query integrity and coherence are preserved in the suggested queries.\nBy suggesting the related and frequently used formulations, it is hoped that the new query can cover more relevant documents.\nHowever, all of the existing studies dealt with monolingual query suggestion and to our knowledge, there is no published study on cross-lingual query suggestion (CLQS).\nCLQS aims to suggest related queries but in a different language.\nIt has wide applications on World Wide Web: for cross-language search or for suggesting relevant bidding terms in a different language.\nCLQS can be approached as a query translation problem, i.e., to suggest the queries that are translations of the original query.\nDictionaries, large size of parallel corpora and existing commercial machine translation systems can be used for translation.\nHowever, these kinds of approaches usually rely on static knowledge and data.\nMoreover, there are some problems with translated queries in target language.\nFor instance,\nthe translated terms can be reasonable translations, but they are not popularly used in the target language.\nFor example, the French query \"aliment biologique\" is translated into \"biologic food\" by Google translation tool2, yet the correct formulation nowadays should be \"organic food\".\nTherefore, there exist many mismatch cases between the translated terms and the really used terms in target language.\nThis mismatch makes the suggested terms in the target language ineffective.\nA natural thinking of solving this mismatch is to map the queries in the source language and the queries in the target language, by using the query log of a search engine.\nWe exploit the fact that the users of search engines in the same period of time have similar interests, and they submit queries on similar topics in different languages.\nAs a result, a query written in a source language likely has an equivalent in a query log in the target language.\nIn particular, if the user intends to perform CLIR, then original query is even more likely to have its correspondent included in the target language query log.\nTherefore, if a candidate for CLQS appears often in the query log, then it is more likely the appropriate one to be suggested.\nIn this paper, we propose a method of calculating the similarity between source language query and the target language query by exploiting, in addition to the translation information, a wide spectrum of bilingual and monolingual information, such as term co-occurrences, query logs with click-through data, etc. .\nA discriminative model is used to learn the cross-lingual query similarity based on a set of manually translated queries.\nThe model is trained by optimizing the cross-lingual similarity to best fit the monolingual similarity between one query and the other query's translation.\nBesides being benchmarked as an independent module, the resulting CLQS system is tested as a new means of query \"translation\" in CLIR task on TREC collections.\nThe results show that this new \"translation\" method is more effective than the traditional query translation method.\n2.\nRELATED WORK\nMost approaches to CLIR perform a query translation followed by a monolingual IR.\nTypically, queries are translated either using a bilingual dictionary [22], a machine translation software [9] or a parallel corpus [20].\nDespite the various types of resources used, out-of-vocabulary (OOV) words and translation disambiguation are the two major bottlenecks for CLIR [20].\nIn [7, 27], OOV term translations are mined from the Web using a search engine.\nIn addition, word co-occurrence statistics in the target language has been leveraged for translation disambiguation [3, 10, 11, 19].\nNevertheless, it is arguable that accurate query translation may not be necessary for CLIR.\nIndeed, in many cases, it is helpful to introduce words even if they are not direct translations of any query word, but are closely related to the meaning of the query.\nThis observation has led to the development of cross-lingual query expansion (CLQE) techniques [2, 16, 18].\n[2] reports the enhancement on CLIR by post-translation expansion.\n[16] develops a cross-lingual relevancy model by leveraging the crosslingual co-occurrence statistics in parallel texts.\nCLQS is different from CLQE in that it aims to suggest full queries that have been formulated by users in another language.\nAs CLQS exploits up-to-date query logs, it is expected that for most user queries, we can find common formulations on these topics in the query log in the target language.\nTherefore, CLQS also plays a role of adapting the original query formulation to the common formulations of similar topics in the target language.\nQuery logs have been successfully used for monolingual IR [8, 12, 15, 26], especially in monolingual query suggestions [12] and relating the semantically relevant terms for query expansion [8, 15].\nIn [1], the target language query log has been exploited to help query translation in CLIR.\n6.\nCONCLUSIONS\nIn this paper, we proposed a new approach to cross-lingual query suggestion by mining relevant queries in different languages from query logs.\nThe key solution to this problem is to learn a crosslingual query similarity measure by a discriminative model exploiting multiple monolingual and bilingual resources.\nThe model is trained based on the principle that cross-lingual similarity should best fit the monolingual similarity between one query and the other query's translation."}}},{"rowIdx":91,"cells":{"id":{"kind":"string","value":"H-44"},"title":{"kind":"string","value":"A Time Machine for Text Search"},"abstract":{"kind":"string","value":"Text search over temporally versioned document collections such as web archives has received little attention as a research problem. As a consequence, there is no scalable and principled solution to search such a collection as of a specified time t. In this work, we address this shortcoming and propose an efficient solution for time-travel text search by extending the inverted file index to make it ready for temporal search. We introduce approximate temporal coalescing as a tunable method to reduce the index size without significantly affecting the quality of results. In order to further improve the performance of time-travel queries, we introduce two principled techniques to trade off index size for its performance. These techniques can be formulated as optimization problems that can be solved to near-optimality. Finally, our approach is evaluated in a comprehensive series of experiments on two large-scale real-world datasets. Results unequivocally show that our methods make it possible to build an efficient time machine scalable to large versioned text collections."},"keyphrases":{"kind":"list like","value":["time machin","text search","version document collect","web archiv","time-travel text search","invert file index","tempor search","approxim tempor coalesc","collabor author environ","timestamp inform feed","document-content overlap","index rang-base valu","open sourc search-engin nutch","static indexprun techniqu","valid time-interv","sublist materi","tempor text index"],"string":"[\n \"time machin\",\n \"text search\",\n \"version document collect\",\n \"web archiv\",\n \"time-travel text search\",\n \"invert file index\",\n \"tempor search\",\n \"approxim tempor coalesc\",\n \"collabor author environ\",\n \"timestamp inform feed\",\n \"document-content overlap\",\n \"index rang-base valu\",\n \"open sourc search-engin nutch\",\n \"static indexprun techniqu\",\n \"valid time-interv\",\n \"sublist materi\",\n \"tempor text index\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","P","P","P","P","P","U","U","U","M","U","M","U","U","R"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"U\",\n \"U\",\n \"U\",\n \"M\",\n \"U\",\n \"M\",\n \"U\",\n \"U\",\n \"R\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"A Time Machine for Text Search Klaus Berberich Srikanta Bedathur Thomas Neumann Gerhard Weikum Max-Planck Institute for Informatics Saarbr¨ucken, Germany {kberberi, bedathur, neumann, weikum}@mpi-inf.mpg.de ABSTRACT Text search over temporally versioned document collections such as web archives has received little attention as a research problem.\nAs a consequence, there is no scalable and principled solution to search such a collection as of a specified time t.\nIn this work, we address this shortcoming and propose an efficient solution for time-travel text search by extending the inverted file index to make it ready for temporal search.\nWe introduce approximate temporal coalescing as a tunable method to reduce the index size without significantly affecting the quality of results.\nIn order to further improve the performance of time-travel queries, we introduce two principled techniques to trade off index size for its performance.\nThese techniques can be formulated as optimization problems that can be solved to near-optimality.\nFinally, our approach is evaluated in a comprehensive series of experiments on two large-scale real-world datasets.\nResults unequivocally show that our methods make it possible to build an efficient time machine scalable to large versioned text collections.\nCategories and Subject Descriptors H.3.1 [Content Analysis and Indexing]: Indexing methods; H.3.3 [Information Search and Retrieval]: Retrieval models, Search process General Terms Algorithms, Experimentation, Performance 1.\nINTRODUCTION In this work we address time-travel text search over temporally versioned document collections.\nGiven a keyword query q and a time t our goal is to identify and rank relevant documents as if the collection was in its state as of time t.\nAn increasing number of such versioned document collections is available today including web archives, collaborative authoring environments like Wikis, or timestamped information feeds.\nText search on these collections, however, is mostly time-ignorant: while the searched collection changes over time, often only the most recent version of a documents is indexed, or, versions are indexed independently and treated as separate documents.\nEven worse, for some collections, in particular web archives like the Internet Archive [18], a comprehensive text-search functionality is often completely missing.\nTime-travel text search, as we develop it in this paper, is a crucial tool to explore these collections and to unfold their full potential as the following example demonstrates.\nFor a documentary about a past political scandal, a journalist needs to research early opinions and statements made by the involved politicians.\nSending an appropriate query to a major web search-engine, the majority of returned results contains only recent coverage, since many of the early web pages have disappeared and are only preserved in web archives.\nIf the query could be enriched with a time point, say August 20th 2003 as the day after the scandal got revealed, and be issued against a web archive, only pages that existed specifically at that time could be retrieved thus better satisfying the journalist``s information need.\nDocument collections like the Web or Wikipedia [32], as we target them here, are already large if only a single snapshot is considered.\nLooking at their evolutionary history, we are faced with even larger data volumes.\nAs a consequence, na¨ıve approaches to time-travel text search fail, and viable approaches must scale-up well to such large data volumes.\nThis paper presents an efficient solution to time-travel text search by making the following key contributions: 1.\nThe popular well-studied inverted file index [35] is transparently extended to enable time-travel text search.\n2.\nTemporal coalescing is introduced to avoid an indexsize explosion while keeping results highly accurate.\n3.\nWe develop two sublist materialization techniques to improve index performance that allow trading off space vs. performance.\n4.\nIn a comprehensive experimental evaluation our approach is evaluated on the English Wikipedia and parts of the Internet Archive as two large-scale real-world datasets with versioned documents.\nThe remainder of this paper is organized as follows.\nThe presented work is put in context with related work in Section 2.\nWe delineate our model of a temporally versioned document collection in Section 3.\nWe present our time-travel inverted index in Section 4.\nBuilding on it, temporal coalescing is described in Section 5.\nIn Section 6 we describe principled techniques to improve index performance, before presenting the results of our experimental evaluation in Section 7.\n2.\nRELATED WORK We can classify the related work mainly into the following two categories: (i) methods that deal explicitly with collections of versioned documents or temporal databases, and (ii) methods for reducing the index size by exploiting either the document-content overlap or by pruning portions of the index.\nWe briefly review work under these categories here.\nTo the best of our knowledge, there is very little prior work dealing with historical search over temporally versioned documents.\nAnick and Flynn [3], while pioneering this research, describe a help-desk system that supports historical queries.\nAccess costs are optimized for accesses to the most recent versions and increase as one moves farther into the past.\nBurrows and Hisgen [10], in a patent description, delineate a method for indexing range-based values and mention its potential use for searching based on dates associated with documents.\nRecent work by Nørv˚ag and Nybø [25] and their earlier proposals concentrate on the relatively simpler problem of supporting text-containment queries only and neglect the relevance scoring of results.\nStack [29] reports practical experiences made when adapting the open source search-engine Nutch to search web archives.\nThis adaptation, however, does not provide the intended time-travel text search functionality.\nIn contrast, research in temporal databases has produced several index structures tailored for time-evolving databases; a comprehensive overview of the state-of-art is available in [28].\nUnlike the inverted file index, their applicability to text search is not well understood.\nMoving on to the second category of related work, Broder et al. [8] describe a technique that exploits large content overlaps between documents to achieve a reduction in index size.\nTheir technique makes strong assumptions about the structure of document overlaps rendering it inapplicable to our context.\nMore recent approaches by Hersovici et al. [17] and Zhang and Suel [34] exploit arbitrary content overlaps between documents to reduce index size.\nNone of the approaches, however, considers time explicitly or provides the desired time-travel text search functionality.\nStatic indexpruning techniques [11, 12] aim to reduce the effective index size, by removing portions of the index that are expected to have low impact on the query result.\nThey also do not consider temporal aspects of documents, and thus are technically quite different from our proposal despite having a shared goal of index-size reduction.\nIt should be noted that index-pruning techniques can be adapted to work along with the temporal text index we propose here.\n3.\nMODEL In the present work, we deal with a temporally versioned document collection D that is modeled as described in the following.\nEach document d ∈ D is a sequence of its versions d = dt1 , dt2 , ... Each version dti has an associated timestamp ti reflecting when the version was created.\nEach version is a vector of searchable terms or features.\nAny modification to a document version results in the insertion of a new version with corresponding timestamp.\nWe employ a discrete definition of time, so that timestamps are non-negative integers.\nThe deletion of a document at time ti, i.e., its disappearance from the current state of the collection, is modeled as the insertion of a special tombstone version ⊥.\nThe validity time-interval val(dti ) of a version dti is [ti, ti+1), if a newer version with associated timestamp ti+1 exists, and [ti, now) otherwise where now points to the greatest possible value of a timestamp (i.e., ∀t : t < now).\nPutting all this together, we define the state Dt of the collection at time t (i.e., the set of versions valid at t that are not deletions) as Dt = [ d∈D {dti ∈ d | t ∈ val(dti ) ∧ dti = ⊥} .\nAs mentioned earlier, we want to enrich a keyword query q with a timestamp t, so that q be evaluated over Dt , i.e., the state of the collection at time t.\nThe enriched time-travel query is written as q t for brevity.\nAs a retrieval model in this work we adopt Okapi BM25 [27], but note that the proposed techniques are not dependent on this choice and are applicable to other retrieval models like tf-idf [4] or language models [26] as well.\nFor our considered setting, we slightly adapt Okapi BM25 as w(q t , dti ) = X v∈q wtf (v, dti ) · widf (v, t) .\nIn the above formula, the relevance w(q t , dti ) of a document version dti to the time-travel query q t is defined.\nWe reiterate that q t is evaluated over Dt so that only the version dti valid at time t is considered.\nThe first factor wtf (v, dti ) in the summation, further referred to as the tfscore is defined as wtf (v, dti ) = (k1 + 1) · tf(v, dti ) k1 · ((1 − b) + b · dl(d ti ) avdl(ti) ) + tf(v, dti ) .\nIt considers the plain term frequency tf(v, dti ) of term v in version dti normalizing it, taking into account both the length dl(dti ) of the version and the average document length avdl(ti) in the collection at time ti.\nThe length-normalization parameter b and the tf-saturation parameter k1 are inherited from the original Okapi BM25 and are commonly set to values 1.2 and 0.75 respectively.\nThe second factor widf (v, t), which we refer to as the idf-score in the remainder, conveys the inverse document frequency of term v in the collection at time t and is defined as widf (v, t) = log N(t) − df(v, t) + 0.5 df(v, t) + 0.5 where N(t) = |Dt | is the collection size at time t and df(v, t) gives the number of documents in the collection that contain the term v at time t.\nWhile the idf-score depends on the whole corpus as of the query time t, the tf-score is specific to each version.\n4.\nTIME-TRAVELINVERTEDFILEINDEX The inverted file index is a standard technique for text indexing, deployed in many systems.\nIn this section, we briefly review this technique and present our extensions to the inverted file index that make it ready for time-travel text search.\n4.1 Inverted File Index An inverted file index consists of a vocabulary, commonly organized as a B+-Tree, that maps each term to its idfscore and inverted list.\nThe index list Lv belonging to term v contains postings of the form ( d, p ) where d is a document-identifier and p is the so-called payload.\nThe payload p contains information about the term frequency of v in d, but may also include positional information about where the term appears in the document.\nThe sort-order of index lists depends on which queries are to be supported efficiently.\nFor Boolean queries it is favorable to sort index lists in document-order.\nFrequencyorder and impact-order sorted index lists are beneficial for ranked queries and enable optimized query processing that stops early after having identified the k most relevant documents [1, 2, 9, 15, 31].\nA variety of compression techniques, such as encoding document identifiers more compactly, have been proposed [33, 35] to reduce the size of index lists.\nFor an excellent recent survey about inverted file indexes we refer to [35].\n4.2 Time-Travel Inverted File Index In order to prepare an inverted file index for time travel we extend both inverted lists and the vocabulary structure by explicitly incorporating temporal information.\nThe main idea for inverted lists is that we include a validity timeinterval [tb, te) in postings to denote when the payload information was valid.\nThe postings in our time-travel inverted file index are thus of the form ( d, p, [tb, te) ) where d and p are defined as in the standard inverted file index above and [tb, te) is the validity time-interval.\nAs a concrete example, in our implementation, for a version dti having the Okapi BM25 tf-score wtf (v, dti ) for term v, the index list Lv contains the posting ( d, wtf (v, dti ), [ti, ti+1) ) .\nSimilarly, the extended vocabulary structure maintains for each term a time-series of idf-scores organized as a B+Tree.\nUnlike the tf-score, the idf-score of every term could vary with every change in the corpus.\nTherefore, we take a simplified approach to idf-score maintenance, by computing idf-scores for all terms in the corpus at specific (possibly periodic) times.\n4.3 Query Processing During processing of a time-travel query q t , for each query term the corresponding idf-score valid at time t is retrieved from the extended vocabulary.\nThen, index lists are sequentially read from disk, thereby accumulating the information contained in the postings.\nWe transparently extend the sequential reading, which is - to the best of our knowledgecommon to all query processing techniques on inverted file indexes, thus making them suitable for time-travel queryprocessing.\nTo this end, sequential reading is extended by skipping all postings whose validity time-interval does not contain t (i.e., t ∈ [tb, te)).\nWhether a posting can be skipped can only be decided after the posting has been transferred from disk into memory and therefore still incurs significant I/O cost.\nAs a remedy, we propose index organization techniques in Section 6 that aim to reduce the I/O overhead significantly.\nWe note that our proposed extension of the inverted file index makes no assumptions about the sort-order of index lists.\nAs a consequence, existing query-processing techniques and most optimizations (e.g., compression techniques) remain equally applicable.\n5.\nTEMPORAL COALESCING If we employ the time-travel inverted index, as described in the previous section, to a versioned document collection, we obtain one posting per term per document version.\nFor frequent terms and large highly-dynamic collections, this time score non-coalesced coalesced Figure 1: Approximate Temporal Coalescing leads to extremely long index lists with very poor queryprocessing performance.\nThe approximate temporal coalescing technique that we propose in this section counters this blowup in index-list size.\nIt builds on the observation that most changes in a versioned document collection are minor, leaving large parts of the document untouched.\nAs a consequence, the payload of many postings belonging to temporally adjacent versions will differ only slightly or not at all.\nApproximate temporal coalescing reduces the number of postings in an index list by merging such a sequence of postings that have almost equal payloads, while keeping the maximal error bounded.\nThis idea is illustrated in Figure 1, which plots non-coalesced and coalesced scores of postings belonging to a single document.\nApproximate temporal coalescing is greatly effective given such fluctuating payloads and reduces the number of postings from 9 to 3 in the example.\nThe notion of temporal coalescing was originally introduced in temporal database research by B¨ohlen et al. [6], where the simpler problem of coalescing only equal information was considered.\nWe next formally state the problem dealt with in approximate temporal coalescing, and discuss the computation of optimal and approximate solutions.\nNote that the technique is applied to each index list separately, so that the following explanations assume a fixed term v and index list Lv.\nAs an input we are given a sequence of temporally adjacent postings I = ( d, pi, [ti, ti+1) ), ... , ( d, pn−1, [tn−1, tn)) ) .\nEach sequence represents a contiguous time period during which the term was present in a single document d.\nIf a term disappears from d but reappears later, we obtain multiple input sequences that are dealt with separately.\nWe seek to generate the minimal length output sequence of postings O = ( d, pj, [tj, tj+1) ), ... , ( d, pm−1, [tm−1, tm)) ) , that adheres to the following constraints: First, O and I must cover the same time-range, i.e., ti = tj and tn = tm.\nSecond, when coalescing a subsequence of postings of the input into a single posting of the output, we want the approximation error to be below a threshold .\nIn other words, if (d, pi, [ti, ti+1)) and (d, pj, [tj, tj+1)) are postings of I and O respectively, then the following must hold for a chosen error function and a threshold : tj ≤ ti ∧ ti+1 ≤ tj+1 ⇒ error(pi, pj) ≤ .\nIn this paper, as an error function we employ the relative error between payloads (i.e., tf-scores) of a document in I and O, defined as: errrel(pi, pj) = |pi − pj| / |pi| .\nFinding an optimal output sequence of postings can be cast into finding a piecewise-constant representation for the points (ti, pi) that uses a minimal number of segments while retaining the above approximation guarantee.\nSimilar problems occur in time-series segmentation [21, 30] and histogram construction [19, 20].\nTypically dynamic programming is applied to obtain an optimal solution in O(n2 m∗ ) [20, 30] time with m∗ being the number of segments in an optimal sequence.\nIn our setting, as a key difference, only a guarantee on the local error is retained - in contrast to a guarantee on the global error in the aforementioned settings.\nExploiting this fact, an optimal solution is computable by means of induction [24] in O(n2 ) time.\nDetails of the optimal algorithm are omitted here but can be found in the accompanying technical report [5].\nThe quadratic complexity of the optimal algorithm makes it inappropriate for the large datasets encountered in this work.\nAs an alternative, we introduce a linear-time approximate algorithm that is based on the sliding-window algorithm given in [21].\nThis algorithm produces nearly-optimal output sequences that retain the bound on the relative error, but possibly require a few additional segments more than an optimal solution.\nAlgorithm 1 Temporal Coalescing (Approximate) 1: I = ( d, pi, [ti, ti+1) ), ... O = 2: pmin = pi pmax = pi p = pi tb = ti te = ti+1 3: for ( d, pj, [tj, tj+1) ) ∈ I do 4: pmin = min( pmin, pj ) pmax = max( pmax, pj ) 5: p = optrep(pmin, pmax) 6: if errrel(pmin, p ) ≤ ∧ errrel(pmax, p ) ≤ then 7: pmin = pmin pmax = pmax p = p te = tj+1 8: else 9: O = O ∪ ( d, p, [tb, te) ) 10: pmin = pj pmax = pj p = pj tb = tj te = tj+1 11: end if 12: end for 13: O = O ∪ ( d, p, [tb, te) ) Algorithm 1 makes one pass over the input sequence I.\nWhile doing so, it coalesces sequences of postings having maximal length.\nThe optimal representative for a sequence of postings depends only on their minimal and maximal payload (pmin and pmax) and can be looked up using optrep in O(1) (see [16] for details).\nWhen reading the next posting, the algorithm tries to add it to the current sequence of postings.\nIt computes the hypothetical new representative p and checks whether it would retain the approximation guarantee.\nIf this test fails, a coalesced posting bearing the old representative is added to the output sequence O and, following that, the bookkeeping is reinitialized.\nThe time complexity of the algorithm is in O(n).\nNote that, since we make no assumptions about the sort order of index lists, temporal-coalescing algorithms have an additional preprocessing cost in O(|Lv| log |Lv|) for sorting the index list and chopping it up into subsequences for each document.\n6.\nSUBLIST MATERIALIZATION Efficiency of processing a query q t on our time-travel inverted index is influenced adversely by the wasted I/O due to read but skipped postings.\nTemporal coalescing implicitly addresses this problem by reducing the overall index list size, but still a significant overhead remains.\nIn this section, we tackle this problem by proposing the idea of materializing sublists each of which corresponds to a contiguous subinterval of time spanned by the full index.\nEach of these sublists contains all coalesced postings that overlap with the corresponding time interval of the sublist.\nNote that all those postings whose validity time-interval spans across the temporal boundaries of several sublists are replicated in each of the spanned sublists.\nThus, in order to process the query q t time t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 d1 d2 d3 document 1 2 3 4 5 6 7 8 9 10 Figure 2: Sublist Materialization it is sufficient to scan any materialized sublist whose timeinterval contains t.\nWe illustrate the idea of sublist materialization using an example shown in Figure 2.\nThe index list Lv visualized in the figure contains a total of 10 postings from three documents d1, d2, and d3.\nFor ease of description, we have numbered boundaries of validity time-intervals, in increasing time-order, as t1, ... , t10 and numbered the postings themselves as 1, ... , 10.\nNow, consider the processing of a query q t with t ∈ [t1, t2) using this inverted list.\nAlthough only three postings (postings 1, 5 and 8) are valid at time t, the whole inverted list has to be read in the worst case.\nSuppose that we split the time axis of the list at time t2, forming two sublists with postings {1, 5, 8} and {2, 3, 4, 5, 6, 7, 8, 9, 10} respectively.\nThen, we can process the above query with optimal cost by reading only those postings that existed at this t.\nAt a first glance, it may seem counterintuitive to reduce index size in the first step (using temporal coalescing), and then to increase it again using the sublist materialization techniques presented in this section.\nHowever, we reiterate that our main objective is to improve the efficiency of processing queries, not to reduce the index size alone.\nThe use of temporal coalescing improves the performance by reducing the index size, while the sublist materialization improves performance by judiciously replicating entries.\nFurther, the two techniques, can be applied separately and are independent.\nIf applied in conjunction, though, there is a synergetic effect - sublists that are materialized from a temporally coalesced index are generally smaller.\nWe employ the notation Lv : [ti, tj) to refer to the materialized sublist for the time interval [ti, tj), that is formally defined as, Lv : [ti, tj) = {( d, p, [tb, te) ) ∈ Lv | tb < tj ∧ te > ti} .\nTo aid the presentation in the rest of the paper, we first provide some definitions.\nLet T = t1 ... tn be the sorted sequence of all unique time-interval boundaries of an inverted list Lv.\nThen we define E = { [ti, ti+1) | 1 ≤ i < n} to be the set of elementary time intervals.\nWe refer to the set of time intervals for which sublists are materialized as M ⊆ { [ti, tj) | 1 ≤ i < j ≤ n } , and demand ∀ t ∈ [t1, tn) ∃ m ∈ M : t ∈ m , i.e., the time intervals in M must completely cover the time interval [t1, tn), so that time-travel queries q t for all t ∈ [t1, tn) can be processed.\nWe also assume that intervals in M are disjoint.\nWe can make this assumption without ruling out any optimal solution with regard to space or performance defined below.\nThe space required for the materialization of sublists in a set M is defined as S( M ) = X m∈M |Lv : m| , i.e., the total length of all lists in M. Given a set M, we let π( [ti, ti+1) ) = [tj, tk) ∈ M : [ti, ti+1) ⊆ [tj, tk) denote the time interval that is used to process queries q t with t ∈ [ti, ti+1).\nThe performance of processing queries q t for t ∈ [ti, ti+1) inversely depends on its processing cost PC( [ti, ti+1) ) = |Lv : π( [ti, ti+1) )| , which is assumed to be proportional to the length of the list Lv : π( [ti, ti+1) ).\nThus, in order to optimize the performance of processing queries we minimize their processing costs.\n6.1 Performance/Space-Optimal Approaches One strategy to eliminate the problem of skipped postings is to eagerly materialize sublists for all elementary time intervals, i.e., to choose M = E.\nIn doing so, for every query q t only postings valid at time t are read and thus the best possible performance is achieved.\nTherefore, we will refer to this approach as Popt in the remainder.\nThe initial approach described above that keeps only the full list Lv and thus picks M = { [t1, tn) } is referred to as Sopt in the remainder.\nThis approach requires minimal space, since it keeps each posting exactly once.\nPopt and Sopt are extremes: the former provides the best possible performance but is not space-efficient, the latter requires minimal space but does not provide good performance.\nThe two approaches presented in the rest of this section allow mutually trading off space and performance and can thus be thought of as means to explore the configuration spectrum between the Popt and the Sopt approach.\n6.2 Performance-Guarantee Approach The Popt approach clearly wastes a lot of space materializing many nearly-identical sublists.\nIn the example illustrated in Figure 2 materialized sublists for [t1, t2) and [t2, t3) differ only by one posting.\nIf the sublist for [t1, t3) was materialized instead, one could save significant space while incurring only an overhead of one skipped posting for all t ∈ [t1, t3).\nThe technique presented next is driven by the idea that significant space savings over Popt are achievable, if an upper-bounded loss on the performance can be tolerated, or to put it differently, if a performance guarantee relative to the optimum is to be retained.\nIn detail, the technique, which we refer to as PG (Performance Guarantee) in the remainder, finds a set M that has minimal required space, but guarantees for any elementary time interval [ti, ti+1) (and thus for any query q t with t ∈ [ti, ti+1)) that performance is worse than optimal by at most a factor of γ ≥ 1.\nFormally, this problem can be stated as argmin M S( M ) s.t. ∀ [ti, ti+1) ∈ E : PC( [ti, ti+1) ) ≤ γ · |Lv : [ti, ti+1)| .\nAn optimal solution to the problem can be computed by means of induction using the recurrence C( [t1, tk+1) ) = min {C( [t1, tj) ) + |Lv : [tj, tk+1)| | 1 ≤ j ≤ k ∧ condition} , where C( [t1, tj) ) is the optimal cost (i.e., the space required) for the prefix subproblem { [ti, ti+1) ∈ E | [ti, ti+1) ⊆ [t1, tj) } and condition stands for ∀ [ti, ti+1) ∈ E : [ti, ti+1) ⊆ [tj, tk+1) ⇒ |Lv : [tj, tk+1)| ≤ γ · |Lv : [ti, ti+1)| .\nIntuitively, the recurrence states that an optimal solution for [t1, tk+1) be combined from an optimal solution to a prefix subproblem C( [t1, tj) ) and a time interval [tj, tk+1) that can be materialized without violating the performance guarantee.\nPseudocode of the algorithm is omitted for space reasons, but can be found in the accompanying technical report [5].\nThe time complexity of the algorithm is in O(n2 ) - for each prefix subproblem the above recurrence must be evaluated, which is possible in linear time if list sizes |L : [ti, tj)| are precomputed.\nThe space complexity is in O(n2 ) - the cost of keeping the precomputed sublist lengths and memoizing optimal solutions to prefix subproblems.\n6.3 Space-Bound Approach So far we considered the problem of materializing sublists that give a guarantee on performance while requiring minimal space.\nIn many situations, though, the storage space is at a premium and the aim would be to materialize a set of sublists that optimizes expected performance while not exceeding a given space limit.\nThe technique presented next, which is named SB, tackles this very problem.\nThe space restriction is modeled by means of a user-specified parameter κ ≥ 1 that limits the maximum allowed blowup in index size from the space-optimal solution provided by Sopt.\nThe SB technique seeks to find a set M that adheres to this space limit but minimizes the expected processing cost (and thus optimizes the expected performance).\nIn the definition of the expected processing cost, P( [ti, ti+1) ) denotes the probability of a query time-point being in [ti, ti+1).\nFormally, this space-bound sublist-materialization problem can be stated as argmin M X [ti, ti+1) ∈ E P( [ti, ti+1) ) · PC( [ti, ti+1) ) s.t. X m∈M |Lv : m| ≤ κ |Lv| .\nThe problem can be solved by using dynamic programming over an increasing number of time intervals: At each time interval in E the algorithms decides whether to start a new materialization time-interval, using the known best materialization decision from the previous time intervals, and keeping track of the required space consumption for materialization.\nA detailed description of the algorithm is omitted here, but can be found in the accompanying technical report [5].\nUnfortunately, the algorithm has time complexity in O(n3 |Lv|) and its space complexity is in O(n2 |Lv|), which is not practical for large data sets.\nWe obtain an approximate solution to the problem using simulated annealing [22, 23].\nSimulated annealing takes a fixed number R of rounds to explore the solution space.\nIn each round a random successor of the current solution is looked at.\nIf the successor does not adhere to the space limit, it is always rejected (i.e., the current solution is kept).\nA successor adhering to the space limit is always accepted if it achieves lower expected processing cost than the current solution.\nIf it achieves higher expected processing cost, it is randomly accepted with probability e−∆/r where ∆ is the increase in expected processing cost and R ≥ r ≥ 1 denotes the number of remaining rounds.\nIn addition, throughout all rounds, the method keeps track of the best solution seen so far.\nThe solution space for the problem at hand can be efficiently explored.\nAs we argued above, we solely have to look at sets M that completely cover the time interval [t1, tn) and do not contain overlapping time intervals.\nWe represent such a set M as an array of n boolean variables b1 ... bn that convey the boundaries of time intervals in the set.\nNote that b1 and bn are always set to true.\nInitially, all n − 2 intermediate variables assume false, which corresponds to the set M = { [t1, tn) }.\nA random successor can now be easily generated by switching the value of one of the n − 2 intermediate variables.\nThe time complexity of the method is in O(n2 ) - the expected processing cost must be computed in each round.\nIts space complexity is in O(n) - for keeping the n boolean variables.\nAs a side remark note that for κ = 1.0 the SB method does not necessarily produce the solution that is obtained from Sopt, but may produce a solution that requires the same amount of space while achieving better expected performance.\n7.\nEXPERIMENTAL EVALUATION We conducted a comprehensive series of experiments on two real-world datasets to evaluate the techniques proposed in this paper.\n7.1 Setup and Datasets The techniques described in this paper were implemented in a prototype system using Java JDK 1.5.\nAll experiments described below were run on a single SUN V40z machine having four AMD Opteron CPUs, 16GB RAM, a large network-attached RAID-5 disk array, and running Microsoft Windows Server 2003.\nAll data and indexes are kept in an Oracle 10g database that runs on the same machine.\nFor our experiments we used two different datasets.\nThe English Wikipedia revision history (referred to as WIKI in the remainder) is available for free download as a single XML file.\nThis large dataset, totaling 0.7 TBytes, contains the full editing history of the English Wikipedia from January 2001 to December 2005 (the time of our download).\nWe indexed all encyclopedia articles excluding versions that were marked as the result of a minor edit (e.g., the correction of spelling errors etc.).\nThis yielded a total of 892,255 documents with 13,976,915 versions having a mean (µ) of 15.67 versions per document at standard deviation (σ) of 59.18.\nWe built a time-travel query workload using the query log temporarily made available recently by AOL Research as follows - we first extracted the 300 most frequent keyword queries that yielded a result click on a Wikipedia article (for e.g., french revolution, hurricane season 2005, da vinci code etc.).\nThe thus extracted queries contained a total of 422 distinct terms.\nFor each extracted query, we randomly picked a time point for each month covered by the dataset.\nThis resulted in a total of 18, 000 (= 300 × 60) time-travel queries.\nThe second dataset used in our experiments was based on a subset of the European Archive [13], containing weekly crawls of 11 .\ngov.uk websites throughout the years 2004 and 2005 amounting close to 2 TBytes of raw data.\nWe filtered out documents not belonging to MIME-types text/plain and text/html, to obtain a dataset that totals 0.4 TBytes and which we refer to as UKGOV in rest of the paper.\nThis included a total of 502,617 documents with 8,687,108 versions (µ = 17.28 and σ = 13.79).\nWe built a corresponding query workload as mentioned before, this time choosing keyword queries that led to a site in the .\ngov.uk domain (e.g., minimum wage, inheritance tax , citizenship ceremony dates etc.), and randomly sampling a time point for every month within the two year period spanned by the dataset.\nThus, we obtained a total of 7,200 (= 300 × 24) time-travel queries for the UKGOV dataset.\nIn total 522 terms appear in the extracted queries.\nThe collection statistics (i.e., N and avdl) and term statistics (i.e., DF) were computed at monthly granularity for both datasets.\n7.2 Impact of Temporal Coalescing Our first set of experiments is aimed at evaluating the approximate temporal coalescing technique, described in Section 5, in terms of index-size reduction and its effect on the result quality.\nFor both the WIKI and UKGOV datasets, we compare temporally coalesced indexes for different values of the error threshold computed using Algorithm 1 with the non-coalesced index as a baseline.\nWIKI UKGOV # Postings Ratio # Postings Ratio - 8,647,996,223 100.00% 7,888,560,482 100.00% 0.00 7,769,776,831 89.84% 2,926,731,708 37.10% 0.01 1,616,014,825 18.69% 744,438,831 9.44% 0.05 556,204,068 6.43% 259,947,199 3.30% 0.10 379,962,802 4.39% 187,387,342 2.38% 0.25 252,581,230 2.92% 158,107,198 2.00% 0.50 203,269,464 2.35% 155,434,617 1.97% Table 1: Index sizes for non-coalesced index (-) and coalesced indexes for different values of Table 1 summarizes the index sizes measured as the total number of postings.\nAs these results demonstrate, approximate temporal coalescing is highly effective in reducing index size.\nEven a small threshold value, e.g. = 0.01, has a considerable effect by reducing the index size almost by an order of magnitude.\nNote that on the UKGOV dataset, even accurate coalescing ( = 0) manages to reduce the index size to less than 38% of the original size.\nIndex size continues to reduce on both datasets, as we increase the value of .\nHow does the reduction in index size affect the query results?\nIn order to evaluate this aspect, we compared the top-k results computed using a coalesced index against the ground-truth result obtained from the original index, for different cutoff levels k. Let Gk and Ck be the top-k documents from the ground-truth result and from the coalesced index respectively.\nWe used the following two measures for comparison: (i) Relative Recall at cutoff level k (RR@k), that measures the overlap between Gk and Ck, which ranges in [0, 1] and is defined as RR@k = |Gk ∩ Ck|/k .\n(ii) Kendall``s τ (see [7, 14] for a detailed definition) at cutoff level k (KT@k), measuring the agreement between two results in the relative order of items in Gk ∩ Ck, with value 1 (or -1) indicating total agreement (or disagreement).\nFigure 3 plots, for cutoff levels 10 and 100, the mean of RR@k and KT@k along with 5% and 95% percentiles, for different values of the threshold starting from 0.01.\nNote that for = 0, results coincide with those obtained by the original index, and hence are omitted from the graph.\nIt is reassuring to see from these results that approximate temporal coalescing induces minimal disruption to the query results, since RR@k and KT@k are within reasonable limits.\nFor = 0.01, the smallest value of in our experiments, RR@100 for WIKI is 0.98 indicating that the results are -1 -0.5 0 0.5 1 ε 0.01 0.05 0.10 0.25 0.50 Relative Recall @ 10 (WIKI) Kendall's τ @ 10 (WIKI) Relative Recall @ 10 (UKGOV) Kendall's τ @ 10 (UKGOV) (a) @10 -1 -0.5 0 0.5 1 ε 0.01 0.05 0.10 0.25 0.50 Relative Recall @ 100 (WIKI) Kendall's τ @ 100 (WIKI) Relative Recall @ 100 (UKGOV) Kendall's τ @ 100 (UKGOV) (b) @100 Figure 3: Relative recall and Kendall``s τ observed on coalesced indexes for different values of almost indistinguishable from those obtained through the original index.\nEven the relative order of these common results is quite high, as the mean KT@100 is close to 0.95.\nFor the extreme value of = 0.5, which results in an index size of just 2.35% of the original, the RR@100 and KT@100 are about 0.8 and 0.6 respectively.\nOn the relatively less dynamic UKGOV dataset (as can be seen from the σ values above), results were even better, with high values of RR and KT seen throughout the spectrum of values for both cutoff values.\n7.3 Sublist Materialization We now turn our attention towards evaluating the sublist materialization techniques introduced in Section 6.\nFor both datasets, we started with the coalesced index produced by a moderate threshold setting of = 0.10.\nIn order to reduce the computational effort, boundaries of elementary time intervals were rounded to day granularity before computing the sublist materializations.\nHowever, note that the postings in the materialized sublists still retain their original timestamps.\nFor a comparative evaluation of the four approaches - Popt, Sopt, PG, and SB - we measure space and performance as follows.\nThe required space S(M), as defined earlier, is equal to the total number of postings in the materialized sublists.\nTo assess performance we compute the expected processing cost (EPC) for all terms in the respective query workload assuming a uniform probability distribution among query time-points.\nWe report the mean EPC, as well as the 5%- and 95%-percentile.\nIn other words, the mean EPC reflects the expected length of the index list (in terms of index postings) that needs to be scanned for a random time point and a random term from the query workload.\nThe Sopt and Popt approaches are, by their definition, parameter-free.\nFor the PG approach, we varied its parameter γ, which limits the maximal performance degradation, between 1.0 and 3.0.\nAnalogously, for the SB approach the parameter κ, as an upper-bound on the allowed space blowup, was varied between 1.0 and 3.0.\nSolutions for the SB approach were obtained running simulated annealing for R = 50, 000 rounds.\nTable 2 lists the obtained space and performance figures.\nNote that EPC values are smaller on WIKI than on UKGOV, since terms in the query workload employed for WIKI are relatively rarer in the corpus.\nBased on the depicted results, we make the following key observations.\ni) As expected, Popt achieves optimal performance at the cost of an enormous space consumption.\nSopt, to the contrary, while consuming an optimal amount of space, provides only poor expected processing cost.\nThe PG and SB methods, for different values of their respective parameter, produce solutions whose space and performance lie in between the extremes that Popt and Sopt represent.\nii) For the PG method we see that for an acceptable performance degradation of only 10% (i.e., γ = 1.10) the required space drops by more than one order of magnitude in comparison to Popt on both datasets.\niii) The SB approach achieves close-to-optimal performance on both datasets, if allowed to consume at most three times the optimal amount of space (i.e., κ = 3.0), which on our datasets still corresponds to a space reduction over Popt by more than one order of magnitude.\nWe also measured wall-clock times on a sample of the queries with results indicating improvements in execution time by up to a factor of 12.\n8.\nCONCLUSIONS In this work we have developed an efficient solution for time-travel text search over temporally versioned document collections.\nExperiments on two real-world datasets showed that a combination of the proposed techniques can reduce index size by up to an order of magnitude while achieving nearly optimal performance and highly accurate results.\nThe present work opens up many interesting questions for future research, e.g.: How can we even further improve performance by applying (and possibly extending) encoding, compression, and skipping techniques [35]?\n.\nHow can we extend the approach for queries q [tb, te] specifying a time interval instead of a time point?\nHow can the described time-travel text search functionality enable or speed up text mining along the time axis (e.g., tracking sentiment changes in customer opinions)?\n9.\nACKNOWLEDGMENTS We are grateful to the anonymous reviewers for their valuable comments - 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Moffat.\nInverted Files for Text Search Engines.\nACM Comput.\nSurv., 38(2):6, 2006."},"lvl-2":{"kind":"string","value":"A Time Machine for Text Search\nABSTRACT\nText search over temporally versioned document collections such as web archives has received little attention as a research problem.\nAs a consequence, there is no scalable and principled solution to search such a collection as of a specified time t.\nIn this work, we address this shortcoming and propose an efficient solution for time-travel text search by extending the inverted file index to make it ready for temporal search.\nWe introduce approximate temporal coalescing as a tunable method to reduce the index size without significantly affecting the quality of results.\nIn order to further improve the performance of time-travel queries, we introduce two principled techniques to trade off index size for its performance.\nThese techniques can be formulated as optimization problems that can be solved to near-optimality.\nFinally, our approach is evaluated in a comprehensive series of experiments on two large-scale real-world datasets.\nResults unequivocally show that our methods make it possible to build an efficient \"time machine\" scalable to large versioned text collections.\n1.\nINTRODUCTION\nIn this work we address time-travel text search over temporally versioned document collections.\nGiven a keyword query q and a time t our goal is to identify and rank relevant documents as if the collection was in its state as of time t.\nAn increasing number of such versioned document collections is available today including web archives, collabo\nrative authoring environments like Wikis, or timestamped information feeds.\nText search on these collections, however, is mostly time-ignorant: while the searched collection changes over time, often only the most recent version of a documents is indexed, or, versions are indexed independently and treated as separate documents.\nEven worse, for some collections, in particular web archives like the Internet Archive [18], a comprehensive text-search functionality is often completely missing.\nTime-travel text search, as we develop it in this paper, is a crucial tool to explore these collections and to unfold their full potential as the following example demonstrates.\nFor a documentary about a past political scandal, a journalist needs to research early opinions and statements made by the involved politicians.\nSending an appropriate query to a major web search-engine, the majority of returned results contains only recent coverage, since many of the early web pages have disappeared and are only preserved in web archives.\nIf the query could be enriched with a time point, say August 20th 2003 as the day after the scandal got revealed, and be issued against a web archive, only pages that existed specifically at that time could be retrieved thus better satisfying the journalist's information need.\nDocument collections like the Web or Wikipedia [32], as we target them here, are already large if only a single snapshot is considered.\nLooking at their evolutionary history, we are faced with even larger data volumes.\nAs a consequence, na ¨ ıve approaches to time-travel text search fail, and viable approaches must scale-up well to such large data volumes.\nThis paper presents an efficient solution to time-travel text search by making the following key contributions:\n1.\nThe popular well-studied inverted file index [35] is transparently extended to enable time-travel text search.\n2.\nTemporal coalescing is introduced to avoid an indexsize explosion while keeping results highly accurate.\n3.\nWe develop two sublist materialization techniques to improve index performance that allow trading off space vs. performance.\n4.\nIn a comprehensive experimental evaluation our approach is evaluated on the English Wikipedia and parts of the Internet Archive as two large-scale real-world datasets with versioned documents.\nThe remainder of this paper is organized as follows.\nThe presented work is put in context with related work in Section 2.\nWe delineate our model of a temporally versioned document collection in Section 3.\nWe present our time-travel inverted index in Section 4.\nBuilding on it, temporal coalescing is described in Section 5.\nIn Section 6 we describe principled techniques to improve index performance, before presenting the results of our experimental evaluation in Section 7.\n2.\nRELATED WORK\nWe can classify the related work mainly into the following two categories: (i) methods that deal explicitly with collections of versioned documents or temporal databases, and (ii) methods for reducing the index size by exploiting either the document-content overlap or by pruning portions of the index.\nWe briefly review work under these categories here.\nTo the best of our knowledge, there is very little prior work dealing with historical search over temporally versioned documents.\nAnick and Flynn [3], while pioneering this research, describe a help-desk system that supports historical queries.\nAccess costs are optimized for accesses to the most recent versions and increase as one moves farther into the past.\nBurrows and Hisgen [10], in a patent description, delineate a method for indexing range-based values and mention its potential use for searching based on dates associated with documents.\nRecent work by Nørv˚ag and Nybø [25] and their earlier proposals concentrate on the relatively simpler problem of supporting text-containment queries only and neglect the relevance scoring of results.\nStack [29] reports practical experiences made when adapting the open source search-engine Nutch to search web archives.\nThis adaptation, however, does not provide the intended time-travel text search functionality.\nIn contrast, research in temporal databases has produced several index structures tailored for time-evolving databases; a comprehensive overview of the state-of-art is available in [28].\nUnlike the inverted file index, their applicability to text search is not well understood.\nMoving on to the second category of related work, Broder et al. [8] describe a technique that exploits large content overlaps between documents to achieve a reduction in index size.\nTheir technique makes strong assumptions about the structure of document overlaps rendering it inapplicable to our context.\nMore recent approaches by Hersovici et al. [17] and Zhang and Suel [34] exploit arbitrary content overlaps between documents to reduce index size.\nNone of the approaches, however, considers time explicitly or provides the desired time-travel text search functionality.\nStatic indexpruning techniques [11, 12] aim to reduce the effective index size, by removing portions of the index that are expected to have low impact on the query result.\nThey also do not consider temporal aspects of documents, and thus are technically quite different from our proposal despite having a shared goal of index-size reduction.\nIt should be noted that index-pruning techniques can be adapted to work along with the temporal text index we propose here.\n3.\nMODEL\nIn the present work, we deal with a temporally versioned document collection D that is modeled as described in the following.\nEach document d E D is a sequence of its versions d = (dt1, dt2, ...).\nEach version d ti has an associated timestamp ti reflecting when the version was created.\nEach version is a vector of searchable terms or features.\nAny modification to a document version results in the insertion of a new version with corresponding timestamp.\nWe employ a discrete definition of time, so that timestamps are non-negative integers.\nThe deletion of a document at time ti, i.e., its disappearance from the current state of the collection, is modeled as the insertion of a special \"tombstone\" version 1.\nThe validity time-interval val (d ti) of a version d ti is [ti, ti +1), if a newer version with associated timestamp ti +1 exists, and [ti, now) otherwise where now points to the greatest possible value of a timestamp (i.e., ` dt: t <now).\nPutting all this together, we define the state D t of the collection at time t (i.e., the set of versions valid at t that are not deletions) as\nAs mentioned earlier, we want to enrich a keyword query q with a timestamp t, so that q be evaluated over D t, i.e., the state of the collection at time t.\nThe enriched time-travel query is written as q t for brevity.\nAs a retrieval model in this work we adopt Okapi BM25 [27], but note that the proposed techniques are not dependent on this choice and are applicable to other retrieval models like tf-idf [4] or language models [26] as well.\nFor our considered setting, we slightly adapt Okapi BM25 as w (q t, d ti) = X wtf (v, d ti).\nwidf (v, t).\nv ∈ q In the above formula, the relevance w (q t, d ti) of a document version dti to the time-travel query q t is defined.\nWe reiterate that q t is evaluated over D t so that only the version d ti valid at time t is considered.\nThe first factor wtf (v, d ti) in the summation, further referred to as the tfscore is defined as\nIt considers the plain term frequency tf (v, d ti) of term v in version d ti normalizing it, taking into account both the length dl (d ti) of the version and the average document length avdl (ti) in the collection at time ti.\nThe length-normalization parameter b and the tf-saturation parameter k1 are inherited from the original Okapi BM25 and are commonly set to values 1.2 and 0.75 respectively.\nThe second factor widf (v, t), which we refer to as the idf-score in the remainder, conveys the inverse document frequency of term v in the collection at time t and is defined as\nwhere N (t) = ID tI is the collection size at time t and df (v, t) gives the number of documents in the collection that contain the term v at time t.\nWhile the idf-score depends on the whole corpus as of the query time t, the tf-score is specific to each version.\n4.\nTIME-TRAVEL INVERTED FILE INDEX\nThe inverted file index is a standard technique for text indexing, deployed in many systems.\nIn this section, we briefly review this technique and present our extensions to the inverted file index that make it ready for time-travel text search.\n4.1 Inverted File Index\nAn inverted file index consists of a vocabulary, commonly organized as a B + - Tree, that maps each term to its idfscore and inverted list.\nThe index list Lv belonging to term v contains postings of the form\nwhere d is a document-identifier and p is the so-called payload.\nThe payload p contains information about the term frequency of v in d, but may also include positional information about where the term appears in the document.\nThe sort-order of index lists depends on which queries are to be supported efficiently.\nFor Boolean queries it is favorable to sort index lists in document-order.\nFrequencyorder and impact-order sorted index lists are beneficial for ranked queries and enable optimized query processing that stops early after having identified the k most relevant documents [1, 2, 9, 15, 31].\nA variety of compression techniques, such as encoding document identifiers more compactly, have been proposed [33, 35] to reduce the size of index lists.\nFor an excellent recent survey about inverted file indexes we refer to [35].\n4.2 Time-Travel Inverted File Index\nIn order to prepare an inverted file index for time travel we extend both inverted lists and the vocabulary structure by explicitly incorporating temporal information.\nThe main idea for inverted lists is that we include a validity timeinterval [tb, te) in postings to denote when the payload information was valid.\nThe postings in our time-travel inverted file index are thus of the form (d, p, [tb, te)) where d and p are defined as in the standard inverted file index above and [tb, te) is the validity time-interval.\nAs a concrete example, in our implementation, for a version d ti having the Okapi BM25 tf-score wtf (v, d ti) for term v, the index list Lv contains the posting (d, wtf (v, d ti), [ti, ti +1)).\nSimilarly, the extended vocabulary structure maintains for each term a time-series of idf-scores organized as a B+T ree.\nUnlike the tf-score, the idf-score of every term could vary with every change in the corpus.\nTherefore, we take a simplified approach to idf-score maintenance, by computing idf-scores for all terms in the corpus at specific (possibly periodic) times.\n4.3 Query Processing\nDuring processing of a time-travel query q t, for each query term the corresponding idf-score valid at time t is retrieved from the extended vocabulary.\nThen, index lists are sequentially read from disk, thereby accumulating the information contained in the postings.\nWe transparently extend the sequential reading, which is--to the best of our knowledge--common to all query processing techniques on inverted file indexes, thus making them suitable for time-travel queryprocessing.\nTo this end, sequential reading is extended by skipping all postings whose validity time-interval does not contain t (i.e., t ∈ 6 [tb, te)).\nWhether a posting can be skipped can only be decided after the posting has been transferred from disk into memory and therefore still incurs significant I/O cost.\nAs a remedy, we propose index organization techniques in Section 6 that aim to reduce the I/O overhead significantly.\nWe note that our proposed extension of the inverted file index makes no assumptions about the sort-order of index lists.\nAs a consequence, existing query-processing techniques and most optimizations (e.g., compression techniques) remain equally applicable.\n5.\nTEMPORAL COALESCING\nIf we employ the time-travel inverted index, as described in the previous section, to a versioned document collection, we obtain one posting per term per document version.\nFor frequent terms and large highly-dynamic collections, this\nFigure 1: Approximate Temporal Coalescing\nleads to extremely long index lists with very poor queryprocessing performance.\nThe approximate temporal coalescing technique that we propose in this section counters this blowup in index-list size.\nIt builds on the observation that most changes in a versioned document collection are minor, leaving large parts of the document untouched.\nAs a consequence, the payload of many postings belonging to temporally adjacent versions will differ only slightly or not at all.\nApproximate temporal coalescing reduces the number of postings in an index list by merging such a sequence of postings that have almost equal payloads, while keeping the maximal error bounded.\nThis idea is illustrated in Figure 1, which plots non-coalesced and coalesced scores of postings belonging to a single document.\nApproximate temporal coalescing is greatly effective given such fluctuating payloads and reduces the number of postings from 9 to 3 in the example.\nThe notion of temporal coalescing was originally introduced in temporal database research by B ¨ ohlen et al. [6], where the simpler problem of coalescing only equal information was considered.\nWe next formally state the problem dealt with in approximate temporal coalescing, and discuss the computation of optimal and approximate solutions.\nNote that the technique is applied to each index list separately, so that the following explanations assume a fixed term v and index list Lv.\nAs an input we are given a sequence of temporally adjacent postings\nEach sequence represents a contiguous time period during which the term was present in a single document d.\nIf a term disappears from d but reappears later, we obtain multiple input sequences that are dealt with separately.\nWe seek to generate the minimal length output sequence of postings\nthat adheres to the following constraints: First, O and I must cover the same time-range, i.e., ti = tj and tn = tm.\nSecond, when coalescing a subsequence of postings of the input into a single posting of the output, we want the approximation error to be below a threshold ~.\nIn other words, if (d, pi, [ti, ti +1)) and (d, pj, [tj, tj +1)) are postings of I and O respectively, then the following must hold for a chosen error function and a threshold ~: tj <ti n ti +1 <tj +1 =:>.\nerror (pi, pj) <~.\nIn this paper, as an error function we employ the relative error between payloads (i.e., tf-scores) of a document in I and O, defined as: errrel (pi, pj) = Ipi--pjI / IpiI.\nFinding an optimal output sequence of postings can be cast into finding a piecewise-constant representation for the points (ti, pi) that uses a minimal number of segments while retaining the above approximation guarantee.\nSimilar problems occur in time-series segmentation [21, 30] and histogram construction [19, 20].\nTypically dynamic programming is score\napplied to obtain an optimal solution in O (n2 m *) [20, 30] time with m * being the number of segments in an optimal sequence.\nIn our setting, as a key difference, only a guarantee on the local error is retained--in contrast to a guarantee on the global error in the aforementioned settings.\nExploiting this fact, an optimal solution is computable by means of induction [24] in O (n2) time.\nDetails of the optimal algorithm are omitted here but can be found in the accompanying technical report [5].\nThe quadratic complexity of the optimal algorithm makes it inappropriate for the large datasets encountered in this work.\nAs an alternative, we introduce a linear-time approximate algorithm that is based on the sliding-window algorithm given in [21].\nThis algorithm produces nearly-optimal output sequences that retain the bound on the relative error, but possibly require a few additional segments more than an optimal solution.\nAlgorithm 1 Temporal Coalescing (Approximate) 1: I = ((d, pi, [ti, ti +1)), ...) O = () 2: pmin = pi pmax = pi p = pi tb = ti te = ti +1 3: for (d, pj, [tj, tj +1)) ∈ I do 4: pnamin = min (pmin, pj) pmmax = max (pmax, pj) 5: p0 = optrep (p' min, pm, max) 6: if errrel (pmin, pI) <e ∧ errrel (pmax, p) <e then 7: pmin = pmin pmax = pmax p = p te = tj +1 8: else 9: O = O U (d, p, [tb, te)) 10: pmin = pj pmax = pj p = pj tb = tj te = tj +1 11: end if 12: end for 13: O = O U (d, p, [tb, te))\nAlgorithm 1 makes one pass over the input sequence I.\nWhile doing so, it coalesces sequences of postings having maximal length.\nThe optimal representative for a sequence of postings depends only on their minimal and maximal payload (pmin and pmax) and can be looked up using optrep in O (1) (see [16] for details).\nWhen reading the next posting, the algorithm tries to add it to the current sequence of postings.\nIt computes the hypothetical new representative p and checks whether it would retain the approximation guarantee.\nIf this test fails, a coalesced posting bearing the old representative is added to the output sequence O and, following that, the bookkeeping is reinitialized.\nThe time complexity of the algorithm is in O (n).\nNote that, since we make no assumptions about the sort order of index lists, temporal-coalescing algorithms have an additional preprocessing cost in O (| Lv | log | Lv |) for sorting the index list and chopping it up into subsequences for each document.\n6.\nSUBLIST MATERIALIZATION\nEfficiency of processing a query q t on our time-travel inverted index is influenced adversely by the wasted I/O due to read but skipped postings.\nTemporal coalescing implicitly addresses this problem by reducing the overall index list size, but still a significant overhead remains.\nIn this section, we tackle this problem by proposing the idea of materializing sublists each of which corresponds to a contiguous subinterval of time spanned by the full index.\nEach of these sublists contains all coalesced postings that overlap with the corresponding time interval of the sublist.\nNote that all those postings whose validity time-interval spans across the temporal boundaries of several sublists are replicated in each of the spanned sublists.\nThus, in order to process the query q t\nand demand\ni.e., the time intervals in M must completely cover the time interval [t1, tn), so that time-travel queries q t for all t ∈ [t1, tn) can be processed.\nWe also assume that intervals in M are disjoint.\nWe can make this assumption without ruling out any optimal solution with regard to space or performance defined below.\nThe space required for the materialization of sublists in a set M is defined as\nFigure 2: Sublist Materialization\nit is sufficient to scan any materialized sublist whose timeinterval contains t.\nWe illustrate the idea of sublist materialization using an example shown in Figure 2.\nThe index list Lv visualized in the figure contains a total of 10 postings from three documents d1, d2, and d3.\nFor ease of description, we have numbered boundaries of validity time-intervals, in increasing time-order, as t1,..., t10 and numbered the postings themselves as 1,..., 10.\nNow, consider the processing of a query q t with t ∈ [t1, t2) using this inverted list.\nAlthough only three postings (postings 1, 5 and 8) are valid at time t, the whole inverted list has to be read in the worst case.\nSuppose that we split the time axis of the list at time t2, forming two sublists with postings {1, 5, 8} and {2, 3, 4, 5, 6, 7, 8, 9, 10} respectively.\nThen, we can process the above query with optimal cost by reading only those postings that existed at this t.\nAt a first glance, it may seem counterintuitive to reduce index size in the first step (using temporal coalescing), and then to increase it again using the sublist materialization techniques presented in this section.\nHowever, we reiterate that our main objective is to improve the efficiency of processing queries, not to reduce the index size alone.\nThe use of temporal coalescing improves the performance by reducing the index size, while the sublist materialization improves performance by judiciously replicating entries.\nFurther, the two techniques, can be applied separately and are independent.\nIf applied in conjunction, though, there is a synergetic effect--sublists that are materialized from a temporally coalesced index are generally smaller.\nWe employ the notation Lv: [ti, tj) to refer to the materialized sublist for the time interval [ti, tj), that is formally defined as,\nTo aid the presentation in the rest of the paper, we first provide some definitions.\nLet T = (t1...tn) be the sorted sequence of all unique time-interval boundaries of an inverted list Lv.\nThen we define\nto be the set of elementary time intervals.\nWe refer to the set of time intervals for which sublists are materialized as\ni.e., the total length of all lists in M. Given a set M, we let π ([ti, ti +1)) = [tj, tk) ∈ M: [ti, ti +1) ⊆ [tj, tk) denote the time interval that is used to process queries q t with t ∈ [ti, ti +1).\nThe performance of processing queries q t for t ∈ [ti, ti +1) inversely depends on its processing cost\nwhich is assumed to be proportional to the length of the list Lv: π ([ti, ti +1)).\nThus, in order to optimize the performance of processing queries we minimize their processing costs.\n6.1 Performance/Space-Optimal Approaches\nOne strategy to eliminate the problem of skipped postings is to eagerly materialize sublists for all elementary time intervals, i.e., to choose M = E.\nIn doing so, for every query q t only postings valid at time t are read and thus the best possible performance is achieved.\nTherefore, we will refer to this approach as Popt in the remainder.\nThe initial approach described above that keeps only the full list Lv and thus picks M = {[t1, tn)} is referred to as Sopt in the remainder.\nThis approach requires minimal space, since it keeps each posting exactly once.\nPopt and Sopt are extremes: the former provides the best possible performance but is not space-efficient, the latter requires minimal space but does not provide good performance.\nThe two approaches presented in the rest of this section allow mutually trading off space and performance and can thus be thought of as means to explore the configuration spectrum between the Popt and the Sopt approach.\n6.2 Performance-Guarantee Approach\nThe Popt approach clearly wastes a lot of space materializing many nearly-identical sublists.\nIn the example illustrated in Figure 2 materialized sublists for [t1, t2) and [t2, t3) differ only by one posting.\nIf the sublist for [t1, t3) was materialized instead, one could save significant space while incurring only an overhead of one skipped posting for all t ∈ [t1, t3).\nThe technique presented next is driven by the idea that significant space savings over Popt are achievable, if an upper-bounded loss on the performance can be tolerated, or to put it differently, if a performance guarantee relative to the optimum is to be retained.\nIn detail, the technique, which we refer to as PG (Performance Guarantee) in the remainder, finds a set M that has minimal required space, but guarantees for any elementary time interval [ti, ti +1) (and thus for any query q t with t ∈ [ti, ti +1)) that performance is worse than optimal by at most a factor of - y ≥ 1.\nFormally, this problem can be stated as\nAn optimal solution to the problem can be computed by means of induction using the recurrence\nwhere C ([t1, tj)) is the optimal cost (i.e., the space required) for the prefix subproblem\nIntuitively, the recurrence states that an optimal solution for [t1, tk +1) be combined from an optimal solution to a prefix subproblem C ([t1, tj)) and a time interval [tj, tk +1) that can be materialized without violating the performance guarantee.\nPseudocode of the algorithm is omitted for space reasons, but can be found in the accompanying technical report [5].\nThe time complexity of the algorithm is in O (n 2)--for each prefix subproblem the above recurrence must be evaluated, which is possible in linear time if list sizes | L: [ti, tj) | are precomputed.\nThe space complexity is in O (n 2)--the cost of keeping the precomputed sublist lengths and memoizing optimal solutions to prefix subproblems.\n6.3 Space-Bound Approach\nSo far we considered the problem of materializing sublists that give a guarantee on performance while requiring minimal space.\nIn many situations, though, the storage space is at a premium and the aim would be to materialize a set of sublists that optimizes expected performance while not exceeding a given space limit.\nThe technique presented next, which is named SB, tackles this very problem.\nThe space restriction is modeled by means of a user-specified parameter r, ≥ 1 that limits the maximum allowed blowup in index size from the space-optimal solution provided by Sopt.\nThe SB technique seeks to find a set M that adheres to this space limit but minimizes the expected processing cost (and thus optimizes the expected performance).\nIn the definition of the expected processing cost, P ([ti, ti +1)) denotes the probability of a query time-point being in [ti, ti +1).\nFormally, this space-bound sublist-materialization problem can be stated as\nThe problem can be solved by using dynamic programming over an increasing number of time intervals: At each time interval in E the algorithms decides whether to start a new materialization time-interval, using the known best materialization decision from the previous time intervals, and keeping track of the required space consumption for materialization.\nA detailed description of the algorithm is omitted here, but can be found in the accompanying technical report [5].\nUnfortunately, the algorithm has time complexity in O (n3 | Lv |) and its space complexity is in O (n2 | Lv |), which is not practical for large data sets.\nWe obtain an approximate solution to the problem using simulated annealing [22, 23].\nSimulated annealing takes a fixed number R of rounds to explore the solution space.\nIn each round a random successor of the current solution is looked at.\nIf the successor does not adhere to the space limit, it is always rejected (i.e., the current solution is kept).\nA successor adhering to the space limit is always accepted if it achieves lower expected processing cost than the current solution.\nIf it achieves higher expected processing cost, it is randomly accepted with probability e − o/r where Δ is the increase in expected processing cost and R ≥ r ≥ 1 denotes the number of remaining rounds.\nIn addition, throughout all rounds, the method keeps track of the best solution seen\nso far.\nThe solution space for the problem at hand can be efficiently explored.\nAs we argued above, we solely have to look at sets M that completely cover the time interval [t1, tn) and do not contain overlapping time intervals.\nWe represent such a set M as an array of n boolean variables b1...bn that convey the boundaries of time intervals in the set.\nNote that b1 and bn are always set to \"true\".\nInitially, all n − 2 intermediate variables assume \"false\", which corresponds to the set M = {[t1, tn)}.\nA random successor can now be easily generated by switching the value of one of the n − 2 intermediate variables.\nThe time complexity of the method is in O (n 2)--the expected processing cost must be computed in each round.\nIts space complexity is in O (n)--for keeping the n boolean variables.\nAs a side remark note that for κ = 1.0 the SB method does not necessarily produce the solution that is obtained from Soft, but may produce a solution that requires the same amount of space while achieving better expected performance.\n7.\nEXPERIMENTAL EVALUATION\nWe conducted a comprehensive series of experiments on two real-world datasets to evaluate the techniques proposed in this paper.\n7.1 Setup and Datasets\nThe techniques described in this paper were implemented in a prototype system using Java JDK 1.5.\nAll experiments described below were run on a single SUN V40z machine having four AMD Opteron CPUs, 16GB RAM, a large network-attached RAID-5 disk array, and running Microsoft Windows Server 2003.\nAll data and indexes are kept in an Oracle 10g database that runs on the same machine.\nFor our experiments we used two different datasets.\nThe English Wikipedia revision history (referred to as WIKI in the remainder) is available for free download as a single XML file.\nThis large dataset, totaling 0.7 TBytes, contains the full editing history of the English Wikipedia from January 2001 to December 2005 (the time of our download).\nWe indexed all encyclopedia articles excluding versions that were marked as the result of a minor edit (e.g., the correction of spelling errors etc.).\nThis yielded a total of 892,255 documents with 13,976,915 versions having a mean (µ) of 15.67 versions per document at standard deviation (σ) of 59.18.\nWe built a time-travel query workload using the query log temporarily made available recently by AOL Research as follows--we first extracted the 300 most frequent keyword queries that yielded a result click on a Wikipedia article (for e.g., \"french revolution\", \"hurricane season 2005\", \"da vinci code\" etc.).\nThe thus extracted queries contained a total of 422 distinct terms.\nFor each extracted query, we randomly picked a time point for each month covered by the dataset.\nThis resulted in a total of 18, 000 (= 300 x 60) time-travel queries.\nThe second dataset used in our experiments was based on a subset of the European Archive [13], containing weekly crawls of 11.\ngov.uk websites throughout the years 2004 and 2005 amounting close to 2 TBytes of raw data.\nWe filtered out documents not belonging to MIME-types text/plain and text/html, to obtain a dataset that totals 0.4 TBytes and which we refer to as UKGOV in rest of the paper.\nThis included a total of 502,617 documents with 8,687,108 versions (µ = 17.28 and σ = 13.79).\nWe built a corresponding query workload as mentioned before, this time choosing keyword queries that led to a site in the.\ngov.uk domain (e.g., \"minimum wage\", \"inheritance tax\", \"citizenship ceremony dates\" etc.), and randomly sampling a time point for every month within the two year period spanned by the dataset.\nThus, we obtained a total of 7,200 (= 300 x 24) time-travel queries for the UKGOV dataset.\nIn total 522 terms appear in the extracted queries.\nThe collection statistics (i.e., N and avdl) and term statistics (i.e., DF) were computed at monthly granularity for both datasets.\n7.2 Impact of Temporal Coalescing\nOur first set of experiments is aimed at evaluating the approximate temporal coalescing technique, described in Section 5, in terms of index-size reduction and its effect on the result quality.\nFor both the WIKI and UKGOV datasets, we compare temporally coalesced indexes for different values of the error threshold e computed using Algorithm 1 with the non-coalesced index as a baseline.\nTable 1: Index sizes for non-coalesced index (-) and coalesced indexes for different values of e\nTable 1 summarizes the index sizes measured as the total number of postings.\nAs these results demonstrate, approximate temporal coalescing is highly effective in reducing index size.\nEven a small threshold value, e.g. e = 0.01, has a considerable effect by reducing the index size almost by an order of magnitude.\nNote that on the UKGOV dataset, even accurate coalescing (e = 0) manages to reduce the index size to less than 38% of the original size.\nIndex size continues to reduce on both datasets, as we increase the value of E.\nHow does the reduction in index size affect the query results?\nIn order to evaluate this aspect, we compared the top-k results computed using a coalesced index against the ground-truth result obtained from the original index, for different cutoff levels k. Let Gk and Ck be the top-k documents from the ground-truth result and from the coalesced index respectively.\nWe used the following two measures for comparison: (i) Relative Recall at cutoff level k (RR@k), that measures the overlap between Gk and Ck, which ranges in [0, 1] and is defined as\n(ii) Kendall's τ (see [7, 14] for a detailed definition) at cutoff level k (KT@k), measuring the agreement between two results in the relative order of items in Gk n Ck, with value 1 (or -1) indicating total agreement (or disagreement).\nFigure 3 plots, for cutoff levels 10 and 100, the mean of RR@k and KT@k along with 5% and 95% percentiles, for different values of the threshold a starting from 0.01.\nNote that for e = 0, results coincide with those obtained by the original index, and hence are omitted from the graph.\nIt is reassuring to see from these results that approximate temporal coalescing induces minimal disruption to the query results, since RR@k and KT@k are within reasonable limits.\nFor e = 0.01, the smallest value of a in our experiments, RR@100 for WIKI is 0.98 indicating that the results are\nFigure 3: Relative recall and Kendall's τ observed on coalesced indexes for different values of e\nalmost indistinguishable from those obtained through the original index.\nEven the relative order of these common results is quite high, as the mean KT@100 is close to 0.95.\nFor the extreme value of e = 0.5, which results in an index size of just 2.35% of the original, the RR@100 and KT@100 are about 0.8 and 0.6 respectively.\nOn the relatively less dynamic UKGOV dataset (as can be seen from the σ values above), results were even better, with high values of RR and KT seen throughout the spectrum of a values for both cutoff values.\n7.3 Sublist Materialization\nWe now turn our attention towards evaluating the sublist materialization techniques introduced in Section 6.\nFor both datasets, we started with the coalesced index produced by a moderate threshold setting of e = 0.10.\nIn order to reduce the computational effort, boundaries of elementary time intervals were rounded to day granularity before computing the sublist materializations.\nHowever, note that the postings in the materialized sublists still retain their original timestamps.\nFor a comparative evaluation of the four approaches--Poet, Soet, PG, and SB--we measure space and performance as follows.\nThe required space S (M), as defined earlier, is equal to the total number of postings in the materialized sublists.\nTo assess performance we compute the expected processing cost (EPC) for all terms in the respective query workload assuming a uniform probability distribution among query time-points.\nWe report the mean EPC, as well as the 5% - and 95% - percentile.\nIn other words, the mean EPC reflects the expected length of the index list (in terms of index postings) that needs to be scanned for a random time point and a random term from the query workload.\nThe Soet and Poet approaches are, by their definition, parameter-free.\nFor the PG approach, we varied its parameter - y, which limits the maximal performance degradation, between 1.0 and 3.0.\nAnalogously, for the SB approach the parameter r,, as an upper-bound on the allowed space blowup, was varied between 1.0 and 3.0.\nSolutions for the SB approach were obtained running simulated annealing for R = 50, 000 rounds.\nTable 2 lists the obtained space and performance figures.\nNote that EPC values are smaller on WIKI than on UKGOV, since terms in the query workload employed for WIKI are relatively rarer in the corpus.\nBased on the depicted results, we make the following key observations.\ni) As expected, Poet achieves optimal performance at the cost of an enormous space consumption.\nSoet, to the contrary, while consuming an optimal amount of space, provides only poor expected processing cost.\nThe PG and SB methods, for different values of their respective parameter, produce solutions whose space and performance lie in between the extremes that Poet and Soet represent.\nii) For the PG method we see that for an acceptable performance degradation of only 10% (i.e., - y = 1.10) the required space drops by more than one order of magnitude in comparison to Poet on both datasets.\niii) The SB approach achieves close-to-optimal performance on both datasets, if allowed to consume at most three times the optimal amount of space (i.e., r, = 3.0), which on our datasets still corresponds to a space reduction over Poet by more than one order of magnitude.\nWe also measured wall-clock times on a sample of the queries with results indicating improvements in execution time by up to a factor of 12.\n8.\nCONCLUSIONS\nIn this work we have developed an efficient solution for time-travel text search over temporally versioned document collections.\nExperiments on two real-world datasets showed that a combination of the proposed techniques can reduce index size by up to an order of magnitude while achieving nearly optimal performance and highly accurate results.\nThe present work opens up many interesting questions for future research, e.g.: How can we even further improve performance by applying (and possibly extending) encoding, compression, and skipping techniques [35]?\n.\nHow can we extend the approach for queries q [tb, te] specifying a time interval instead of a time point?\nHow can the described time-travel text search functionality enable or speed up text mining along the time axis (e.g., tracking sentiment changes in customer opinions)?"},"lvl-3":{"kind":"string","value":"A Time Machine for Text Search\nABSTRACT\nText search over temporally versioned document collections such as web archives has received little attention as a research problem.\nAs a consequence, there is no scalable and principled solution to search such a collection as of a specified time t.\nIn this work, we address this shortcoming and propose an efficient solution for time-travel text search by extending the inverted file index to make it ready for temporal search.\nWe introduce approximate temporal coalescing as a tunable method to reduce the index size without significantly affecting the quality of results.\nIn order to further improve the performance of time-travel queries, we introduce two principled techniques to trade off index size for its performance.\nThese techniques can be formulated as optimization problems that can be solved to near-optimality.\nFinally, our approach is evaluated in a comprehensive series of experiments on two large-scale real-world datasets.\nResults unequivocally show that our methods make it possible to build an efficient \"time machine\" scalable to large versioned text collections.\n1.\nINTRODUCTION\nIn this work we address time-travel text search over temporally versioned document collections.\nGiven a keyword query q and a time t our goal is to identify and rank relevant documents as if the collection was in its state as of time t.\nAn increasing number of such versioned document collections is available today including web archives, collabo\nrative authoring environments like Wikis, or timestamped information feeds.\nText search on these collections, however, is mostly time-ignorant: while the searched collection changes over time, often only the most recent version of a documents is indexed, or, versions are indexed independently and treated as separate documents.\nEven worse, for some collections, in particular web archives like the Internet Archive [18], a comprehensive text-search functionality is often completely missing.\nTime-travel text search, as we develop it in this paper, is a crucial tool to explore these collections and to unfold their full potential as the following example demonstrates.\nFor a documentary about a past political scandal, a journalist needs to research early opinions and statements made by the involved politicians.\nSending an appropriate query to a major web search-engine, the majority of returned results contains only recent coverage, since many of the early web pages have disappeared and are only preserved in web archives.\nIf the query could be enriched with a time point, say August 20th 2003 as the day after the scandal got revealed, and be issued against a web archive, only pages that existed specifically at that time could be retrieved thus better satisfying the journalist's information need.\nDocument collections like the Web or Wikipedia [32], as we target them here, are already large if only a single snapshot is considered.\nLooking at their evolutionary history, we are faced with even larger data volumes.\nAs a consequence, na ¨ ıve approaches to time-travel text search fail, and viable approaches must scale-up well to such large data volumes.\nThis paper presents an efficient solution to time-travel text search by making the following key contributions:\n1.\nThe popular well-studied inverted file index [35] is transparently extended to enable time-travel text search.\n2.\nTemporal coalescing is introduced to avoid an indexsize explosion while keeping results highly accurate.\n3.\nWe develop two sublist materialization techniques to improve index performance that allow trading off space vs. performance.\n4.\nIn a comprehensive experimental evaluation our approach is evaluated on the English Wikipedia and parts of the Internet Archive as two large-scale real-world datasets with versioned documents.\nThe remainder of this paper is organized as follows.\nThe presented work is put in context with related work in Section 2.\nWe delineate our model of a temporally versioned document collection in Section 3.\nWe present our time-travel inverted index in Section 4.\nBuilding on it, temporal coalescing is described in Section 5.\nIn Section 6 we describe principled techniques to improve index performance, before presenting the results of our experimental evaluation in Section 7.\n2.\nRELATED WORK\nWe can classify the related work mainly into the following two categories: (i) methods that deal explicitly with collections of versioned documents or temporal databases, and (ii) methods for reducing the index size by exploiting either the document-content overlap or by pruning portions of the index.\nWe briefly review work under these categories here.\nTo the best of our knowledge, there is very little prior work dealing with historical search over temporally versioned documents.\nAnick and Flynn [3], while pioneering this research, describe a help-desk system that supports historical queries.\nAccess costs are optimized for accesses to the most recent versions and increase as one moves farther into the past.\nBurrows and Hisgen [10], in a patent description, delineate a method for indexing range-based values and mention its potential use for searching based on dates associated with documents.\nRecent work by Nørv˚ag and Nybø [25] and their earlier proposals concentrate on the relatively simpler problem of supporting text-containment queries only and neglect the relevance scoring of results.\nStack [29] reports practical experiences made when adapting the open source search-engine Nutch to search web archives.\nThis adaptation, however, does not provide the intended time-travel text search functionality.\nIn contrast, research in temporal databases has produced several index structures tailored for time-evolving databases; a comprehensive overview of the state-of-art is available in [28].\nUnlike the inverted file index, their applicability to text search is not well understood.\nMoving on to the second category of related work, Broder et al. [8] describe a technique that exploits large content overlaps between documents to achieve a reduction in index size.\nTheir technique makes strong assumptions about the structure of document overlaps rendering it inapplicable to our context.\nMore recent approaches by Hersovici et al. [17] and Zhang and Suel [34] exploit arbitrary content overlaps between documents to reduce index size.\nNone of the approaches, however, considers time explicitly or provides the desired time-travel text search functionality.\nStatic indexpruning techniques [11, 12] aim to reduce the effective index size, by removing portions of the index that are expected to have low impact on the query result.\nThey also do not consider temporal aspects of documents, and thus are technically quite different from our proposal despite having a shared goal of index-size reduction.\nIt should be noted that index-pruning techniques can be adapted to work along with the temporal text index we propose here.\n3.\nMODEL\n4.\nTIME-TRAVEL INVERTED FILE INDEX\n4.1 Inverted File Index\n4.2 Time-Travel Inverted File Index\n4.3 Query Processing\n5.\nTEMPORAL COALESCING\n6.\nSUBLIST MATERIALIZATION\n6.1 Performance/Space-Optimal Approaches\n6.2 Performance-Guarantee Approach\n6.3 Space-Bound Approach\n7.\nEXPERIMENTAL EVALUATION\n7.1 Setup and Datasets\n7.2 Impact of Temporal Coalescing\n7.3 Sublist Materialization\n8.\nCONCLUSIONS\nIn this work we have developed an efficient solution for time-travel text search over temporally versioned document collections.\nExperiments on two real-world datasets showed that a combination of the proposed techniques can reduce index size by up to an order of magnitude while achieving nearly optimal performance and highly accurate results.\nThe present work opens up many interesting questions for future research, e.g.: How can we even further improve performance by applying (and possibly extending) encoding, compression, and skipping techniques [35]?\n.\nHow can we extend the approach for queries q [tb, te] specifying a time interval instead of a time point?\nHow can the described time-travel text search functionality enable or speed up text mining along the time axis (e.g., tracking sentiment changes in customer opinions)?"},"lvl-4":{"kind":"string","value":"A Time Machine for Text Search\nABSTRACT\nText search over temporally versioned document collections such as web archives has received little attention as a research problem.\nAs a consequence, there is no scalable and principled solution to search such a collection as of a specified time t.\nIn this work, we address this shortcoming and propose an efficient solution for time-travel text search by extending the inverted file index to make it ready for temporal search.\nWe introduce approximate temporal coalescing as a tunable method to reduce the index size without significantly affecting the quality of results.\nIn order to further improve the performance of time-travel queries, we introduce two principled techniques to trade off index size for its performance.\nThese techniques can be formulated as optimization problems that can be solved to near-optimality.\nFinally, our approach is evaluated in a comprehensive series of experiments on two large-scale real-world datasets.\nResults unequivocally show that our methods make it possible to build an efficient \"time machine\" scalable to large versioned text collections.\n1.\nINTRODUCTION\nIn this work we address time-travel text search over temporally versioned document collections.\nGiven a keyword query q and a time t our goal is to identify and rank relevant documents as if the collection was in its state as of time t.\nAn increasing number of such versioned document collections is available today including web archives, collabo\nText search on these collections, however, is mostly time-ignorant: while the searched collection changes over time, often only the most recent version of a documents is indexed, or, versions are indexed independently and treated as separate documents.\nEven worse, for some collections, in particular web archives like the Internet Archive [18], a comprehensive text-search functionality is often completely missing.\nTime-travel text search, as we develop it in this paper, is a crucial tool to explore these collections and to unfold their full potential as the following example demonstrates.\nDocument collections like the Web or Wikipedia [32], as we target them here, are already large if only a single snapshot is considered.\nAs a consequence, na ¨ ıve approaches to time-travel text search fail, and viable approaches must scale-up well to such large data volumes.\nThis paper presents an efficient solution to time-travel text search by making the following key contributions:\n1.\nThe popular well-studied inverted file index [35] is transparently extended to enable time-travel text search.\n2.\nTemporal coalescing is introduced to avoid an indexsize explosion while keeping results highly accurate.\n3.\nWe develop two sublist materialization techniques to improve index performance that allow trading off space vs. performance.\n4.\nIn a comprehensive experimental evaluation our approach is evaluated on the English Wikipedia and parts of the Internet Archive as two large-scale real-world datasets with versioned documents.\nThe remainder of this paper is organized as follows.\nThe presented work is put in context with related work in Section 2.\nWe delineate our model of a temporally versioned document collection in Section 3.\nWe present our time-travel inverted index in Section 4.\nBuilding on it, temporal coalescing is described in Section 5.\nIn Section 6 we describe principled techniques to improve index performance, before presenting the results of our experimental evaluation in Section 7.\n2.\nRELATED WORK\nWe can classify the related work mainly into the following two categories: (i) methods that deal explicitly with collections of versioned documents or temporal databases, and (ii) methods for reducing the index size by exploiting either the document-content overlap or by pruning portions of the index.\nWe briefly review work under these categories here.\nTo the best of our knowledge, there is very little prior work dealing with historical search over temporally versioned documents.\nAnick and Flynn [3], while pioneering this research, describe a help-desk system that supports historical queries.\nBurrows and Hisgen [10], in a patent description, delineate a method for indexing range-based values and mention its potential use for searching based on dates associated with documents.\nStack [29] reports practical experiences made when adapting the open source search-engine Nutch to search web archives.\nThis adaptation, however, does not provide the intended time-travel text search functionality.\nIn contrast, research in temporal databases has produced several index structures tailored for time-evolving databases; a comprehensive overview of the state-of-art is available in [28].\nUnlike the inverted file index, their applicability to text search is not well understood.\nMoving on to the second category of related work, Broder et al. [8] describe a technique that exploits large content overlaps between documents to achieve a reduction in index size.\nTheir technique makes strong assumptions about the structure of document overlaps rendering it inapplicable to our context.\nMore recent approaches by Hersovici et al. [17] and Zhang and Suel [34] exploit arbitrary content overlaps between documents to reduce index size.\nNone of the approaches, however, considers time explicitly or provides the desired time-travel text search functionality.\nStatic indexpruning techniques [11, 12] aim to reduce the effective index size, by removing portions of the index that are expected to have low impact on the query result.\nThey also do not consider temporal aspects of documents, and thus are technically quite different from our proposal despite having a shared goal of index-size reduction.\nIt should be noted that index-pruning techniques can be adapted to work along with the temporal text index we propose here.\n8.\nCONCLUSIONS\nIn this work we have developed an efficient solution for time-travel text search over temporally versioned document collections.\nExperiments on two real-world datasets showed that a combination of the proposed techniques can reduce index size by up to an order of magnitude while achieving nearly optimal performance and highly accurate results.\n.\nHow can we extend the approach for queries q [tb, te] specifying a time interval instead of a time point?\nHow can the described time-travel text search functionality enable or speed up text mining along the time axis (e.g., tracking sentiment changes in customer opinions)?"}}},{"rowIdx":92,"cells":{"id":{"kind":"string","value":"H-50"},"title":{"kind":"string","value":"An Outranking Approach for Rank Aggregation in Information Retrieval"},"abstract":{"kind":"string","value":"Research in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.). In this paper, we focus on the rank aggregation problem, also called data fusion problem, where rankings of documents, searched into the same collection and provided by multiple methods, are combined in order to produce a new ranking. In this context, we propose a rank aggregation method within a multiple criteria framework using aggregation mechanisms based on decision rules identifying positive and negative reasons for judging whether a document should get a better rank than another. We show that the proposed method deals well with the Information Retrieval distinctive features. Experimental results are reported showing that the suggested method performs better than the well-known CombSUM and CombMNZ operators."},"keyphrases":{"kind":"list like","value":["outrank approach","rank aggreg","inform retriev","data fusion problem","data fusion","decis rule","multipl criterion framework","metasearch engin","combsum and combmnz strategi","majoritarian method","ir model","multipl criterium approach","outrank method"],"string":"[\n \"outrank approach\",\n \"rank aggreg\",\n \"inform retriev\",\n \"data fusion problem\",\n \"data fusion\",\n \"decis rule\",\n \"multipl criterion framework\",\n \"metasearch engin\",\n \"combsum and combmnz strategi\",\n \"majoritarian method\",\n \"ir model\",\n \"multipl criterium approach\",\n \"outrank method\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","P","P","P","M","U","M","M","U","M","R"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"M\",\n \"U\",\n \"M\",\n \"M\",\n \"U\",\n \"M\",\n \"R\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"An Outranking Approach for Rank Aggregation in Information Retrieval Mohamed Farah Lamsade, Paris Dauphine University Place du Mal de Lattre de Tassigny 75775 Paris Cedex 16, France farah@lamsade.dauphine.fr Daniel Vanderpooten Lamsade, Paris Dauphine University Place du Mal de Lattre de Tassigny 75775 Paris Cedex 16, France vdp@lamsade.dauphine.fr ABSTRACT Research in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.).\nIn this paper, we focus on the rank aggregation problem, also called data fusion problem, where rankings of documents, searched into the same collection and provided by multiple methods, are combined in order to produce a new ranking.\nIn this context, we propose a rank aggregation method within a multiple criteria framework using aggregation mechanisms based on decision rules identifying positive and negative reasons for judging whether a document should get a better rank than another.\nWe show that the proposed method deals well with the Information Retrieval distinctive features.\nExperimental results are reported showing that the suggested method performs better than the well-known CombSUM and CombMNZ operators.\nCategories and Subject Descriptors: H.3.3 [Information Systems]: Information Search and Retrieval - Retrieval models.\nGeneral Terms: Algorithms, Measurement, Experimentation, Performance, Theory.\n1.\nINTRODUCTION A wide range of current Information Retrieval (IR) approaches are based on various search models (Boolean, Vector Space, Probabilistic, Language, etc. [2]) in order to retrieve relevant documents in response to a user request.\nThe result lists produced by these approaches depend on the exact definition of the relevance concept.\nRank aggregation approaches, also called data fusion approaches, consist in combining these result lists in order to produce a new and hopefully better ranking.\nSuch approaches give rise to metasearch engines in the Web context.\nWe consider, in the following, cases where only ranks are available and no other additional information is provided such as the relevance scores.\nThis corresponds indeed to the reality, where only ordinal information is available.\nData fusion is also relevant in other contexts, such as when the user writes several queries of his/her information need (e.g., a boolean query and a natural language query) [4], or when many document surrogates are available [16].\nSeveral studies argued that rank aggregation has the potential of combining effectively all the various sources of evidence considered in various input methods.\nFor instance, experiments carried out in [16], [30], [4] and [19] showed that documents which appear in the lists of the majority of the input methods are more likely to be relevant.\nMoreover, Lee [19] and Vogt and Cottrell [31] found that various retrieval approaches often return very different irrelevant documents, but many of the same relevant documents.\nBartell et al. [3] also found that rank aggregation methods improve the performances w.r.t. those of the input methods, even when some of them have weak individual performances.\nThese methods also tend to smooth out biases of the input methods according to Montague and Aslam [22].\nData fusion has recently been proved to improve performances for both the ad-hoc retrieval and categorization tasks within the TREC genomics track in 2005 [1].\nThe rank aggregation problem was addressed in various fields such as i) in social choice theory which studies voting algorithms which specify winners of elections or winners of competitions in tournaments [29], ii) in statistics when studying correlation between rankings, iii) in distributed databases when results from different databases must be combined [12], and iv) in collaborative filtering [23].\nMost current rank aggregation methods consider each input ranking as a permutation over the same set of items.\nThey also give rigid interpretation to the exact ranking of the items.\nBoth of these assumptions are rather not valid in the IR context, as will be shown in the following sections.\nThe remaining of the paper is organized as follows.\nWe first review current rank aggregation methods in Section 2.\nThen we outline the specificities of the data fusion problem in the IR context (Section 3).\nIn Section 4, we present a new aggregation method which is proven to best fit the IR context.\nExperimental results are presented in Section 5 and conclusions are provided in a final section.\n2.\nRELATED WORK As pointed out by Riker [25], we can distinguish two families of rank aggregation methods: positional methods which assign scores to items to be ranked according to the ranks they receive and majoritarian methods which are based on pairwise comparisons of items to be ranked.\nThese two families of methods find their roots in the pioneering works of Borda [5] and Condorcet [7], respectively, in the social choice literature.\n2.1 Preliminaries We first introduce some basic notations to present the rank aggregation methods in a uniform way.\nLet D = {d1, d2, ... , dnd } be a set of nd documents.\nA list or a ranking j is an ordering defined on Dj ⊆ D (j = 1, ... , n).\nThus, di j di means di `is ranked better than'' di in j.\nWhen Dj = D, j is said to be a full list.\nOtherwise, it is a partial list.\nIf di belongs to Dj, rj i denotes the rank or position of di in j.\nWe assume that the best answer (document) is assigned the position 1 and the worst one is assigned the position |Dj|.\nLet D be the set of all permutations on D or all subsets of D.\nA profile is a n-tuple of rankings PR = ( 1, 2, ... , n).\nRestricting PR to the rankings containing document di defines PRi.\nWe also call the number of rankings which contain document di the rank hits of di [19].\nThe rank aggregation or data fusion problem consists of finding a ranking function or mechanism Ψ (also called a social welfare function in the social choice theory terminology) defined by: Ψ : n D → D PR = ( 1, 2, ... , n) → σ = Ψ(PR) where σ is called a consensus ranking.\n2.2 Positional Methods 2.2.1 Borda Count This method [5] first assigns a score n j=1 rj i to each document di.\nDocuments are then ranked by increasing order of this score, breaking ties, if any, arbitrarily.\n2.2.2 Linear Combination Methods This family of methods basically combine scores of documents.\nWhen used for the rank aggregation problem, ranks are assumed to be scores or performances to be combined using aggregation operators such as the weighted sum or some variation of it [3, 31, 17, 28].\nFor instance, Callan et al. [6] used the inference networks model [30] to combine rankings.\nFox and Shaw [15] proposed several combination strategies which are CombSUM, CombMIN, CombMAX, CombANZ and CombMNZ.\nThe first three operators correspond to the sum, min and max operators, respectively.\nCombANZ and CombMNZ respectively divides and multiplies the CombSUM score by the rank hits.\nIt is shown in [19] that the CombSUM and CombMNZ operators perform better than the others.\nMetasearch engines such as SavvySearch and MetaCrawler use the CombSUM strategy to fuse rankings.\n2.2.3 Footrule Optimal Aggregation In this method, a consensus ranking minimizes the Spearman footrule distance from the input rankings [21].\nFormally, given two full lists j and j , this distance is given by F( j, j ) = nd i=1 |rj i − rj i |.\nIt extends to several lists as follows.\nGiven a profile PR and a consensus ranking σ, the Spearman footrule distance of σ to PR is given by F(σ, PR) = n j=1 F(σ, j).\nCook and Kress [8] proposed a similar method which consists in optimizing the distance D( j, j ) = 1 2 nd i,i =1 |rj i,i − rj i,i |, where rj i,i = rj i −rj i .\nThis formulation has the advantage that it considers the intensity of preferences.\n2.2.4 Probabilistic Methods This kind of methods assume that the performance of the input methods on a number of training queries is indicative of their future performance.\nDuring the training process, probabilities of relevance are calculated.\nFor subsequent queries, documents are ranked based on these probabilities.\nFor instance, in [20], each input ranking j is divided into a number of segments, and the conditional probability of relevance (R) of each document di depending on the segment k it occurs in, is computed, i.e. prob(R|di, k, j).\nFor subsequent queries, the score of each document di is given by n j=1 prob(R|di,k, j ) k .\nLe Calve and Savoy [18] suggest using a logistic regression approach for combining scores.\nTraining data is needed to infer the model parameters.\n2.3 Majoritarian Methods 2.3.1 Condorcet Procedure The original Condorcet rule [7] specifies that a winner of the election is any item that beats or ties with every other item in a pairwise contest.\nFormally, let C(diσdi ) = { j∈ PR : di j di } be the coalition of rankings that are concordant with establishing diσdi , i.e. with the proposition di `should be ranked better than'' di in the final ranking σ.\ndi beats or ties with di iff |C(diσdi )| ≥ |C(di σdi)|.\nThe repetitive application of the Condorcet algorithm can produce a ranking of items in a natural way: select the Condorcet winner, remove it from the lists, and repeat the previous two steps until there are no more documents to rank.\nSince there is not always Condorcet winners, variations of the Condorcet procedure have been developed within the multiple criteria decision aid theory, with methods such as ELECTRE [26].\n2.3.2 Kemeny Optimal Aggregation As in section 2.2.3, a consensus ranking minimizes a geometric distance from the input rankings, where the Kendall tau distance is used instead of the Spearman footrule distance.\nFormally, given two full lists j and j , the Kendall tau distance is given by K( j, j ) = |{(di, di ) : i < i , rj i < rj i , rj i > rj i }|, i.e. the number of pairwise disagreements between the two lists.\nIt is easy to show that the consensus ranking corresponds to the geometric median of the input rankings and that the Kemeny optimal aggregation problem corresponds to the minimum feedback edge set problem.\n2.3.3 Markov Chain Methods Markov chains (MCs) have been used by Dwork et al. [11] as a `natural'' method to obtain a consensus ranking where states correspond to the documents to be ranked and the transition probabilities vary depending on the interpretation of the transition event.\nIn the same reference, the authors proposed four specific MCs and experimental testing had shown that the following MC is the best performing one (see also [24]): • MC4: move from the current state di to the next state di by first choosing a document di uniformly from D.\nIf for the majority of the rankings, we have rj i ≤ rj i , then move to di , else stay in di.\nThe consensus ranking corresponds to the stationary distribution of MC4.\n3.\nSPECIFICITIES OF THE RANK AGGREGATION PROBLEM IN THE IR CONTEXT 3.1 Limited Significance of the Rankings The exact positions of documents in one input ranking have limited significance and should not be overemphasized.\nFor instance, having three relevant documents in the first three positions, any perturbation of these three items will have the same value.\nIndeed, in the IR context, the complete order provided by an input method may hide ties.\nIn this case, we call such rankings semi orders.\nThis was outlined in [13] as the problem of aggregation with ties.\nIt is therefore important to build the consensus ranking based on robust information: • Documents with near positions in j are more likely to have similar interest or relevance.\nThus a slight perturbation of the initial ranking is meaningless.\n• Assuming that document di is better ranked than document di in a ranking j, di is more likely to be definitively more relevant than di in j when the number of intermediate positions between di and di increases.\n3.2 Partial Lists In real world applications, such as metasearch engines, rankings provided by the input methods are often partial lists.\nThis was outlined in [14] as the problem of having to merge top-k results from various input lists.\nFor instance, in the experiments carried out by Dwork et al. [11], authors found that among the top 100 best documents of 7 input search engines, 67% of the documents were present in only one search engine, whereas less than two documents were present in all the search engines.\nRank aggregation of partial lists raises four major difficulties which we state hereafter, proposing for each of them various working assumptions: 1.\nPartial lists can have various lengths, which can favour long lists.\nWe thus consider the following two working hypotheses: H1 k : We only consider the top k best documents from each input ranking.\nH1 all: We consider all the documents from each input ranking.\n2.\nSince there are different documents in the input rankings, we must decide which documents should be kept in the consensus ranking.\nTwo working hypotheses are therefore considered: H2 k : We only consider documents which are present in at least k input rankings (k > 1).\nH2 all: We consider all the documents which are ranked in at least one input ranking.\nHereafter, we call documents which will be retained in the consensus ranking, candidate documents, and documents that will be excluded from the consensus ranking, excluded documents.\nWe also call a candidate document which is missing in one or more rankings, a missing document.\n3.\nSome candidate documents are missing documents in some input rankings.\nMain reasons for a missing document are that it was not indexed or it was indexed but deemed irrelevant ; usually this information is not available.\nWe consider the following two working hypotheses: H3 yes: Each missing document in each j is assigned a position.\nH3 no: No assumption is made, that is each missing document is considered neither better nor worse than any other document.\n4.\nWhen assumption H2 k holds, each input ranking may contain documents which will not be considered in the consensus ranking.\nRegarding the positions of the candidate documents, we can consider the following working hypotheses: H4 init: The initial positions of candidate documents are kept in each input ranking.\nH4 new: Candidate documents receive new positions in each input ranking, after discarding excluded ones.\nIn the IR context, rank aggregation methods need to decide more or less explicitly which assumptions to retain w.r.t. the above-mentioned difficulties.\n4.\nOUTRANKING APPROACH FOR RANK AGGREGATION 4.1 Presentation Positional methods consider implicitly that the positions of the documents in the input rankings are scores giving thus a cardinal meaning to an ordinal information.\nThis constitutes a strong assumption that is questionable, especially when the input rankings have different lengths.\nMoreover, for positional methods, assumptions H3 and H4 , which are often arbitrary, have a strong impact on the results.\nFor instance, let us consider an input ranking of 500 documents out of 1000 candidate documents.\nWhether we assign to each of the missing documents the position 1, 501, 750 or 1000 -corresponding to variations of H3 yes- will give rise to very contrasted results, especially regarding the top of the consensus ranking.\nMajoritarian methods do not suffer from the above-mentioned drawbacks of the positional methods since they build consensus rankings exploiting only ordinal information contained in the input rankings.\nNevertheless, they suppose that such rankings are complete orders, ignoring that they may hide ties.\nTherefore, majoritarian methods base consensus rankings on illusory discriminant information rather than less discriminant but more robust information.\nTrying to overcome the limits of current rank aggregation methods, we found that outranking approaches, which were initially used for multiple criteria aggregation problems [26], can also be used for the rank aggregation purpose, where each ranking plays the role of a criterion.\nTherefore, in order to decide whether a document di should be ranked better than di in the consensus ranking σ, the two following conditions should be met: • a concordance condition which ensures that a majority of the input rankings are concordant with diσdi (majority principle).\n• a discordance condition which ensures that none of the discordant input rankings strongly refutes dσd (respect of minorities principle).\nFormally, the concordance coalition with diσdi is Csp (diσdi ) = { j∈ PR : rj i ≤ rj i − sp} where sp is a preference threshold which is the variation of document positions -whether it is absolute or relative to the ranking length- which draws the boundaries between an indifference and a preference situation between documents.\nThe discordance coalition with diσdi is Dsv (diσdi ) = { j∈ PR : rj i ≥ rj i + sv} where sv is a veto threshold which is the variation of document positions -whether it is absolute or relative to the ranking length- which draws the boundaries between a weak and a strong opposition to diσdi .\nDepending on the exact definition of the preceding concordance and discordance coalitions leading to the definition of some decision rules, several outranking relations can be defined.\nThey can be more or less demanding depending on i) the values of the thresholds sp and sv, ii) the importance or minimal size cmin required for the concordance coalition, and iii) the importance or maximum size dmax of the discordance coalition.\nA generic outranking relation can thus be defined as follows: diS(sp,sv,cmin,dmax)di ⇔ |Csp (diσdi )| ≥ cmin AND |Dsv (diσdi )| ≤ dmax This expression defines a family of nested outranking relations since S(sp,sv,cmin,dmax) ⊆ S(sp,sv,cmin,dmax) when cmin ≥ cmin and/or dmax ≤ dmax and/or sp ≥ sp and/or sv ≤ sv.\nThis expression also generalizes the majority rule which corresponds to the particular relation S(0,∞, n 2 ,n).\nIt also satisfies important properties of rank aggregation methods, called neutrality, Pareto-optimality, Condorcet property and Extended Condorcet property, in the social choice literature [29].\nOutranking relations are not necessarily transitive and do not necessarily correspond to rankings since directed cycles may exist.\nTherefore, we need specific procedures in order to derive a consensus ranking.\nWe propose the following procedure which finds its roots in [27].\nIt consists in partitioning the set of documents into r ranked classes.\nEach class Ch contains documents with the same relevance and results from the application of all relations (if possible) to the set of documents remaining after previous classes are computed.\nDocuments within the same equivalence class are ranked arbitrarily.\nFormally, let • R be the set of candidate documents for a query, • S1 , S2 , ... be a family of nested outranking relations, • Fk(di, E) = |{di ∈ E : diSk di }| be the number of documents in E(E ⊆ R) that could be considered `worse'' than di according to relation Sk , • fk(di, E) = |{di ∈ E : di Sk di}| be the number of documents in E that could be considered `better'' than di according to Sk , • sk(di, E) = Fk(di, E) − fk(di, E) be the qualification of di in E according to Sk .\nEach class Ch results from a distillation process.\nIt corresponds to the last distillate of a series of sets E0 ⊇ E1 ⊇ ... where E0 = R \\ (C1 ∪ ... ∪ Ch−1) and Ek is a reduced subset of Ek−1 resulting from the application of the following procedure: 1.\ncompute for each di ∈ Ek−1 its qualification according to Sk , i.e. sk(di, Ek−1), 2.\ndefine smax = maxdi∈Ek−1 {sk(di, Ek−1)}, then 3.\nEk = {di ∈ Ek−1 : sk(di, Ek−1) = smax} When one outranking relation is used, the distillation process stops after the first application of the previous procedure, i.e., Ch corresponds to distillate E1.\nWhen different outranking relations are used, the distillation process stops when all the pre-defined outranking relations have been used or when |Ek| = 1.\n4.2 Illustrative Example This section illustrates the concepts and procedures of section 4.1.\nLet us consider a set of candidate documents R = {d1, d2, d3, d4, d5}.\nThe following table gives a profile PR of different rankings of the documents of R: PR = ( 1 , 2, 3, 4).\nTable 1: Rankings of documents rj i 1 2 3 4 d1 1 3 1 5 d2 2 1 3 3 d3 3 2 2 1 d4 4 4 5 2 d5 5 5 4 4 Let us suppose that the preference and veto thresholds are set to values 1 and 4 respectively, and that the concordance and discordance thresholds are set to values 2 and 1 respectively.\nThe following tables give the concordance, discordance and outranking matrices.\nEach entry csp (di, di ) (dsv (di, di )) in the concordance (discordance) matrix gives the number of rankings that are concordant (discordant) with diσdi , i.e. csp (di, di ) = |Csp (diσdi )| and dsv (di, di ) = |Dsv (diσdi )|.\nTable 2: Computation of the outranking relation d1 d2 d3 d4 d5 d1 - 2 2 3 3 d2 2 - 2 3 4 d3 2 2 - 4 4 d4 1 1 0 - 3 d5 1 0 0 1Concordance Matrix d1 d2 d3 d4 d5 d1 - 0 1 0 0 d2 0 - 0 0 0 d3 0 0 - 0 0 d4 1 0 0 - 0 d5 1 1 0 0Discordance Matrix d1 d2 d3 d4 d5 d1 - 1 1 1 1 d2 1 - 1 1 1 d3 1 1 - 1 1 d4 0 0 0 - 1 d5 0 0 0 0Outranking Matrix (S1) For instance, the concordance coalition for the assertion d1σd4 is C1(d1σd4) = { 1, 2, 3} and the discordance coalition for the same assertion is D4(d1σd4) = ∅.\nTherefore, c1(d1, d4) = 3, d4(d1, d4) = 0 and d1S1 d4 holds.\nNotice that Fk(di, R) (fk(di, R)) is given by summing the values of the ith row (column) of the outranking matrix.\nThe consensus ranking is obtained as follows: to get the first class C1, we compute the qualifications of all the documents of E0 = R with respect to S1 .\nThey are respectively 2, 2, 2, -2 and -4.\nTherefore smax equals 2 and C1 = E1 = {d1, d2, d3}.\nObserve that, if we had used a second outranking relation S2(⊇ S1), these three documents could have been possibly discriminated.\nAt this stage, we remove documents of C1 from the outranking matrix and compute the next class C2: we compute the new qualifications of the documents of E0 = R \\ C1 = {d4, d5}.\nThey are respectively 1 and -1.\nSo C3 = E1 = {d4}.\nThe last document d5 is the only document of the last class C3.\nThus, the consensus ranking is {d1, d2, d3} → {d4} → {d5}.\n5.\nEXPERIMENTS AND RESULTS 5.1 Test Setting To facilitate empirical investigation of the proposed methodology, we developed a prototype metasearch engine that implements a version of our outranking approach for rank aggregation.\nIn this paper, we apply our approach to the Topic Distillation (TD) task of TREC-2004 Web track [10].\nIn this task, there are 75 topics where only a short description of each is given.\nFor each query, we retained the rankings of the 10 best runs of the TD task which are provided by TREC-2004 participating teams.\nThe performances of these runs are reported in table 3.\nTable 3: Performances of the 10 best runs of the TD task of TREC-2004 Run Id MAP P@10 S@1 S@5 S@10 uogWebCAU150 17.9% 24.9% 50.7% 77.3% 89.3% MSRAmixed1 17.8% 25.1% 38.7% 72.0% 88.0% MSRC04C12 16.5% 23.1% 38.7% 74.7% 80.0% humW04rdpl 16.3% 23.1% 37.3% 78.7% 90.7% THUIRmix042 14.7% 20.5% 21.3% 58.7% 74.7% UAmsT04MWScb 14.6% 20.9% 36.0% 66.7% 76.0% ICT04CIIS1AT 14.1% 20.8% 33.3% 64.0% 78.7% SJTUINCMIX5 12.9% 18.9% 29.3% 57.3% 72.0% MU04web1 11.5% 19.9% 33.3% 64.0% 76.0% MeijiHILw3 11.5% 15.3% 30.7% 54.7% 64.0% Average 14.7% 21.2% 34.9% 66.8% 78.94% For each query, each run provides a ranking of about 1000 documents.\nThe number of documents retrieved by all these runs ranges from 543 to 5769.\nTheir average (median) number is 3340 (3386).\nIt is worth noting that we found similar distributions of the documents among the rankings as in [11].\nFor evaluation, we used the `trec eval'' standard tool which is used by the TREC community to calculate the standard measures of system effectiveness which are Mean Average Precision (MAP) and Success@n (S@n) for n=1, 5 and 10.\nOur approach effectiveness is compared against some high performing official results from TREC-2004 as well as against some standard rank aggregation algorithms.\nIn the experiments, significance testing is mainly based on the t-student statistic which is computed on the basis of the MAP values of the compared runs.\nIn the tables of the following section, statistically significant differences are marked with an asterisk.\nValues between brackets of the first column of each table, indicate the parameter value of the corresponding run.\n5.2 Results We carried out several series of runs in order to i) study performance variations of the outranking approach when tuning the parameters and working assumptions, ii) compare performances of the outranking approach vs standard rank aggregation strategies , and iii) check whether rank aggregation performs better than the best input rankings.\nWe set our basic run mcm with the following parameters.\nWe considered that each input ranking is a complete order (sp = 0) and that an input ranking strongly refutes diσdi when the difference of both document positions is large enough (sv = 75%).\nPreference and veto thresholds are computed proportionally to the number of documents retained in each input ranking.\nThey consequently may vary from one ranking to another.\nIn addition, to accept the assertion diσdi , we supposed that the majority of the rankings must be concordant (cmin = 50%) and that every input ranking can impose its veto (dmax = 0).\nConcordance and discordance thresholds are computed for each tuple (di, di ) as the percentage of the input rankings of PRi ∩PRi .\nThus, our choice of parameters leads to the definition of the outranking relation S(0,75%,50%,0).\nTo test the run mcm, we had chosen the following assumptions.\nWe retained the top 100 best documents from each input ranking (H1 100), only considered documents which are present in at least half of the input rankings (H2 5 ) and assumed H3 no and H4 new.\nIn these conditions, the number of successful documents was about 100 on average, and the computation time per query was less than one second.\nObviously, modifying the working assumptions should have deeper impact on the performances than tuning our model parameters.\nThis was validated by preliminary experiments.\nThus, we hereafter begin by studying performance variation when different sets of assumptions are considered.\nAfterwards, we study the impact of tuning parameters.\nFinally, we compare our model performances w.r.t. the input rankings as well as some standard data fusion algorithms.\n5.2.1 Impact of the Working Assumptions Table 4 summarizes the performance variation of the outranking approach under different working hypotheses.\nIn Table 4: Impact of the working assumptions Run Id MAP S@1 S@5 S@10 mcm 18.47% 41.33% 81.33% 86.67% mcm22 (H3 yes) 17.72% (-4.06%) 34.67% 81.33% 86.67% mcm23 (H4 init) 18.26% (-1.14%) 41.33% 81.33% 86.67% mcm24 (H1 all) 20.67% (+11.91%*) 38.66% 80.00% 86.66% mcm25 (H2 all) 21.68% (+17.38%*) 40.00% 78.66% 89.33% this table, we first show that run mcm22, in which missing documents are all put in the same last position of each input ranking, leads to performance drop w.r.t. run mcm.\nMoreover, S@1 moves from 41.33% to 34.67% (-16.11%).\nThis shows that several relevant documents which were initially put at the first position of the consensus ranking in mcm, lose this first position but remain ranked in the top 5 documents since S@5 did not change.\nWe also conclude that documents which have rather good positions in some input rankings are more likely to be relevant, even though they are missing in some other rankings.\nConsequently, when they are missing in some rankings, assigning worse ranks to these documents is harmful for performance.\nAlso, from Table 4, we found that the performances of runs mcm and mcm23 are similar.\nTherefore, the outranking approach is not sensitive to keeping the initial positions of candidate documents or recomputing them by discarding excluded ones.\nFrom the same Table 4, performance of the outranking approach increases significantly for runs mcm24 and mcm25.\nTherefore, whether we consider all the documents which are present in half of the rankings (mcm24) or we consider all the documents which are ranked in the first 100 positions in one or more rankings (mcm25), increases performances.\nThis result was predictable since in both cases we have more detailed information on the relative importance of documents.\nTables 5 and 6 confirm this evidence.\nTable 5, where values between brackets of the first column give the number of documents which are retained from each input ranking, shows that selecting more documents from each input ranking leads to performance increase.\nIt is worth mentioning that selecting more than 600 documents from each input ranking does not improve performance.\nTable 5: Impact of the number of retained documents Run Id MAP S@1 S@5 S@10 mcm (100) 18.47% 41.33% 81.33% 86.67% mcm24-1 (200) 19.32% (+4.60%) 42.67% 78.67% 88.00% mcm24-2 (400) 19.88% (+7.63%*) 37.33% 80.00% 88.00% mcm24-3 (600) 20.80% (+12.62%*) 40.00% 80.00% 88.00% mcm24-4 (800) 20.66% (+11.86%*) 40.00% 78.67% 86.67% mcm24 (1000) 20.67% (+11.91%*) 38.66% 80.00% 86.66% Table 6 reports runs corresponding to variations of H2 k .\nValues between brackets are rank hits.\nFor instance, in the run mcm32, only documents which are present in 3 or more input rankings, were considered successful.\nThis table shows that performance is significantly better when rare documents are considered, whereas it decreases significantly when these documents are discarded.\nTherefore, we conclude that many of the relevant documents are retrieved by a rather small set of IR models.\nTable 6: Performance considering different rank hits Run Id MAP S@1 S@5 S@10 mcm25 (1) 21.68% (+17.38%*) 40.00% 78.67% 89.33% mcm32 (3) 18.98% (+2.76%) 38.67% 80.00% 85.33% mcm (5) 18.47% 41.33% 81.33% 86.67% mcm33 (7) 15.83% (-14.29%*) 37.33% 78.67% 85.33% mcm34 (9) 10.96% (-40.66%*) 36.11% 66.67% 70.83% mcm35 (10) 7.42% (-59.83%*) 39.22% 62.75% 64.70% For both runs mcm24 and mcm25, the number of successful documents was about 1000 and therefore, the computation time per query increased and became around 5 seconds.\n5.2.2 Impact of the Variation of the Parameters Table 7 shows performance variation of the outranking approach when different preference thresholds are considered.\nWe found performance improvement up to threshold values of about 5%, then there is a decrease in the performance which becomes significant for threshold values greater than 10%.\nMoreover, S@1 improves from 41.33% to 46.67% when preference threshold changes from 0 to 5%.\nWe can thus conclude that the input rankings are semi orders rather than complete orders.\nTable 8 shows the evolution of the performance measures w.r.t. the concordance threshold.\nWe can conclude that in order to put document di before di in the consensus ranking, Table 7: Impact of the variation of the preference threshold from 0 to 12.5% Run Id MAP S@1 S@5 S@10 mcm (0%) 18.47% 41.33% 81.33% 86.67% mcm1 (1%) 18.57% (+0.54%) 41.33% 81.33% 86.67% mcm2 (2.5%) 18.63% (+0.87%) 42.67% 78.67% 86.67% mcm3 (5%) 18.69% (+1.19%) 46.67% 81.33% 86.67% mcm4 (7.5%) 18.24% (-1.25%) 46.67% 81.33% 86.67% mcm5 (10%) 17.93% (-2.92%) 40.00% 82.67% 86.67% mcm5b (12.5%) 17.51% (-5.20%*) 41.33% 80.00% 86.67% at least half of the input rankings of PRi ∩ PRi should be concordant.\nPerformance drops significantly for very low and very high values of the concordance threshold.\nIn fact, for such values, the concordance condition is either fulfilled rather always by too many document pairs or not fulfilled at all, respectively.\nTherefore, the outranking relation becomes either too weak or too strong respectively.\nTable 8: Impact of the variation of cmin Run Id MAP S@1 S@5 S@10 mcm11 (20%) 17.63% (-4.55%*) 41.33% 76.00% 85.33% mcm12 (40%) 18.37% (-0.54%) 42.67% 76.00% 86.67% mcm (50%) 18.47% 41.33% 81.33% 86.67% mcm13 (60%) 18.42% (-0.27%) 40.00% 78.67% 86.67% mcm14 (80%) 17.43% (-5.63%*) 40.00% 78.67% 86.67% mcm15 (100%) 16.12% (-12.72%*) 41.33% 70.67% 85.33% In the experiments, varying the veto threshold as well as the discordance threshold within reasonable intervals does not have significant impact on performance measures.\nIn fact, runs with different veto thresholds (sv ∈ [50%; 100%]) had similar performances even though there is a slight advantage for runs with high threshold values which means that it is better not to allow the input rankings to put their veto easily.\nAlso, tuning the discordance threshold was carried out for values 50% and 75% of the veto threshold.\nFor these runs we did not get any noticeable performance variation, although for low discordance thresholds (dmax < 20%), performance slightly decreased.\n5.2.3 Impact of the Variation of the Number of Input Rankings To study performance evolution when different sets of input rankings are considered, we carried three more runs where 2, 4, and 6 of the best performing sets of the input rankings are considered.\nResults reported in Table 9 are seemingly counter-intuitive and also do not support previous findings regarding rank aggregation research [3].\nNevertheless, this result shows that low performing rankings bring more noise than information to the establishment of the consensus ranking.\nTherefore, when they are considered, performance decreases.\nTable 9: Performance considering different best performing sets of input rankings Run Id MAP S@1 S@5 S@10 mcm (10) 18.47% 41.33% 81.33% 86.67% mcm27 (6) 18.60% (+0.70%) 41.33% 80.00% 85.33% mcm28 (4) 19.02% (+2.98%) 40.00% 86.67% 88.00% mcm29 (2) 18.33% (-0.76%) 44.00% 76.00% 88.00% 5.2.4 Comparison of the Performance of Different Rank Aggregation Methods In this set of runs, we compare the outranking approach with some standard rank aggregation methods which were proven to have acceptable performance in previous studies: we considered two positional methods which are the CombSUM and the CombMNZ strategies.\nWe also examined the performance of one majoritarian method which is the Markov chain method (MC4).\nFor the comparisons, we considered a specific outranking relation S∗ = S(5%,50%,50%,30%) which results in good overall performances when tuning all the parameters.\nThe first row of Table 10 gives performances of the rank aggregation methods w.r.t. a basic assumption set A1 = (H1 100, H2 5 , H4 new): we only consider the 100 first documents from each ranking, then retain documents present in 5 or more rankings and update ranks of successful documents.\nFor positional methods, we place missing documents at the queue of the ranking (H3 yes) whereas for our method as well as for MC4, we retained hypothesis H3 no.\nThe three following rows of Table 10 report performances when changing one element from the basic assumption set: the second row corresponds to the assumption set A2 = (H1 1000, H2 5 , H4 new), i.e. changing the number of retained documents from 100 to 1000.\nThe third row corresponds to the assumption set A3 = (H1 100, H2 all, H4 new), i.e. considering the documents present in at least one ranking.\nThe fourth row corresponds to the assumption set A4 = (H1 100, H2 5 , H4 init), i.e. keeping the original ranks of successful documents.\nThe fifth row of Table 10, labeled A5, gives performance when all the 225 queries of the Web track of TREC-2004 are considered.\nObviously, performance level cannot be compared with previous lines since the additional queries are different from the TD queries and correspond to other tasks (Home Page and Named Page tasks [10]) of TREC-2004 Web track.\nThis set of runs aims to show whether relative performance of the various methods is task-dependent.\nThe last row of Table 10, labeled A6, reports performance of the various methods considering the TD task of TREC2002 instead of TREC-2004: we fused the results of input rankings of the 10 best official runs for each of the 50 TD queries [9] considering the set of assumptions A1 of the first row.\nThis aims to show whether relative performance of the various methods changes from year to year.\nValues between brackets of Table 10 are variations of performance of each rank aggregation method w.r.t. performance of the outranking approach.\nTable 10: Performance (MAP) of different rank aggregation methods under 3 different test collections mcm combSUM combMNZ markov A1 18.79% 17.54% (-6.65%*) 17.08% (-9.10%*) 18.63% (-0.85%) A2 21.36% 19.18% (-10.21%*) 18.61% (-12.87%*) 21.33% (-0.14%) A3 21.92% 21.38% (-2.46%) 20.88% (-4.74%) 19.35% (-11.72%*) A4 18.64% 17.58% (-5.69%*) 17.18% (-7.83%*) 18.63% (-0.05%) A5 55.39% 52.16% (-5.83%*) 49.70% (-10.27%*) 53.30% (-3.77%) A6 16.95% 15.65% (-7.67%*) 14.57% (-14.04%*) 16.39% (-3.30%) From the analysis of table 10 the following can be established: • for all the runs, considering all the documents in each input ranking (A2) significantly improves performance (MAP increases by 11.62% on average).\nThis is predictable since some initially unreported relevant documents would receive better positions in the consensus ranking.\n• for all the runs, considering documents even those present in only one input ranking (A3) significantly improves performance.\nFor mcm, combSUM and combMNZ, performance improvement is more important (MAP increases by 20.27% on average) than for the markov run (MAP increases by 3.86%).\n• preserving the initial positions of documents (A4) or recomputing them (A1) does not have a noticeable influence on performance for both positional and majoritarian methods.\n• considering all the queries of the Web track of TREC2004 (A5) as well as the TD queries of the Web track of TREC-2002 (A6) does not alter the relative performance of the different data fusion methods.\n• considering the TD queries of the Web track of TREC2002, performances of all the data fusion methods are lower than that of the best performing input ranking for which the MAP value equals 18.58%.\nThis is because most of the fused input rankings have very low performances compared to the best one, which brings more noise to the consensus ranking.\n• performances of the data fusion methods mcm and markov are significantly better than that of the best input ranking uogWebCAU150.\nThis remains true for runs combSUM and combMNZ only under assumptions H1 all or H2 all.\nThis shows that majoritarian methods are less sensitive to assumptions than positional methods.\n• outranking approach always performs significantly better than positional methods combSUM and combMNZ.\nIt has also better performances than the Markov chain method, especially under assumption H2 all where difference of performances becomes significant.\n6.\nCONCLUSIONS In this paper, we address the rank aggregation problem where different, but not disjoint, lists of documents are to be fused.\nWe noticed that the input rankings can hide ties, so they should not be considered as complete orders.\nOnly robust information should be used from each input ranking.\nCurrent rank aggregation methods, and especially positional methods (e.g. combSUM [15]), are not initially designed to work with such rankings.\nThey should be adapted by considering specific working assumptions.\nWe propose a new outranking method for rank aggregation which is well adapted to the IR context.\nIndeed, it ranks two documents w.r.t. the intensity of their positions difference in each input ranking and also considering the number of the input rankings that are concordant and discordant in favor of a specific document.\nThere is also no need to make specific assumptions on the positions of the missing documents.\nThis is an important feature since the absence of a document from a ranking should not be necessarily interpreted negatively.\nExperimental results show that the outranking method significantly out-performs popular classical positional data fusion methods like combSUM and combMNZ strategies.\nIt also out-performs a good performing majoritarian methods which is the Markov chain method.\nThese results are tested against different test collections and queries.\nFrom the experiments, we can also conclude that in order to improve the performances, we should fuse result lists of well performing IR models, and that majoritarian data fusion methods perform better than positional methods.\nThe proposed method can have a real impact on Web metasearch performances since only ranks are available from most primary search engines, whereas most of the current approaches need scores to merge result lists into one single list.\nFurther work involves investigating whether the outranking approach performs well in various other contexts, e.g. using the document scores or some combination of document ranks and scores.\nAcknowledgments The authors would like to thank Jacques Savoy for his valuable comments on a preliminary version of this paper.\n7.\nREFERENCES [1] A. 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Hugonnard.\nRanking of suburban line extension projects on the Paris metro system by a multicriteria method.\nTransportation Research, 16A(4):301-312, 1982.\n[28] L. Si and J. Callan.\nUsing sampled data and regression to merge search engine results.\nIn Proceedings ACM-SIGIR``2002, pages 19-26.\nACM Press, 2002.\n[29] M. Truchon.\nAn extension of the Condorcet criterion and Kemeny orders.\nCahier 9813, Centre de Recherche en Economie et Finance Appliqu´ees, Oct. 1998.\n[30] H. Turtle and W. B. Croft.\nInference networks for document retrieval.\nIn Proceedings of ACM-SIGIR``90, pages 1-24.\nACM Press, 1990.\n[31] C. C. Vogt and G. W. Cottrell.\nFusion via a linear combination of scores.\nInformation Retrieval, 1(3):151-173, 1999."},"lvl-2":{"kind":"string","value":"An Outranking Approach for Rank Aggregation in Information Retrieval\nABSTRACT\nResearch in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.).\nIn this paper, we focus on the rank aggregation problem, also called data fusion problem, where rankings of documents, searched into the same collection and provided by multiple methods, are combined in order to produce a new ranking.\nIn this context, we propose a rank aggregation method within a multiple criteria framework using aggregation mechanisms based on decision rules identifying positive and negative reasons for judging whether a document should get a better rank than another.\nWe show that the proposed method deals well with the Information Retrieval distinctive features.\nExperimental results are reported showing that the suggested method performs better than the well-known CombSUM and CombMNZ operators.\n1.\nINTRODUCTION\nA wide range of current Information Retrieval (IR) approaches are based on various search models (Boolean, Vector Space, Probabilistic, Language, etc. [2]) in order to retrieve relevant documents in response to a user request.\nThe result lists produced by these approaches depend on the exact definition of the relevance concept.\nRank aggregation approaches, also called data fusion approaches, consist in combining these result lists in order to produce a new and hopefully better ranking.\nSuch approaches give rise to metasearch engines in the Web context.\nWe consider, in the following, cases where only ranks are\navailable and no other additional information is provided such as the relevance scores.\nThis corresponds indeed to the reality, where only ordinal information is available.\nData fusion is also relevant in other contexts, such as when the user writes several queries of his/her information need (e.g., a boolean query and a natural language query) [4], or when many document surrogates are available [16].\nSeveral studies argued that rank aggregation has the potential of combining effectively all the various sources of evidence considered in various input methods.\nFor instance, experiments carried out in [16], [30], [4] and [19] showed that documents which appear in the lists of the majority of the input methods are more likely to be relevant.\nMoreover, Lee [19] and Vogt and Cottrell [31] found that various retrieval approaches often return very different irrelevant documents, but many of the same relevant documents.\nBartell et al. [3] also found that rank aggregation methods improve the performances w.r.t. those of the input methods, even when some of them have weak individual performances.\nThese methods also tend to smooth out biases of the input methods according to Montague and Aslam [22].\nData fusion has recently been proved to improve performances for both the ad hoc retrieval and categorization tasks within the TREC genomics track in 2005 [1].\nThe rank aggregation problem was addressed in various fields such as i) in social choice theory which studies voting algorithms which specify winners of elections or winners of competitions in tournaments [29], ii) in statistics when studying correlation between rankings, iii) in distributed databases when results from different databases must be combined [12], and iv) in collaborative filtering [23].\nMost current rank aggregation methods consider each input ranking as a permutation over the same set of items.\nThey also give rigid interpretation to the exact ranking of the items.\nBoth of these assumptions are rather not valid in the IR context, as will be shown in the following sections.\nThe remaining of the paper is organized as follows.\nWe first review current rank aggregation methods in Section 2.\nThen we outline the specificities of the data fusion problem in the IR context (Section 3).\nIn Section 4, we present a new aggregation method which is proven to best fit the IR context.\nExperimental results are presented in Section 5 and conclusions are provided in a final section.\n2.\nRELATED WORK\nAs pointed out by Riker [25], we can distinguish two families of rank aggregation methods: positional methods which assign scores to items to be ranked according to the ranks\nthey receive and majoritarian methods which are based on pairwise comparisons of items to be ranked.\nThese two families of methods find their roots in the pioneering works of Borda [5] and Condorcet [7], respectively, in the social choice literature.\n2.1 Preliminaries\nWe first introduce some basic notations to present the rank aggregation methods in a uniform way.\nLet D = {d1, d2,..., dnd} be a set of nd documents.\nA list or a ranking ~ j is an ordering defined on Dj ⊆ D (j = 1,..., n).\nThus, di ~ j di, means di ` is ranked better than' di, in ~ j.\nWhen Dj = D, ~ j is said to be a full list.\nOtherwise, it is a partial list.\nIf di belongs to Dj, rji denotes the rank or position of di in ~ j.\nWe assume that the best answer (document) is assigned the position 1 and the worst one is assigned the position | Dj |.\nLet ~ D be the set of all permutations on D or all subsets of D.\nA profile is a n-tuple of rankings PR = (~ 1, ~ 2,..., ~ n).\nRestricting PR to the rankings containing document di defines PRi.\nWe also call the number of rankings which contain document di the rank hits of di [19].\nThe rank aggregation or data fusion problem consists of finding a ranking function or mechanism Ψ (also called a social welfare function in the social choice theory terminology) defined by:\nwhere σ is called a consensus ranking.\n2.2 Positional Methods\n2.2.1 Borda Count\nThis method [5] first assigns a score Enj = 1 rji to each document di.\nDocuments are then ranked by increasing order of this score, breaking ties, if any, arbitrarily.\n2.2.2 Linear Combination Methods\nThis family of methods basically combine scores of documents.\nWhen used for the rank aggregation problem, ranks are assumed to be scores or performances to be combined using aggregation operators such as the weighted sum or some variation of it [3, 31, 17, 28].\nFor instance, Callan et al. [6] used the inference networks model [30] to combine rankings.\nFox and Shaw [15] proposed several combination strategies which are CombSUM, CombMIN, CombMAX, CombANZ and CombMNZ.\nThe first three operators correspond to the sum, min and max operators, respectively.\nCombANZ and CombMNZ respectively divides and multiplies the CombSUM score by the rank hits.\nIt is shown in [19] that the CombSUM and CombMNZ operators perform better than the others.\nMetasearch engines such as SavvySearch and MetaCrawler use the CombSUM strategy to fuse rankings.\n2.2.3 Footrule Optimal Aggregation\nIn this method, a consensus ranking minimizes the Spearman footrule distance from the input rankings [21].\nFormally, given two full lists ~ j and ~ j,, this distance is given by F (~ j, ~ j,) = nd\nas follows.\nGiven a profile PR and a consensus ranking σ, the Spearman footrule distance of σ to PR is given by\ni, i, |, where rji, i, = rji, − rji.\nThis formulation has the advantage that it considers the intensity of preferences.\n2.2.4 Probabilistic Methods\nThis kind of methods assume that the performance of the input methods on a number of training queries is indicative of their future performance.\nDuring the training process, probabilities of relevance are calculated.\nFor subsequent queries, documents are ranked based on these probabilities.\nFor instance, in [20], each input ranking ~ j is divided into a number of segments, and the conditional probability of relevance (R) of each document di depending on the segment k it occurs in, is computed, i.e. prob (R | di, k, ~ j).\nFor subsequent queries, the score of each document di is given by En prob (R | di, k, Yj).\nLe Calve and Savoy [18] suggest using j = 1 k a logistic regression approach for combining scores.\nTraining data is needed to infer the model parameters.\n2.3 Majoritarian Methods\n2.3.1 Condorcet Procedure\nThe original Condorcet rule [7] specifies that a winner of the election is any item that beats or ties with every other item in a pairwise contest.\nFormally, let C (diσdi,) = {~ j ∈ PR: di ~ j di,} be the coalition of rankings that are concordant with establishing diσdi,, i.e. with the proposition di ` should be ranked better than' di, in the final ranking σ.\ndi beats or ties with di, iff | C (diσdi,) | ≥ | C (di, σdi) |.\nThe repetitive application of the Condorcet algorithm can produce a ranking of items in a natural way: select the Condorcet winner, remove it from the lists, and repeat the previous two steps until there are no more documents to rank.\nSince there is not always Condorcet winners, variations of the Condorcet procedure have been developed within the multiple criteria decision aid theory, with methods such as ELECTRE [26].\n2.3.2 Kemeny Optimal Aggregation\nAs in section 2.2.3, a consensus ranking minimizes a geometric distance from the input rankings, where the Kendall tau distance is used instead of the Spearman footrule distance.\nFormally, given two full lists ~ j and ~ j,, the Kendall tau distance is given by K (~ j, ~ j,) = | {(di, di,): i <i ~, rji <rji,, rj,\ni,} |, i.e. the number of pairwise disagreements between the two lists.\nIt is easy to show that the consensus ranking corresponds to the geometric median of the input rankings and that the Kemeny optimal aggregation problem corresponds to the minimum feedback edge set problem.\n2.3.3 Markov Chain Methods\nMarkov chains (MCs) have been used by Dwork et al. [11] as a ` natural' method to obtain a consensus ranking where states correspond to the documents to be ranked and the transition probabilities vary depending on the interpretation of the transition event.\nIn the same reference, the authors proposed four specific MCs and experimental testing had shown that the following MC is the best performing one (see also [24]):\n• MC4: move from the current state di to the next state di, by first choosing a document di, uniformly from D.\nIf for the majority of the rankings, we have rji, ≤ rji, then move to di,, else stay in di.\nThe consensus ranking corresponds to the stationary distribution of MC4.\n3.\nSPECIFICITIES OF THE RANK AGGREGATION PROBLEM IN THE IR CONTEXT\n3.1 Limited Significance of the Rankings\nThe exact positions of documents in one input ranking have limited significance and should not be overemphasized.\nFor instance, having three relevant documents in the first three positions, any perturbation of these three items will have the same value.\nIndeed, in the IR context, the complete order provided by an input method may hide ties.\nIn this case, we call such rankings semi orders.\nThis was outlined in [13] as the problem of aggregation with ties.\nIt is therefore important to build the consensus ranking based on robust information:\n• Documents with near positions in> - j are more likely to have similar interest or relevance.\nThus a slight perturbation of the initial ranking is meaningless.\n• Assuming that document di is better ranked than document di, in a ranking> - j, di is more likely to be definitively more relevant than di, in> - j when the number of intermediate positions between di and di, increases.\n3.2 Partial Lists\nIn real world applications, such as metasearch engines, rankings provided by the input methods are often partial lists.\nThis was outlined in [14] as the problem of having to merge top-k results from various input lists.\nFor instance, in the experiments carried out by Dwork et al. [11], authors found that among the top 100 best documents of 7 input search engines, 67% of the documents were present in only one search engine, whereas less than two documents were present in all the search engines.\nRank aggregation of partial lists raises four major difficulties which we state hereafter, proposing for each of them various working assumptions:\n1.\nPartial lists can have various lengths, which can favour long lists.\nWe thus consider the following two working hypotheses: H1k: We only consider the top k best documents from each input ranking.\nH1 all: We consider all the documents from each input ranking.\n2.\nSince there are different documents in the input rankings, we must decide which documents should be kept in the consensus ranking.\nTwo working hypotheses are therefore considered: H2k: We only consider documents which are present in at least k input rankings (k> 1).\nH2 all: We consider all the documents which are ranked in at least one input ranking.\nHereafter, we call documents which will be retained in the consensus ranking, candidate documents, and documents that will be excluded from the consensus ranking, excluded documents.\nWe also call a candidate document which is missing in one or more rankings, a missing document.\n3.\nSome candidate documents are missing documents in some input rankings.\nMain reasons for a missing doc\nument are that it was not indexed or it was indexed but deemed irrelevant; usually this information is not available.\nWe consider the following two working hypotheses: H3 yes: Each missing document in each> - j is assigned a position.\nH3no: No assumption is made, that is each missing document is considered neither better nor worse than any other document.\n4.\nWhen assumption H2k holds, each input ranking may contain documents which will not be considered in the consensus ranking.\nRegarding the positions of the candidate documents, we can consider the following working hypotheses: H4init: The initial positions of candidate documents are kept in each input ranking.\nH4 new: Candidate documents receive new positions in each input ranking, after discarding excluded ones.\nIn the IR context, rank aggregation methods need to decide more or less explicitly which assumptions to retain w.r.t. the above-mentioned difficulties.\n4.\nOUTRANKING APPROACH FOR RANK AGGREGATION\n4.1 Presentation\nPositional methods consider implicitly that the positions of the documents in the input rankings are scores giving thus a cardinal meaning to an ordinal information.\nThis constitutes a strong assumption that is questionable, especially when the input rankings have different lengths.\nMoreover, for positional methods, assumptions H3 and H4, which are often arbitrary, have a strong impact on the results.\nFor instance, let us consider an input ranking of 500 documents out of 1000 candidate documents.\nWhether we assign to each of the missing documents the position 1, 501, 750 or 1000 - corresponding to variations of H3 yes - will give rise to very contrasted results, especially regarding the top of the consensus ranking.\nMajoritarian methods do not suffer from the above-mentioned drawbacks of the positional methods since they build consensus rankings exploiting only ordinal information contained in the input rankings.\nNevertheless, they suppose that such rankings are complete orders, ignoring that they may hide ties.\nTherefore, majoritarian methods base consensus rankings on illusory discriminant information rather than less discriminant but more robust information.\nTrying to overcome the limits of current rank aggregation methods, we found that outranking approaches, which were initially used for multiple criteria aggregation problems [26], can also be used for the rank aggregation purpose, where each ranking plays the role of a criterion.\nTherefore, in order to decide whether a document di should be ranked better than di, in the consensus ranking σ, the two following conditions should be met:\n• a concordance condition which ensures that a majority of the input rankings are concordant with diadi' (majority principle).\n• a discordance condition which ensures that none of the discordant input rankings strongly refutes dad ~ (respect of minorities principle).\nFormally, the concordance coalition with diadi' is Csp (diadi') = {~ j ∈ PR: rji ≤ rji' − sp} where sp is a preference threshold which is the variation of document positions - whether it is absolute or relative to the ranking length - which draws the boundaries between an indifference and a preference situation between documents.\nThe discordance coalition with diadi' is Dsv (diadi') = {~ j ∈ PR: rji ≥ rji' + sv} where sv is a veto threshold which is the variation of document positions - whether it is absolute or relative to the ranking length - which draws the boundaries between a weak and a strong opposition to diadi'.\nDepending on the exact definition of the preceding concordance and discordance coalitions leading to the definition of some decision rules, several outranking relations can be defined.\nThey can be more or less demanding depending on i) the values of the thresholds sp and sv, ii) the importance or minimal size cmin required for the concordance coalition, and iii) the importance or maximum size dmax of the discordance coalition.\nA generic outranking relation can thus be defined as follows:\nThis expression defines a family of nested outranking relations since S (sp, sv, cmin, dmax) ⊆ S (s' p, s' v, c'm in, d'max) when cmin ≥ c ~ min and/or dmax ≤ d ~ max and/or sp ≥ s ~ p and/or sv ≤ s ~ v.\nThis expression also generalizes the majority rule which corresponds to the particular relation S (0, ∞, n 2, n).\nIt also satisfies important properties of rank aggregation methods, called neutrality, Pareto-optimality, Condorcet property and Extended Condorcet property, in the social choice literature [29].\nOutranking relations are not necessarily transitive and do not necessarily correspond to rankings since directed cycles may exist.\nTherefore, we need specific procedures in order to derive a consensus ranking.\nWe propose the following procedure which finds its roots in [27].\nIt consists in partitioning the set of documents into r ranked classes.\nEach class Ch contains documents with the same relevance and results from the application of all relations (if possible) to the set of documents remaining after previous classes are computed.\nDocuments within the same equivalence class are ranked arbitrarily.\nFormally, let\n• R be the set of candidate documents for a query, • S1, S2,...be a family of nested outranking relations, • Fk (di, E) = | {di' ∈ E: diSkdi'} | be the number of documents in E (E ⊆ R) that could be considered ` worse' than di according to relation Sk, • fk (di, E) = | {di' ∈ E: di' Skdi} | be the number of documents in E that could be considered ` better' than di according to Sk, • sk (di, E) = Fk (di, E) − fk (di, E) be the qualification of di in E according to Sk.\nEach class Ch results from a distillation process.\nIt corresponds to the last distillate of a series of sets E0 ⊇ E1 ⊇...where E0 = R \\ (C1 ∪...∪ Ch − 1) and Ek is a reduced subset of Ek − 1 resulting from the application of the following procedure:\n1.\ncompute for each di ∈ Ek − 1 its qualification according to Sk, i.e. sk (di, Ek − 1), 2.\ndefine smax = maxdi ∈ Ek-1 {sk (di, Ek − 1)}, then 3.\nEk = {di ∈ Ek − 1: sk (di, Ek − 1) = smax}\nWhen one outranking relation is used, the distillation process stops after the first application of the previous procedure, i.e., Ch corresponds to distillate E1.\nWhen different outranking relations are used, the distillation process stops when all the pre-defined outranking relations have been used or when | Ek | = 1.\n4.2 Illustrative Example\nThis section illustrates the concepts and procedures of section 4.1.\nLet us consider a set of candidate documents\nTable 1: Rankings of documents\nLet us suppose that the preference and veto thresholds are set to values 1 and 4 respectively, and that the concordance and discordance thresholds are set to values 2 and 1 respectively.\nThe following tables give the concordance, discordance and outranking matrices.\nEach entry csp (di, di') (dsv (di, di')) in the concordance (discordance) matrix gives the number of rankings that are concordant (discordant) with diadi', i.e. csp (di, di') = | Csp (diadi') | and dsv (di, di') = | Dsv (diadi') |.\nTable 2: Computation of the outranking relation\nFor instance, the concordance coalition for the assertion d1ad4 is C1 (d1ad4) = {~ 1, ~ 2, ~ 3} and the discordance coalition for the same assertion is D4 (d1ad4) = ∅.\nTherefore, c1 (d1, d4) = 3, d4 (d1, d4) = 0 and d1S1d4 holds.\nNotice that Fk (di, R) (fk (di, R)) is given by summing the values of the ith row (column) of the outranking matrix.\nThe\nconsensus ranking is obtained as follows: to get the first class C1, we compute the qualifications of all the documents of E0 = R with respect to S1.\nThey are respectively 2, 2, 2, -2 and -4.\nTherefore smax equals 2 and C1 = E1 = {d1, d2, d3}.\nObserve that, if we had used a second outranking relation S2 (⊇ S1), these three documents could have been possibly discriminated.\nAt this stage, we remove documents of C1 from the outranking matrix and compute the next class C2: we compute the new qualifications of the documents of E0 = R \\ C1 = {d4, d5}.\nThey are respectively 1 and -1.\nSo C3 = E1 = {d4}.\nThe last document d5 is the only document of the last class C3.\nThus, the consensus ranking is {d1, d2, d3} - {d4} - {d5}.\n5.\nEXPERIMENTS AND RESULTS\n5.1 Test Setting\nTo facilitate empirical investigation of the proposed methodology, we developed a prototype metasearch engine that implements a version of our outranking approach for rank aggregation.\nIn this paper, we apply our approach to the Topic Distillation (TD) task of TREC-2004 Web track [10].\nIn this task, there are 75 topics where only a short description of each is given.\nFor each query, we retained the rankings of the 10 best runs of the TD task which are provided by TREC-2004 participating teams.\nThe performances of these runs are reported in table 3.\nTable 3: Performances of the 10 best runs of the TD task of TREC-2004\nFor each query, each run provides a ranking of about 1000 documents.\nThe number of documents retrieved by all these runs ranges from 543 to 5769.\nTheir average (median) number is 3340 (3386).\nIt is worth noting that we found similar distributions of the documents among the rankings as in [11].\nFor evaluation, we used the ` trec eval' standard tool which is used by the TREC community to calculate the standard measures of system effectiveness which are Mean Average Precision (MAP) and Success@n (S0n) for n = 1, 5 and 10.\nOur approach effectiveness is compared against some high performing official results from TREC-2004 as well as against some standard rank aggregation algorithms.\nIn the experiments, significance testing is mainly based on the t-student statistic which is computed on the basis of the MAP values of the compared runs.\nIn the tables of the following section, statistically significant differences are marked with an asterisk.\nValues between brackets of the first column of each table, indicate the parameter value of the corresponding run.\n5.2 Results\nWe carried out several series of runs in order to i) study performance variations of the outranking approach when tuning the parameters and working assumptions, ii) compare performances of the outranking approach vs standard rank aggregation strategies, and iii) check whether rank aggregation performs better than the best input rankings.\nWe set our basic run mcm with the following parameters.\nWe considered that each input ranking is a complete order (sp = 0) and that an input ranking strongly refutes diσdi, when the difference of both document positions is large enough (sv = 75%).\nPreference and veto thresholds are computed proportionally to the number of documents retained in each input ranking.\nThey consequently may vary from one ranking to another.\nIn addition, to accept the assertion diσdii, we supposed that the majority of the rankings must be concordant (cmin = 50%) and that every input ranking can impose its veto (dmax = 0).\nConcordance and discordance thresholds are computed for each tuple (di, dii) as the percentage of the input rankings of PRi ∩ PRii.\nThus, our choice of parameters leads to the definition of the outranking relation S (0,75%,50%,0).\nTo test the run mcm, we had chosen the following assumptions.\nWe retained the top 100 best documents from each input ranking (H1100), only considered documents which are present in at least half of the input rankings (H25) and assumed H3no and H4 new.\nIn these conditions, the number of successful documents was about 100 on average, and the computation time per query was less than one second.\nObviously, modifying the working assumptions should have deeper impact on the performances than tuning our model parameters.\nThis was validated by preliminary experiments.\nThus, we hereafter begin by studying performance variation when different sets of assumptions are considered.\nAfterwards, we study the impact of tuning parameters.\nFinally, we compare our model performances w.r.t. the input rankings as well as some standard data fusion algorithms.\n5.2.1 Impact of the Working Assumptions\nTable 4 summarizes the performance variation of the outranking approach under different working hypotheses.\nIn\nTable 4: Impact of the working assumptions\nthis table, we first show that run mcm22, in which missing documents are all put in the same last position of each input ranking, leads to performance drop w.r.t. run mcm.\nMoreover, S01 moves from 41.33% to 34.67% (-16.11%).\nThis shows that several relevant documents which were initially put at the first position of the consensus ranking in mcm, lose this first position but remain ranked in the top 5 documents since S05 did not change.\nWe also conclude that documents which have rather good positions in some input rankings are more likely to be relevant, even though they are missing in some other rankings.\nConsequently, when they are missing in some rankings, assigning worse ranks to these documents is harmful for performance.\nAlso, from Table 4, we found that the performances of runs mcm and mcm23 are similar.\nTherefore, the outranking approach is not sensitive to keeping the initial positions of candidate documents or recomputing them by discarding excluded ones.\nFrom the same Table 4, performance of the outranking approach increases significantly for runs mcm24 and mcm25.\nTherefore, whether we consider all the documents which are present in half of the rankings (mcm24) or we consider all the documents which are ranked in the first 100 positions in one or more rankings (mcm25), increases performances.\nThis result was predictable since in both cases we have more detailed information on the relative importance of documents.\nTables 5 and 6 confirm this evidence.\nTable 5, where values between brackets of the first column give the number of documents which are retained from each input ranking, shows that selecting more documents from each input ranking leads to performance increase.\nIt is worth mentioning that selecting more than 600 documents from each input ranking does not improve performance.\nTable 5: Impact of the number of retained documents\nTable 6 reports runs corresponding to variations of H2k.\nValues between brackets are rank hits.\nFor instance, in\nthe run mcm32, only documents which are present in 3 or more input rankings, were considered successful.\nThis table shows that performance is significantly better when rare documents are considered, whereas it decreases significantly when these documents are discarded.\nTherefore, we conclude that many of the relevant documents are retrieved by a rather small set of IR models.\nTable 6: Performance considering different rank hits\nFor both runs mcm24 and mcm25, the number of successful documents was about 1000 and therefore, the computation time per query increased and became around 5 seconds.\n5.2.2 Impact of the Variation of the Parameters\nTable 7 shows performance variation of the outranking approach when different preference thresholds are considered.\nWe found performance improvement up to threshold values of about 5%, then there is a decrease in the performance which becomes significant for threshold values greater than 10%.\nMoreover, S@1 improves from 41.33% to 46.67% when preference threshold changes from 0 to 5%.\nWe can thus conclude that the input rankings are semi orders rather than complete orders.\nTable 8 shows the evolution of the performance measures w.r.t. the concordance threshold.\nWe can conclude that in order to put document di before di, in the consensus ranking,\nTable 7: Impact of the variation of the preference\nat least half of the input rankings of PRi ∩ PRii should be concordant.\nPerformance drops significantly for very low and very high values of the concordance threshold.\nIn fact, for such values, the concordance condition is either fulfilled rather always by too many document pairs or not fulfilled at all, respectively.\nTherefore, the outranking relation becomes either too weak or too strong respectively.\nTable 8: Impact of the variation of cmin\nIn the experiments, varying the veto threshold as well as the discordance threshold within reasonable intervals does not have significant impact on performance measures.\nIn fact, runs with different veto thresholds (sv ∈ [50%; 100%]) had similar performances even though there is a slight advantage for runs with high threshold values which means that it is better not to allow the input rankings to put their veto easily.\nAlso, tuning the discordance threshold was carried out for values 50% and 75% of the veto threshold.\nFor these runs we did not get any noticeable performance variation, although for low discordance thresholds (dmax <20%), performance slightly decreased.\n5.2.3 Impact of the Variation of the Number of Input Rankings\nTo study performance evolution when different sets of input rankings are considered, we carried three more runs where 2, 4, and 6 of the best performing sets of the input rankings are considered.\nResults reported in Table 9 are seemingly counter-intuitive and also do not support previous findings regarding rank aggregation research [3].\nNevertheless, this result shows that low performing rankings bring more noise than information to the establishment of the consensus ranking.\nTherefore, when they are considered, performance decreases.\nTable 9: Performance considering different best performing sets of input rankings\n5.2.4 Comparison of the Performance of Different Rank Aggregation Methods\nIn this set of runs, we compare the outranking approach with some standard rank aggregation methods which were\nproven to have acceptable performance in previous studies: we considered two positional methods which are the CombSUM and the CombMNZ strategies.\nWe also examined the performance of one majoritarian method which is the Markov chain method (MC4).\nFor the comparisons, we considered a specific outranking relation S ∗ = S (5%,50%,50%,30%) which results in good overall performances when tuning all the parameters.\nThe first row of Table 10 gives performances of the rank aggregation methods w.r.t. a basic assumption set A1 = (H1100, H25, H4new): we only consider the 100 first documents from each ranking, then retain documents present in 5 or more rankings and update ranks of successful documents.\nFor positional methods, we place missing documents at the queue of the ranking (H3 yes) whereas for our method as well as for MC4, we retained hypothesis Hn3o.\nThe three following rows of Table 10 report performances when changing one element from the basic assumption set: the second row corresponds to the assumption set A2 = (H11000, H25, H4new), i.e. changing the number of retained documents from 100 to 1000.\nThe third row corresponds to the assumption set A3 = (H1100, H2 all, Hn4ew), i.e. considering the documents present in at least one ranking.\nThe fourth row corresponds to the assumption set A4 = (H1100, H25, H4init), i.e. keeping the original ranks of successful documents.\nThe fifth row of Table 10, labeled A5, gives performance when all the 225 queries of the Web track of TREC-2004 are considered.\nObviously, performance level cannot be compared with previous lines since the additional queries are different from the TD queries and correspond to other tasks (Home Page and Named Page tasks [10]) of TREC-2004 Web track.\nThis set of runs aims to show whether relative performance of the various methods is task-dependent.\nThe last row of Table 10, labeled A6, reports performance of the various methods considering the TD task of TREC2002 instead of TREC-2004: we fused the results of input rankings of the 10 best official runs for each of the 50 TD queries [9] considering the set of assumptions A1 of the first row.\nThis aims to show whether relative performance of the various methods changes from year to year.\nValues between brackets of Table 10 are variations of performance of each rank aggregation method w.r.t. performance of the outranking approach.\nTable 10: Performance (MAP) of different rank aggregation methods under 3 different test collections\nFrom the analysis of table 10 the following can be established: • for all the runs, considering all the documents in each input ranking (A2) significantly improves performance (MAP increases by 11.62% on average).\nThis is predictable since some initially unreported relevant documents would receive better positions in the consensus ranking.\n• for all the runs, considering documents even those present in only one input ranking (A3) significantly im\nproves performance.\nFor mcm, combSUM and combMNZ, performance improvement is more important (MAP increases by 20.27% on average) than for the markov run (MAP increases by 3.86%).\n• preserving the initial positions of documents (A4) or recomputing them (A1) does not have a noticeable influence on performance for both positional and majoritarian methods.\n• considering all the queries of the Web track of TREC2004 (A5) as well as the TD queries of the Web track of TREC-2002 (A6) does not alter the relative performance of the different data fusion methods.\n• considering the TD queries of the Web track of TREC\n2002, performances of all the data fusion methods are lower than that of the best performing input ranking for which the MAP value equals 18.58%.\nThis is because most of the fused input rankings have very low performances compared to the best one, which brings more noise to the consensus ranking.\n• performances of the data fusion methods mcm and markov are significantly better than that of the best input ranking uogWebCAU150.\nThis remains true for runs combSUM and combMNZ only under assumptions H1 all or H2 all.\nThis shows that majoritarian methods are less sensitive to assumptions than positional methods.\n• outranking approach always performs significantly bet\nter than positional methods combSUM and combMNZ.\nIt has also better performances than the Markov chain method, especially under assumption H2 all where difference of performances becomes significant.\n6.\nCONCLUSIONS\nIn this paper, we address the rank aggregation problem where different, but not disjoint, lists of documents are to be fused.\nWe noticed that the input rankings can hide ties, so they should not be considered as complete orders.\nOnly robust information should be used from each input ranking.\nCurrent rank aggregation methods, and especially positional methods (e.g. combSUM [15]), are not initially designed to work with such rankings.\nThey should be adapted by considering specific working assumptions.\nWe propose a new outranking method for rank aggregation which is well adapted to the IR context.\nIndeed, it ranks two documents w.r.t. the intensity of their positions difference in each input ranking and also considering the number of the input rankings that are concordant and discordant in favor of a specific document.\nThere is also no need to make specific assumptions on the positions of the missing documents.\nThis is an important feature since the absence of a document from a ranking should not be necessarily interpreted negatively.\nExperimental results show that the outranking method significantly out-performs popular classical positional data fusion methods like combSUM and combMNZ strategies.\nIt also out-performs a good performing majoritarian methods which is the Markov chain method.\nThese results are tested against different test collections and queries.\nFrom the experiments, we can also conclude that in order to improve the performances, we should fuse result lists of well performing\nIR models, and that majoritarian data fusion methods perform better than positional methods.\nThe proposed method can have a real impact on Web metasearch performances since only ranks are available from most primary search engines, whereas most of the current approaches need scores to merge result lists into one single list.\nFurther work involves investigating whether the outranking approach performs well in various other contexts, e.g. using the document scores or some combination of document ranks and scores."},"lvl-3":{"kind":"string","value":"An Outranking Approach for Rank Aggregation in Information Retrieval\nABSTRACT\nResearch in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.).\nIn this paper, we focus on the rank aggregation problem, also called data fusion problem, where rankings of documents, searched into the same collection and provided by multiple methods, are combined in order to produce a new ranking.\nIn this context, we propose a rank aggregation method within a multiple criteria framework using aggregation mechanisms based on decision rules identifying positive and negative reasons for judging whether a document should get a better rank than another.\nWe show that the proposed method deals well with the Information Retrieval distinctive features.\nExperimental results are reported showing that the suggested method performs better than the well-known CombSUM and CombMNZ operators.\n1.\nINTRODUCTION\nA wide range of current Information Retrieval (IR) approaches are based on various search models (Boolean, Vector Space, Probabilistic, Language, etc. [2]) in order to retrieve relevant documents in response to a user request.\nThe result lists produced by these approaches depend on the exact definition of the relevance concept.\nRank aggregation approaches, also called data fusion approaches, consist in combining these result lists in order to produce a new and hopefully better ranking.\nSuch approaches give rise to metasearch engines in the Web context.\nWe consider, in the following, cases where only ranks are\navailable and no other additional information is provided such as the relevance scores.\nThis corresponds indeed to the reality, where only ordinal information is available.\nData fusion is also relevant in other contexts, such as when the user writes several queries of his/her information need (e.g., a boolean query and a natural language query) [4], or when many document surrogates are available [16].\nSeveral studies argued that rank aggregation has the potential of combining effectively all the various sources of evidence considered in various input methods.\nFor instance, experiments carried out in [16], [30], [4] and [19] showed that documents which appear in the lists of the majority of the input methods are more likely to be relevant.\nMoreover, Lee [19] and Vogt and Cottrell [31] found that various retrieval approaches often return very different irrelevant documents, but many of the same relevant documents.\nBartell et al. [3] also found that rank aggregation methods improve the performances w.r.t. those of the input methods, even when some of them have weak individual performances.\nThese methods also tend to smooth out biases of the input methods according to Montague and Aslam [22].\nData fusion has recently been proved to improve performances for both the ad hoc retrieval and categorization tasks within the TREC genomics track in 2005 [1].\nThe rank aggregation problem was addressed in various fields such as i) in social choice theory which studies voting algorithms which specify winners of elections or winners of competitions in tournaments [29], ii) in statistics when studying correlation between rankings, iii) in distributed databases when results from different databases must be combined [12], and iv) in collaborative filtering [23].\nMost current rank aggregation methods consider each input ranking as a permutation over the same set of items.\nThey also give rigid interpretation to the exact ranking of the items.\nBoth of these assumptions are rather not valid in the IR context, as will be shown in the following sections.\nThe remaining of the paper is organized as follows.\nWe first review current rank aggregation methods in Section 2.\nThen we outline the specificities of the data fusion problem in the IR context (Section 3).\nIn Section 4, we present a new aggregation method which is proven to best fit the IR context.\nExperimental results are presented in Section 5 and conclusions are provided in a final section.\n2.\nRELATED WORK\nAs pointed out by Riker [25], we can distinguish two families of rank aggregation methods: positional methods which assign scores to items to be ranked according to the ranks\nthey receive and majoritarian methods which are based on pairwise comparisons of items to be ranked.\nThese two families of methods find their roots in the pioneering works of Borda [5] and Condorcet [7], respectively, in the social choice literature.\n2.1 Preliminaries\nWe first introduce some basic notations to present the rank aggregation methods in a uniform way.\nLet D = {d1, d2,..., dnd} be a set of nd documents.\nA list or a ranking ~ j is an ordering defined on Dj ⊆ D (j = 1,..., n).\nThus, di ~ j di, means di ` is ranked better than' di, in ~ j.\nWhen Dj = D, ~ j is said to be a full list.\nOtherwise, it is a partial list.\nIf di belongs to Dj, rji denotes the rank or position of di in ~ j.\nWe assume that the best answer (document) is assigned the position 1 and the worst one is assigned the position | Dj |.\nLet ~ D be the set of all permutations on D or all subsets of D.\nA profile is a n-tuple of rankings PR = (~ 1, ~ 2,..., ~ n).\nRestricting PR to the rankings containing document di defines PRi.\nWe also call the number of rankings which contain document di the rank hits of di [19].\nThe rank aggregation or data fusion problem consists of finding a ranking function or mechanism Ψ (also called a social welfare function in the social choice theory terminology) defined by:\nwhere σ is called a consensus ranking.\n2.2 Positional Methods\n2.2.1 Borda Count\nThis method [5] first assigns a score Enj = 1 rji to each document di.\nDocuments are then ranked by increasing order of this score, breaking ties, if any, arbitrarily.\n2.2.2 Linear Combination Methods\nThis family of methods basically combine scores of documents.\nWhen used for the rank aggregation problem, ranks are assumed to be scores or performances to be combined using aggregation operators such as the weighted sum or some variation of it [3, 31, 17, 28].\nFor instance, Callan et al. [6] used the inference networks model [30] to combine rankings.\nFox and Shaw [15] proposed several combination strategies which are CombSUM, CombMIN, CombMAX, CombANZ and CombMNZ.\nThe first three operators correspond to the sum, min and max operators, respectively.\nCombANZ and CombMNZ respectively divides and multiplies the CombSUM score by the rank hits.\nIt is shown in [19] that the CombSUM and CombMNZ operators perform better than the others.\nMetasearch engines such as SavvySearch and MetaCrawler use the CombSUM strategy to fuse rankings.\n2.2.3 Footrule Optimal Aggregation\nIn this method, a consensus ranking minimizes the Spearman footrule distance from the input rankings [21].\nFormally, given two full lists ~ j and ~ j,, this distance is given by F (~ j, ~ j,) = nd\nas follows.\nGiven a profile PR and a consensus ranking σ, the Spearman footrule distance of σ to PR is given by\ni, i, |, where rji, i, = rji, − rji.\nThis formulation has the advantage that it considers the intensity of preferences.\n2.2.4 Probabilistic Methods\nThis kind of methods assume that the performance of the input methods on a number of training queries is indicative of their future performance.\nDuring the training process, probabilities of relevance are calculated.\nFor subsequent queries, documents are ranked based on these probabilities.\nFor instance, in [20], each input ranking ~ j is divided into a number of segments, and the conditional probability of relevance (R) of each document di depending on the segment k it occurs in, is computed, i.e. prob (R | di, k, ~ j).\nFor subsequent queries, the score of each document di is given by En prob (R | di, k, Yj).\nLe Calve and Savoy [18] suggest using j = 1 k a logistic regression approach for combining scores.\nTraining data is needed to infer the model parameters.\n2.3 Majoritarian Methods\n2.3.1 Condorcet Procedure\nThe original Condorcet rule [7] specifies that a winner of the election is any item that beats or ties with every other item in a pairwise contest.\nFormally, let C (diσdi,) = {~ j ∈ PR: di ~ j di,} be the coalition of rankings that are concordant with establishing diσdi,, i.e. with the proposition di ` should be ranked better than' di, in the final ranking σ.\ndi beats or ties with di, iff | C (diσdi,) | ≥ | C (di, σdi) |.\nThe repetitive application of the Condorcet algorithm can produce a ranking of items in a natural way: select the Condorcet winner, remove it from the lists, and repeat the previous two steps until there are no more documents to rank.\nSince there is not always Condorcet winners, variations of the Condorcet procedure have been developed within the multiple criteria decision aid theory, with methods such as ELECTRE [26].\n2.3.2 Kemeny Optimal Aggregation\nAs in section 2.2.3, a consensus ranking minimizes a geometric distance from the input rankings, where the Kendall tau distance is used instead of the Spearman footrule distance.\nFormally, given two full lists ~ j and ~ j,, the Kendall tau distance is given by K (~ j, ~ j,) = | {(di, di,): i <i ~, rji <rji,, rj,\ni,} |, i.e. the number of pairwise disagreements between the two lists.\nIt is easy to show that the consensus ranking corresponds to the geometric median of the input rankings and that the Kemeny optimal aggregation problem corresponds to the minimum feedback edge set problem.\n2.3.3 Markov Chain Methods\nMarkov chains (MCs) have been used by Dwork et al. [11] as a ` natural' method to obtain a consensus ranking where states correspond to the documents to be ranked and the transition probabilities vary depending on the interpretation of the transition event.\nIn the same reference, the authors proposed four specific MCs and experimental testing had shown that the following MC is the best performing one (see also [24]):\n• MC4: move from the current state di to the next state di, by first choosing a document di, uniformly from D.\nIf for the majority of the rankings, we have rji, ≤ rji, then move to di,, else stay in di.\nThe consensus ranking corresponds to the stationary distribution of MC4.\n3.\nSPECIFICITIES OF THE RANK AGGREGATION PROBLEM IN THE IR CONTEXT\n3.1 Limited Significance of the Rankings\n3.2 Partial Lists\n4.\nOUTRANKING APPROACH FOR RANK AGGREGATION\n4.1 Presentation\n4.2 Illustrative Example\n5.\nEXPERIMENTS AND RESULTS\n5.1 Test Setting\n5.2 Results\n5.2.1 Impact of the Working Assumptions\n5.2.2 Impact of the Variation of the Parameters\n5.2.3 Impact of the Variation of the Number of Input Rankings\n5.2.4 Comparison of the Performance of Different Rank Aggregation Methods\n6.\nCONCLUSIONS\nIn this paper, we address the rank aggregation problem where different, but not disjoint, lists of documents are to be fused.\nWe noticed that the input rankings can hide ties, so they should not be considered as complete orders.\nOnly robust information should be used from each input ranking.\nCurrent rank aggregation methods, and especially positional methods (e.g. combSUM [15]), are not initially designed to work with such rankings.\nThey should be adapted by considering specific working assumptions.\nWe propose a new outranking method for rank aggregation which is well adapted to the IR context.\nIndeed, it ranks two documents w.r.t. the intensity of their positions difference in each input ranking and also considering the number of the input rankings that are concordant and discordant in favor of a specific document.\nThere is also no need to make specific assumptions on the positions of the missing documents.\nThis is an important feature since the absence of a document from a ranking should not be necessarily interpreted negatively.\nExperimental results show that the outranking method significantly out-performs popular classical positional data fusion methods like combSUM and combMNZ strategies.\nIt also out-performs a good performing majoritarian methods which is the Markov chain method.\nThese results are tested against different test collections and queries.\nFrom the experiments, we can also conclude that in order to improve the performances, we should fuse result lists of well performing\nIR models, and that majoritarian data fusion methods perform better than positional methods.\nThe proposed method can have a real impact on Web metasearch performances since only ranks are available from most primary search engines, whereas most of the current approaches need scores to merge result lists into one single list.\nFurther work involves investigating whether the outranking approach performs well in various other contexts, e.g. using the document scores or some combination of document ranks and scores."},"lvl-4":{"kind":"string","value":"An Outranking Approach for Rank Aggregation in Information Retrieval\nABSTRACT\nResearch in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.).\nIn this paper, we focus on the rank aggregation problem, also called data fusion problem, where rankings of documents, searched into the same collection and provided by multiple methods, are combined in order to produce a new ranking.\nIn this context, we propose a rank aggregation method within a multiple criteria framework using aggregation mechanisms based on decision rules identifying positive and negative reasons for judging whether a document should get a better rank than another.\nWe show that the proposed method deals well with the Information Retrieval distinctive features.\nExperimental results are reported showing that the suggested method performs better than the well-known CombSUM and CombMNZ operators.\n1.\nINTRODUCTION\nThe result lists produced by these approaches depend on the exact definition of the relevance concept.\nRank aggregation approaches, also called data fusion approaches, consist in combining these result lists in order to produce a new and hopefully better ranking.\nSuch approaches give rise to metasearch engines in the Web context.\nWe consider, in the following, cases where only ranks are\navailable and no other additional information is provided such as the relevance scores.\nThis corresponds indeed to the reality, where only ordinal information is available.\nSeveral studies argued that rank aggregation has the potential of combining effectively all the various sources of evidence considered in various input methods.\nFor instance, experiments carried out in [16], [30], [4] and [19] showed that documents which appear in the lists of the majority of the input methods are more likely to be relevant.\nBartell et al. [3] also found that rank aggregation methods improve the performances w.r.t. those of the input methods, even when some of them have weak individual performances.\nThese methods also tend to smooth out biases of the input methods according to Montague and Aslam [22].\nMost current rank aggregation methods consider each input ranking as a permutation over the same set of items.\nThey also give rigid interpretation to the exact ranking of the items.\nBoth of these assumptions are rather not valid in the IR context, as will be shown in the following sections.\nThe remaining of the paper is organized as follows.\nWe first review current rank aggregation methods in Section 2.\nThen we outline the specificities of the data fusion problem in the IR context (Section 3).\nIn Section 4, we present a new aggregation method which is proven to best fit the IR context.\nExperimental results are presented in Section 5 and conclusions are provided in a final section.\n2.\nRELATED WORK\nAs pointed out by Riker [25], we can distinguish two families of rank aggregation methods: positional methods which assign scores to items to be ranked according to the ranks\nthey receive and majoritarian methods which are based on pairwise comparisons of items to be ranked.\nThese two families of methods find their roots in the pioneering works of Borda [5] and Condorcet [7], respectively, in the social choice literature.\n2.1 Preliminaries\nWe first introduce some basic notations to present the rank aggregation methods in a uniform way.\nLet D = {d1, d2,..., dnd} be a set of nd documents.\nA list or a ranking ~ j is an ordering defined on Dj ⊆ D (j = 1,..., n).\nThus, di ~ j di, means di ` is ranked better than' di, in ~ j.\nWhen Dj = D, ~ j is said to be a full list.\nOtherwise, it is a partial list.\nIf di belongs to Dj, rji denotes the rank or position of di in ~ j.\nWe assume that the best answer (document) is assigned the position 1 and the worst one is assigned the position | Dj |.\nA profile is a n-tuple of rankings PR = (~ 1, ~ 2,..., ~ n).\nRestricting PR to the rankings containing document di defines PRi.\nWe also call the number of rankings which contain document di the rank hits of di [19].\nThe rank aggregation or data fusion problem consists of finding a ranking function or mechanism Ψ (also called a social welfare function in the social choice theory terminology) defined by:\nwhere σ is called a consensus ranking.\n2.2 Positional Methods\n2.2.1 Borda Count\nThis method [5] first assigns a score Enj = 1 rji to each document di.\nDocuments are then ranked by increasing order of this score, breaking ties, if any, arbitrarily.\n2.2.2 Linear Combination Methods\nThis family of methods basically combine scores of documents.\nWhen used for the rank aggregation problem, ranks are assumed to be scores or performances to be combined using aggregation operators such as the weighted sum or some variation of it [3, 31, 17, 28].\nFor instance, Callan et al. [6] used the inference networks model [30] to combine rankings.\nThe first three operators correspond to the sum, min and max operators, respectively.\nCombANZ and CombMNZ respectively divides and multiplies the CombSUM score by the rank hits.\nIt is shown in [19] that the CombSUM and CombMNZ operators perform better than the others.\nMetasearch engines such as SavvySearch and MetaCrawler use the CombSUM strategy to fuse rankings.\n2.2.3 Footrule Optimal Aggregation\nIn this method, a consensus ranking minimizes the Spearman footrule distance from the input rankings [21].\nFormally, given two full lists ~ j and ~ j,, this distance is given by F (~ j, ~ j,) = nd\nas follows.\nGiven a profile PR and a consensus ranking σ, the Spearman footrule distance of σ to PR is given by\n2.2.4 Probabilistic Methods\nThis kind of methods assume that the performance of the input methods on a number of training queries is indicative of their future performance.\nDuring the training process, probabilities of relevance are calculated.\nFor subsequent queries, documents are ranked based on these probabilities.\nFor subsequent queries, the score of each document di is given by En prob (R | di, k, Yj).\nLe Calve and Savoy [18] suggest using j = 1 k a logistic regression approach for combining scores.\nTraining data is needed to infer the model parameters.\n2.3 Majoritarian Methods\n2.3.1 Condorcet Procedure\nThe repetitive application of the Condorcet algorithm can produce a ranking of items in a natural way: select the Condorcet winner, remove it from the lists, and repeat the previous two steps until there are no more documents to rank.\n2.3.2 Kemeny Optimal Aggregation\nAs in section 2.2.3, a consensus ranking minimizes a geometric distance from the input rankings, where the Kendall tau distance is used instead of the Spearman footrule distance.\ni,} |, i.e. the number of pairwise disagreements between the two lists.\nIt is easy to show that the consensus ranking corresponds to the geometric median of the input rankings and that the Kemeny optimal aggregation problem corresponds to the minimum feedback edge set problem.\n2.3.3 Markov Chain Methods\nMarkov chains (MCs) have been used by Dwork et al. [11] as a ` natural' method to obtain a consensus ranking where states correspond to the documents to be ranked and the transition probabilities vary depending on the interpretation of the transition event.\nIn the same reference, the authors proposed four specific MCs and experimental testing had shown that the following MC is the best performing one (see also [24]):\n• MC4: move from the current state di to the next state di, by first choosing a document di, uniformly from D.\nIf for the majority of the rankings, we have rji, ≤ rji, then move to di,, else stay in di.\nThe consensus ranking corresponds to the stationary distribution of MC4.\n6.\nCONCLUSIONS\nIn this paper, we address the rank aggregation problem where different, but not disjoint, lists of documents are to be fused.\nWe noticed that the input rankings can hide ties, so they should not be considered as complete orders.\nOnly robust information should be used from each input ranking.\nCurrent rank aggregation methods, and especially positional methods (e.g. combSUM [15]), are not initially designed to work with such rankings.\nThey should be adapted by considering specific working assumptions.\nWe propose a new outranking method for rank aggregation which is well adapted to the IR context.\nIndeed, it ranks two documents w.r.t. the intensity of their positions difference in each input ranking and also considering the number of the input rankings that are concordant and discordant in favor of a specific document.\nThere is also no need to make specific assumptions on the positions of the missing documents.\nThis is an important feature since the absence of a document from a ranking should not be necessarily interpreted negatively.\nExperimental results show that the outranking method significantly out-performs popular classical positional data fusion methods like combSUM and combMNZ strategies.\nIt also out-performs a good performing majoritarian methods which is the Markov chain method.\nThese results are tested against different test collections and queries.\nFrom the experiments, we can also conclude that in order to improve the performances, we should fuse result lists of well performing\nIR models, and that majoritarian data fusion methods perform better than positional methods.\nThe proposed method can have a real impact on Web metasearch performances since only ranks are available from most primary search engines, whereas most of the current approaches need scores to merge result lists into one single list.\nFurther work involves investigating whether the outranking approach performs well in various other contexts, e.g. using the document scores or some combination of document ranks and scores."}}},{"rowIdx":93,"cells":{"id":{"kind":"string","value":"H-87"},"title":{"kind":"string","value":"Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation"},"abstract":{"kind":"string","value":"This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering. Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granularity levels, and measured performance with different utility settings. We found that LR performs strongly and robustly in optimizing T11SU (a TREC utility function) while Rocchio is better for optimizing Ctrk (the TDT tracking cost), a high-recall oriented objective function. Using systematic cross-corpus parameter optimization with both methods, we obtained the best results ever reported on TDT5, TREC10 and TREC11. Relevance feedback on a small portion (0.05~0.2%) of the TDT5 test documents yielded significant performance improvements, measuring up to a 54% reduction in Ctrk and a 20.9% increase in T11SU (with β=0.1), compared to the results of the top-performing system in TDT2004 without relevance feedback information."},"keyphrases":{"kind":"list like","value":["robust","adapt filter","adapt filter","cross-benchmark evalu","regular","logist regress","lr","rocchio","topic detect","util function","cross-corpu paramet optim","relev feedback","inform retriev","topic track","statist learn","systemat method for paramet tune across multipl corpora","extern corpu","valid set","granular differ","cost function","penalti ratio","optim criterion","probabilist threshold calibr","rocchio-style method","posterior probabl","sigmoid function","bia","gaussian"],"string":"[\n \"robust\",\n \"adapt filter\",\n \"adapt filter\",\n \"cross-benchmark evalu\",\n \"regular\",\n \"logist regress\",\n \"lr\",\n \"rocchio\",\n \"topic detect\",\n \"util function\",\n \"cross-corpu paramet optim\",\n \"relev feedback\",\n \"inform retriev\",\n \"topic track\",\n \"statist learn\",\n \"systemat method for paramet tune across multipl corpora\",\n \"extern corpu\",\n \"valid set\",\n \"granular differ\",\n \"cost function\",\n \"penalti ratio\",\n \"optim criterion\",\n \"probabilist threshold calibr\",\n \"rocchio-style method\",\n \"posterior probabl\",\n \"sigmoid function\",\n \"bia\",\n \"gaussian\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","P","P","P","P","P","P","P","P","P","R","R","U","M","U","M","R","R","U","M","U","M","U","M","U","U"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"R\",\n \"R\",\n \"U\",\n \"M\",\n \"U\",\n \"M\",\n \"R\",\n \"R\",\n \"U\",\n \"M\",\n \"U\",\n \"M\",\n \"U\",\n \"M\",\n \"U\",\n \"U\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation Yiming Yang, Shinjae Yoo, Jian Zhang, Bryan Kisiel School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA ABSTRACT This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering.\nUsing four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granularity levels, and measured performance with different utility settings.\nWe found that LR performs strongly and robustly in optimizing T11SU (a TREC utility function) while Rocchio is better for optimizing Ctrk (the TDT tracking cost), a high-recall oriented objective function.\nUsing systematic cross-corpus parameter optimization with both methods, we obtained the best results ever reported on TDT5, TREC10 and TREC11.\nRelevance feedback on a small portion (0.05~0.2%) of the TDT5 test documents yielded significant performance improvements, measuring up to a 54% reduction in Ctrk and a 20.9% increase in T11SU (with β=0.1), compared to the results of the top-performing system in TDT2004 without relevance feedback information.\nCategories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Information filtering, Relevance feedback, Retrieval models, Selection process; I.5.2 [Design Methodology]: Classifier design and evaluation General Terms Algorithms, Measurement, Performance, Experimentation 1.\nINTRODUCTION Adaptive filtering (AF) has been a challenging research topic in information retrieval.\nThe task is for the system to make an online topic membership decision (yes or no) for every document, as soon as it arrives, with respect to each pre-defined topic of interest.\nStarting from 1997 in the Topic Detection and Tracking (TDT) area and 1998 in the Text Retrieval Conferences (TREC), benchmark evaluations have been conducted by NIST under the following conditions[6][7][8][3][4]: • A very small number (1 to 4) of positive training examples was provided for each topic at the starting point.\n• Relevance feedback was available but only for the systemaccepted documents (with a yes decision) in the TREC evaluations for AF.\n• Relevance feedback (RF) was not allowed in the TDT evaluations for AF (or topic tracking in the TDT terminology) until 2004.\n• TDT2004 was the first time that TREC and TDT metrics were jointly used in evaluating AF methods on the same benchmark (the TDT5 corpus) where non-stationary topics dominate.\nThe above conditions attempt to mimic realistic situations where an AF system would be used.\nThat is, the user would be willing to provide a few positive examples for each topic of interest at the start, and might or might not be able to provide additional labeling on a small portion of incoming documents through relevance feedback.\nFurthermore, topics of interest might change over time, with new topics appearing and growing, and old topics shrinking and diminishing.\nThese conditions make adaptive filtering a difficult task in statistical learning (online classification), for the following reasons: 1) it is difficult to learn accurate models for prediction based on extremely sparse training data; 2) it is not obvious how to correct the sampling bias (i.e., relevance feedback on system-accepted documents only) during the adaptation process; 3) it is not well understood how to effectively tune parameters in AF methods using cross-corpus validation where the validation and evaluation topics do not overlap, and the documents may be from different sources or different epochs.\nNone of these problems is addressed in the literature of statistical learning for batch classification where all the training data are given at once.\nThe first two problems have been studied in the adaptive filtering literature, including topic profile adaptation using incremental Rocchio, Gaussian-Exponential density models, logistic regression in a Bayesian framework, etc., and threshold optimization strategies using probabilistic calibration or local fitting techniques [1][2][9][10][11][12][13].\nAlthough these works provide valuable insights for understanding the problems and possible solutions, it is difficult to draw conclusions regarding the effectiveness and robustness of current methods because the third problem has not been thoroughly investigated.\nAddressing the third issue is the main focus in this paper.\nWe argue that robustness is an important measure for evaluating and comparing AF methods.\nBy robust we mean consistent and strong performance across benchmark corpora with a systematic method for parameter tuning across multiple corpora.\nMost AF methods have pre-specified parameters that may influence the performance significantly and that must be determined before the test process starts.\nAvailable training examples, on the other hand, are often insufficient for tuning the parameters.\nIn TDT5, for example, there is only one labeled training example per topic at the start; parameter optimization on such training data is doomed to be ineffective.\nThis leaves only one option (assuming tuning on the test set is not an alternative), that is, choosing an external corpus as the validation set.\nNotice that the validation-set topics often do not overlap with the test-set topics, thus the parameter optimization is performed under the tough condition that the validation data and the test data may be quite different from each other.\nNow the important question is: which methods (if any) are robust under the condition of using cross-corpus validation to tune parameters?\nCurrent literature does not offer an answer because no thorough investigation on the robustness of AF methods has been reported.\nIn this paper we address the above question by conducting a cross-benchmark evaluation with two effective approaches in AF: incremental Rocchio and regularized logistic regression (LR).\nRocchio-style classifiers have been popular in AF, with good performance in benchmark evaluations (TREC and TDT) if appropriate parameters were used and if combined with an effective threshold calibration strategy [2][4][7][8][9][11][13].\nLogistic regression is a classical method in statistical learning, and one of the best in batch-mode text categorization [15][14].\nIt was recently evaluated in adaptive filtering and was found to have relatively strong performance (Section 5.1).\nFurthermore, a recent paper [13] reported that the joint use of Rocchio and LR in a Bayesian framework outperformed the results of using each method alone on the TREC11 corpus.\nStimulated by those findings, we decided to include Rocchio and LR in our crossbenchmark evaluation for robustness testing.\nSpecifically, we focus on how much the performance of these methods depends on parameter tuning, what the most influential parameters are in these methods, how difficult (or how easy) to optimize these influential parameters using cross-corpus validation, how strong these methods perform on multiple benchmarks with the systematic tuning of parameters on other corpora, and how efficient these methods are in running AF on large benchmark corpora.\nThe organization of the paper is as follows: Section 2 introduces the four benchmark corpora (TREC10 and TREC11, TDT3 and TDT5) used in this study.\nSection 3 analyzes the differences among the TREC and TDT metrics (utilities and tracking cost) and the potential implications of those differences.\nSection 4 outlines the Rocchio and LR approaches to AF, respectively.\nSection 5 reports the experiments and results.\nSection 6 concludes the main findings in this study.\n2.\nBENCHMARK CORPORA We used four benchmark corpora in our study.\nTable 1 shows the statistics about these data sets.\nTREC10 was the evaluation benchmark for adaptive filtering in TREC 2001, consisting of roughly 806,791 Reuters news stories from August 1996 to August 1997 with 84 topic labels (subject categories)[7].\nThe first two weeks (August 20th to 31st , 1996) of documents is the training set, and the remaining 11 & 1/2 months (from September 1st , 1996 to August 19th , 1997) is the test set.\nTREC11 was the evaluation benchmark for adaptive filtering in TREC 2002, consisting of the same set of documents as those in TREC10 but with a slightly different splitting point for the training and test sets.\nThe TREC11 topics (50) are quite different from those in TREC10; they are queries for retrieval with relevance judgments by NIST assessors [8].\nTDT3 was the evaluation benchmark in the TDT2001 dry run1 .\nThe tracking part of the corpus consists of 71,388 news stories from multiple sources in English and Mandarin (AP, NYT, CNN, ABC, NBC, MSNBC, Xinhua, Zaobao, Voice of America and PRI the World) in the period of October to December 1998.\nMachine-translated versions of the non-English stories (Xinhua, Zaobao and VOA Mandarin) are provided as well.\nThe splitting point for training-test sets is different for each topic in TDT.\nTDT5 was the evaluation benchmark in TDT2004 [4].\nThe tracking part of the corpus consists of 407,459 news stories in the period of April to September, 2003 from 15 news agents or broadcast sources in English, Arabic and Mandarin, with machine-translated versions of the non-English stories.\nWe only used the English versions of those documents in our experiments for this paper.\nThe TDT topics differ from TREC topics both conceptually and statistically.\nInstead of generic, ever-lasting subject categories (as those in TREC), TDT topics are defined at a finer level of granularity, for events that happen at certain times and locations, and that are born and die, typically associated with a bursty distribution over chronologically ordered news stories.\nThe average size of TDT topics (events) is two orders of magnitude smaller than that of the TREC10 topics.\nFigure 1 compares the document densities of a TREC topic (Civil Wars) and two TDT topics (Gunshot and APEC Summit Meeting, respectively) over a 3-month time period, where the area under each curve is normalized to one.\nThe granularity differences among topics and the corresponding non-stationary distributions make the cross-benchmark evaluation interesting.\nFor example, algorithms favoring large and stable topics may not work well for short-lasting and nonstationary topics, and vice versa.\nCross-benchmark evaluations allow us to test this hypothesis and possibly identify the weaknesses in current approaches to adaptive filtering in tracking the drifting trends of topics.\n1 http://www.ldc.upenn.edu/Projects/TDT2001/topics.html Table 1: Statistics of benchmark corpora for adaptive filtering evaluations N(tr) is the number of the initial training documents; N(ts) is the number of the test documents; n+ is the number of positive examples of a predefined topic; * is an average over all the topics.\n0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Week P(topic|week) Gunshot (TDT5) APEC Summit Meeting (TDT3) Civil War(TREC10) Figure 1: The temporal nature of topics 3.\nMETRICS To make our results comparable to the literature, we decided to use both TREC-conventional and TDT-conventional metrics in our evaluation.\n3.1 TREC11 metrics Let A, B, C and D be, respectively, the numbers of true positives, false alarms, misses and true negatives for a specific topic, and DCBAN +++= be the total number of test documents.\nThe TREC-conventional metrics are defined as: Precision )/( BAA += , Recall )/( CAA += )(2 )21( CABA A F +++ + = β β β ( ) η ηηβ ηβ − −+− = 1 ),/()(max 11 , CABA SUT where parameters β and η were set to 0.5 and -0.5 respectively in TREC10 (2001) and TREC11 (2002).\nFor evaluating the performance of a system, the performance scores are computed for individual topics first and then averaged over topics (macroaveraging).\n3.2 TDT metrics The TDT-conventional metric for topic tracking is defined as: famisstrk PTPwPTPwTC ))(1()()( 21 −+= where P(T) is the percentage of documents on topic T, missP is the miss rate by the system on that topic, faP is the false alarm rate, and 1w and 2w are the costs (pre-specified constants) for a miss and a false alarm, respectively.\nThe TDT benchmark evaluations (since 1997) have used the settings of 11 =w , 1.02 =w and 02.0)( =TP for all topics.\nFor evaluating the performance of a system, Ctrk is computed for each topic first and then the resulting scores are averaged for a single measure (the topic-weighted Ctrk).\nTo make the intuition behind this measure transparent, we substitute the terms in the definition of Ctrk as follows: N CA TP + =-RRB-( , N DB TP + =− )(1 , CA C Pmiss + = , DB B Pfa + = , )( 1 )( 21 21 BwCw N DB B N DB w CA C N CA wTCtrk +⋅= + ⋅ + ⋅+ + ⋅ + ⋅= Clearly, trkC is the average cost per error on topic T, with 1w and 2w controlling the penalty ratio for misses vs. false alarms.\nIn addition to trkC , TDT2004 also employed 1.011 =βSUT as a utility metric.\nTo distinguish this from the 5.011 =βSUT in TREC11, we call former TDT5SU in the rest of this paper.\nCorpus #Topics N(tr) N(ts) Avg n+ (tr) Avg n+ (ts) Max n+ (ts) Min n+ (ts) #Topics per doc (ts) TREC10 84 20,307 783,484 2 9795.3 39,448 38 1.57 TREC11 50 80.664 726,419 3 378.0 597 198 1.12 TDT3 53 18,738* 37,770* 4 79.3 520 1 1.06 TDT5 111 199,419* 207,991* 1 71.3 710 1 1.01 3.3 The correlations and the differences From an optimization point of view, TDT5SU and T11SU are both utility functions while Ctrk is a cost function.\nOur objective is to maximize the former or to minimize the latter on test documents.\nThe differences and correlations among these objective functions can be analyzed through the shared counts of A, B, C and D in their definitions.\nFor example, both TDT5SU and T11SU are positively correlated to the values of A and D, and negatively correlated to the values of B and C; the only difference between them is in their penalty ratios for misses vs. false alarms, i.e., 10:1 in TDT5SU and 2:1 in T11SU.\nThe Ctrk function, on the other hand, is positively correlated to the values of C and B, and negatively correlated to the values of A and D; hence, it is negatively correlated to T11SU and TDT5SU.\nMore importantly, there is a subtle and major difference between Ctrk and the utility functions: T11SU and TDT5SU.\nThat is, Ctrk has a very different penalty ratio for misses vs. false alarms: it favors recall-oriented systems to an extreme.\nAt first glance, one would think that the penalty ratio in Ctrk is 10:1 since 11 =w and 1.02 =w .\nHowever, this is not true if 02.0)( =TP is an inaccurate estimate of the on-topic documents on average for the test corpus.\nUsing TDT3 as an example, the true percentage is: 002.0 37770 3.79 )( ≈= + = N n TP where N is the average size of the test sets in TDT3, and n+ is the average number of positive examples per topic in the test sets.\nUsing 02.0)(ˆ =TP as an (inaccurate) estimate of 0.002 enlarges the intended penalty ratio of 10:1 to 100:1, roughly speaking.\nTo wit: )1.010( 1 1.010 )3.7937770(37770 3.79 1011.0101 1011.0101 ))(1(2)(1 )02.01(202.01)( BC NN B N C B N C DB B N CA N C faPTPwmissPTPw faPwmissPwTtrkC ×+×=×+×≈ − ×−×+××= + + ×−×+××= ×−⋅+××= −×+××= ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ρρ where 10 002.0 02.0 )( )(ˆ === TP TP ρ is the factor of enlargement in the estimation of P(T) compared to the truth.\nComparing the above result to formula 2, we can see the actual penalty ratio for misses vs. false alarms was 100:1 in the evaluations on TDT3 using Ctrk.\nSimilarly, we can compute the enlargement factor for TDT5 using the statistics in Table 1 as follows: 3.58 991,207/3.71 02.0 )( )(ˆ === TP TP ρ which means the actual penalty ratio for misses vs. false alarms in the evaluation on TDT5 using Ctrk was approximately 583:1.\nThe implications of the above analysis are rather significant: • Ctrk defined in the same formula does not necessarily mean the same objective function in evaluation; instead, the optimization criterion depends on the test corpus.\n• Systems optimized for Ctrk would not optimize TDT5SU (and T11SU) because the former favors high-recall oriented to an extreme while the latter does not.\n• Parameters tuned on one corpus (e.g., TDT3) might not work for an evaluation on another corpus (say, TDT5) unless we account for the previously-unknown subtle dependency of Ctrk on data.\n• Results in Ctrk in the past years of TDT evaluations may not be directly comparable to each other because the evaluation collections changed most years and hence the penalty ratio in Ctrk varied.\nAlthough these problems with Ctrk were not originally anticipated, it offered an opportunity to examine the ability of systems in trading off precision for extreme recall.\nThis was a challenging part of the TDT2004 evaluation for AF.\nComparing the metrics in TDT and TREC from a utility or cost optimization point of view is important for understanding the evaluation results of adaptive filtering methods.\nThis is the first time this issue is explicitly analyzed, to our knowledge.\n4.\nMETHODS 4.1 Incremental Rocchio for AF We employed a common version of Rocchio-style classifiers which computes a prototype vector per topic (T) as follows: |)(| ' |)(| )()( )(')( TD d TD d TqTp TDdTDd − ∈ + ∈ ∑∑ −+ −+= rr rr rr γβα The first term on the RHS is the weighted vector representation of topic description whose elements are terms weights.\nThe second term is the weighted centroid of the set )(TD+ of positive training examples, each of which is a vector of withindocument term weights.\nThe third term is the weighted centroid of the set )(TD− of negative training examples which are the nearest neighbors of the positive centroid.\nThe three terms are given pre-specified weights of βα, and γ , controlling the relative influence of these components in the prototype.\nThe prototype of a topic is updated each time the system makes a yes decision on a new document for that topic.\nIf relevance feedback is available (as is the case in TREC adaptive filtering), the new document is added to the pool of either )(TD+ or )(TD− , and the prototype is recomputed accordingly; if relevance feedback is not available (as is the case in TDT event tracking), the system``s prediction (yes) is treated as the truth, and the new document is added to )(TD+ for updating the prototype.\nBoth cases are part of our experiments in this paper (and part of the TDT 2004 evaluations for AF).\nTo distinguish the two, we call the first case simply Rocchio and the second case PRF Rocchio where PRF stands for pseudorelevance feedback.\nThe predictions on a new document are made by computing the cosine similarity between each topic prototype and the document vector, and then comparing the resulting scores against a threshold: ⎩ ⎨ ⎧ − + =− )( )( ))),((cos( no yes dTpsign new θ rr Threshold calibration in incremental Rocchio is a challenging research topic.\nMultiple approaches have been developed.\nThe simplest is to use a universal threshold for all topics, tuned on a validation set and fixed during the testing phase.\nMore elaborate methods include probabilistic threshold calibration which converts the non-probabilistic similarity scores to probabilities (i.e., )|( dTP r ) for utility optimization [9][13], and margin-based local regression for risk reduction [11].\nIt is beyond the scope of this paper to compare all the different ways to adapt Rocchio-style methods for AF.\nInstead, our focus here is to investigate the robustness of Rocchio-style methods in terms of how much their performance depends on elaborate system tuning, and how difficult (or how easy) it is to get good performance through cross-corpus parameter optimization.\nHence, we decided to use a relatively simple version of Rocchio as the baseline, i.e., with a universal threshold tuned on a validation corpus and fixed for all topics in the testing phase.\nThis simple version of Rocchio has been commonly used in the past TDT benchmark evaluations for topic tracking, and had strong performance in the TDT2004 evaluations for adaptive filtering with and without relevance feedback (Section 5.1).\nResults of more complex variants of Rocchio are also discussed when relevant.\n4.2 Logistic Regression for AF Logistic regression (LR) estimates the posterior probability of a topic given a document using a sigmoid function )1/(1),|1( xw ewxyP rrrr ⋅− +== where x r is the document vector whose elements are term weights, w r is the vector of regression coefficients, and }1,1{ −+∈y is the output variable corresponding to yes or no with respect to a particular topic.\nGiven a training set of labeled documents { }),(,),,( 11 nn yxyxD r L r = , the standard regression problem is defined as to find the maximum likelihood estimates of the regression coefficients (the model parameters): { } { } { }))exp(1(1logminarg )|(logmaxarg)|(maxarg ii xwyn i w wDP w wDP w mlw rr r r r r r r ⋅−+∑ == == This is a convex optimization problem which can be solved using a standard conjugate gradient algorithm in O(INF) time for training per topic, where I is the average number of iterations needed for convergence, and N and F are the number of training documents and number of features respectively [14].\nOnce the regression coefficients are optimized on the training data, the filtering prediction on each incoming document is made as: ( ) ⎩ ⎨ ⎧ − + =− )( )( ),|( no yes wxyPsign optnew θ rr Note that w r is constantly updated whenever a new relevance judgment is available in the testing phase of AF, while the optimal threshold optθ is constant, depending only on the predefined utility (or cost) function for evaluation.\nIf T11SU is the metric, for example, with the penalty ratio of 2:1 for misses and false alarms (Section 3.1), the optimal threshold for LR is 33.0)12/(1 =+ for all topics.\nWe modified the standard (above) version of LR to allow more flexible optimization criteria as follows: ⎭ ⎬ ⎫ ⎩ ⎨ ⎧ −++= ∑= ⋅− 2 1 )1log()(minarg μλ rrr rr r weysw n i xwy i w map ii where )( iys is taken to be α , β and γ for query, positive and negative documents respectively, which are similar to those in Rocchio, giving different weights to the three kinds of training examples: topic descriptions (queries), on-topic documents and off-topic documents.\nThe second term in the objective function is for regularization, equivalent to adding a Gaussian prior to the regression coefficients with mean μ r and covariance variance matrix Ι⋅λ2/1 , where Ι is the identity matrix.\nTuning λ (≥0) is theoretically justified for reducing model complexity (the effective degree of freedom) and avoiding over-fitting on training data [5].\nHow to find an effective μ r is an open issue for research, depending on the user``s belief about the parameter space and the optimal range.\nThe solution of the modified objective function is called the Maximum A Posteriori (MAP) estimate, which reduces to the maximum likelihood solution for standard LR if 0=λ .\n5.\nEVALUATIONS We report our empirical findings in four parts: the TDT2004 official evaluation results, the cross-corpus parameter optimization results, and the results corresponding to the amounts of relevance feedback.\n5.1 TDT2004 benchmark results The TDT2004 evaluations for adaptive filtering were conducted by NIST in November 2004.\nMultiple research teams participated and multiple runs from each team were allowed.\nCtrk and TDT5SU were used as the metrics.\nFigure 2 and Figure 3 show the results; the best run from each team was selected with respect to Ctrk or TDT5SU, respectively.\nOur Rocchio (with adaptive profiles but fixed universal threshold for all topics) had the best result in Ctrk, and our logistic regression had the best result in TDT5SU.\nAll the parameters of our runs were tuned on the TDT3 corpus.\nResults for other sites are also listed anonymously for comparison.\nCtrk Ours 0.0324 Site2 0.0467 Site3 0.1366 Site4 0.2438 Metric = Ctrk (the lower the better) 0.0324 0.0467 0.1366 0.2438 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Ours Site2 Site3 Site4 Figure 2: TDT2004 results in Ctrk of systems using true relevance feedback.\n(Ours is the Rocchio method.)\nWe also put the 1st and 3rd quartiles as sticks for each site.2 T11SU Ours 0.7328 Site3 0.7281 Site2 0.6672 Site4 0.382 Metric = TDT5SU (the higher the better) 0.7328 0.7281 0.6672 0.382 0 0.2 0.4 0.6 0.8 1 Ours Site3 Site2 Site4 Figure 3:TDT2004 results in TDT5SU of systems using true relevance feedback.\n(Ours is LR with 0=μ r and 005.0=λ ).\nCTrk Ours 0.0707 Site2 0.1545 Site5 0.5669 Site4 0.6507 Site6 0.8973 Primary Topic Traking Results in TDT2004 0.0707 0.8973 0.6507 0.1545 0.5669 0 0.2 0.4 0.6 0.8 1 1.2 Ours Site2 Site5 Site4 Site6 Ctrk Figure 4:TDT2004 results in Ctrk of systems without using true relevance feedback.\n(Ours is PRF Rocchio.)\nAdaptive filtering without using true relevance feedback was also a part of the evaluations.\nIn this case, systems had only one labeled training example per topic during the entire training and testing processes, although unlabeled test documents could be used as soon as predictions on them were made.\nSuch a setting has been conventional for the Topic Tracking task in TDT until 2004.\nFigure 4 shows the summarized official submissions from each team.\nOur PRF Rocchio (with a fixed threshold for all the topics) had the best performance.\n2 We use quartiles rather than standard deviations since the former is more resistant to outliers.\n5.2 Cross-corpus parameter optimization How much the strong performance of our systems depends on parameter tuning is an important question.\nBoth Rocchio and LR have parameters that must be prespecified before the AF process.\nThe shared parameters include the sample weightsα , β and γ , the sample size of the negative training documents (i.e., )(TD− ), the term-weighting scheme, and the maximal number of non-zero elements in each document vector.\nThe method-specific parameters include the decision threshold in Rocchio, and μ r , λ and MI (the maximum number of iterations in training) in LR.\nGiven that we only have one labeled example per topic in the TDT5 training sets, it is impossible to effectively optimize these parameters on the training data, and we had to choose an external corpus for validation.\nAmong the choices of TREC10, TREC11 and TDT3, we chose TDT3 (c.f. Section 2) because it is most similar to TDT5 in terms of the nature of the topics (Section 2).\nWe optimized the parameters of our systems on TDT3, and fixed those parameters in the runs on TDT5 for our submissions to TDT2004.\nWe also tested our methods on TREC10 and TREC11 for further analysis.\nSince exhaustive testing of all possible parameter settings is computationally intractable, we followed a step-wise forward chaining procedure instead: we pre-specified an order of the parameters in a method (Rocchio or LR), and then tuned one parameter at the time while fixing the settings of the remaining parameters.\nWe repeated this procedure for several passes as time allowed.\n0.05 0.26 0.67 0.69 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 0.02 0.03 0.04 0.06 0.08 0.1 0.15 0.2 0.25 0.3 Threshold TDT5SU TDT3 TDT5 TREC10 TREC11 Figure 5: Performance curves of adaptive Rocchio Figure 5 compares the performance curves in TDT5SU for Rocchio on TDT3, TDT5, TREC10 and TREC11 when the decision threshold varied.\nThese curves peak at different locations: the TDT3-optimal is closest to the TDT5-optimal while the TREC10-optimal and TREC1-optimal are quite far away from the TDT5-optimal.\nIf we were using TREC10 or TREC11 instead of TDT3 as the validation corpus for TDT5, or if the TDT3 corpus were not available, we would have difficulty in obtaining strong performance for Rocchio in TDT2004.\nThe difficulty comes from the ad-hoc (non-probabilistic) scores generated by the Rocchio method: the distribution of the scores depends on the corpus, making cross-corpus threshold optimization a tricky problem.\nLogistic regression has less difficulty with respect to threshold tuning because it produces probabilistic scores of )|1Pr( xy = upon which the optimal threshold can be directly computed if probability estimation is accurate.\nGiven the penalty ratio for misses vs. false alarms as 2:1 in T11SU, 10:1 in TDT5SU and 583:1 in Ctrk (Section 3.3), the corresponding optimal thresholds (t) are 0.33, 0.091 and 0.0017 respectively.\nAlthough the theoretical threshold could be inaccurate, it still suggests the range of near-optimal settings.\nWith these threshold settings in our experiments for LR, we focused on the crosscorpus validation of the Bayesian prior parameters, that is, μ r and λ.\nTable 2 summarizes the results 3 .\nWe measured the performance of the runs on TREC10 and TREC11 using T11SU, and the performance of the runs on TDT3 and TDT5 using TDT5SU.\nFor comparison we also include the best results of Rocchio-based methods on these corpora, which are our own results of Rocchio on TDT3 and TDT5, and the best results reported by NIST for TREC10 and TREC11.\nFrom this set of results, we see that LR significantly outperformed Rocchio on all the corpora, even in the runs of standard LR without any tuning, i.e. λ=0.\nThis empirical finding is consistent with a previous report [13] for LR on TREC11 although our results of LR (0.585~0.608 in T11SU) are stronger than the results (0.49 for standard LR and 0.54 for LR using Rocchio prototype as the prior) in that report.\nMore importantly, our cross-benchmark evaluation gives strong evidence for the robustness of LR.\nThe robustness, we believe, comes from the probabilistic nature of the system-generated scores.\nThat is, compared to the ad-hoc scores in Rocchio, the normalized posterior probabilities make the threshold optimization in LR a much easier problem.\nMoreover, logistic regression is known to converge towards the Bayes classifier asymptotically while Rocchio classifiers'' parameters do not.\nAnother interesting observation in these results is that the performance of LR did not improve when using a Rocchio prototype as the mean in the prior; instead, the performance decreased in some cases.\nThis observation does not support the previous report by [13], but we are not surprised because we are not convinced that Rocchio prototypes are more accurate than LR models for topics in the early stage of the AF process, and we believe that using a Rocchio prototype as the mean in the Gaussian prior would introduce undesirable bias to LR.\nWe also believe that variance reduction (in the testing phase) should be controlled by the choice of λ (but not μ r ), for which we conducted the experiments as shown in Figure 6.\nTable 2: Results of LR with different Bayesian priors Corpus TDT3 TDT5 TREC10 TREC11 LR(μ=0,λ=0) 0.7562 0.7737 0.585 0.5715 LR(μ=0,λ=0.01) 0.8384 0.7812 0.6077 0.5747 LR(μ=roc*,λ=0.01) 0.8138 0.7811 0.5803 0.5698 Best Rocchio 0.6628 0.6917 0.4964 0.475 3 The LR results (0.77~0.78) on TDT5 in this table are better than our TDT2004 official result (0.73) because parameter optimization has been improved afterwards.\n4 The TREC10-best result (0.496 by Oracle) is only available in T10U which is not directly comparable to the scores in T11SU, just indicative.\n*: μ r was set to the Rocchio prototype 0 0.2 0.4 0.6 0.8 0.000 0.001 0.005 0.050 0.500 Lambda Performance Ctrk on TDT3 TDT5SU on TDT3 TDT5SU on TDT5 T11SU on TREC11 Figure 6: LR with varying lambda.\nThe performance of LR is summarized with respect to λ tuning on the corpora of TREC10, TREC11 and TDT3.\nThe performance on each corpus was measured using the corresponding metrics, that is, T11SU for the runs on TREC10 and TREC11, and TDT5SU and Ctrk for the runs on TDT3,.\nIn the case of maximizing the utilities, the safe interval for λ is between 0 and 0.01, meaning that the performance of regularized LR is stable, the same as or improved slightly over the performance of standard LR.\nIn the case of minimizing Ctrk, the safe range for λ is between 0 and 0.1, and setting λ between 0.005 and 0.05 yielded relatively large improvements over the performance of standard LR because training a model for extremely high recall is statistically more tricky, and hence more regularization is needed.\nIn either case, tuning λ is relatively safe, and easy to do successfully by cross-corpus tuning.\nAnother influential choice in our experiment settings is term weighting: we examined the choices of binary, TF and TF-IDF (the ltc version) schemes.\nWe found TF-IDF most effective for both Rocchio and LR, and used this setting in all our experiments.\n5.3 Percentages of labeled data How much relevance feedback (RF) would be needed during the AF process is a meaningful question in real-world applications.\nTo answer it, we evaluated Rocchio and LR on TDT with the following settings: • Basic Rocchio, no adaptation at all • PRF Rocchio, updating topic profiles without using true relevance feedback; • Adaptive Rocchio, updating topic profiles using relevance feedback on system-accepted documents plus 10 documents randomly sampled from the pool of systemrejected documents; • LR with 0 rr =μ , 01.0=λ and threshold = 0.004; • All the parameters in Rocchio tuned on TDT3.\nTable 3 summarizes the results in Ctrk: Adaptive Rocchio with relevance feedback on 0.6% of the test documents reduced the tracking cost by 54% over the result of the PRF Rocchio, the best system in the TDT2004 evaluation for topic tracking without relevance feedback information.\nIncremental LR, on the other hand, was weaker but still impressive.\nRecall that Ctrk is an extremely high-recall oriented metric, causing frequent updating of profiles and hence an efficiency problem in LR.\nFor this reason we set a higher threshold (0.004) instead of the theoretically optimal threshold (0.0017) in LR to avoid an untolerable computation cost.\nThe computation time in machine-hours was 0.33 for the run of adaptive Rocchio and 14 for the run of LR on TDT5 when optimizing Ctrk.\nTable 4 summarizes the results in TDT5SU; adaptive LR was the winner in this case, with relevance feedback on 0.05% of the test documents improving the utility by 20.9% over the results of PRF Rocchio.\nTable 3: AF methods on TDT5 (Performance in Ctrk) Base Roc PRF Roc Adp Roc LR % of RF 0% 0% 0.6% 0.2% Ctrk 0.076 0.0707 0.0324 0.0382 ±% +7% (baseline) -54% -46% Table 4: AF methods on TDT5 (Performance in TDT5SU) Base Roc PRF Roc Adp Roc LR(λ=.01) % of RF 0% 0% 0.04% 0.05% TDT5SU 0.57 0.6452 0.69 0.78 ±% -11.7% (baseline) +6.9% +20.9% Evidently, both Rocchio and LR are highly effective in adaptive filtering, in terms of using of a small amount of labeled data to significantly improve the model accuracy in statistical learning, which is the main goal of AF.\n5.4 Summary of Adaptation Process After we decided the parameter settings using validation, we perform the adaptive filtering in the following steps for each topic: 1) Train the LR/Rocchio model using the provided positive training examples and 30 randomly sampled negative examples; 2) For each document in the test corpus: we first make a prediction about relevance, and then get relevance feedback for those (predicted) positive documents.\n3) Model and IDF statistics will be incrementally updated if we obtain its true relevance feedback.\n6.\nCONCLUDING REMARKS We presented a cross-benchmark evaluation of incremental Rocchio and incremental LR in adaptive filtering, focusing on their robustness in terms of performance consistency with respect to cross-corpus parameter optimization.\nOur main conclusions from this study are the following: • Parameter optimization in AF is an open challenge but has not been thoroughly studied in the past.\n• Robustness in cross-corpus parameter tuning is important for evaluation and method comparison.\n• We found LR more robust than Rocchio; it had the best results (in T11SU) ever reported on TDT5, TREC10 and TREC11 without extensive tuning.\n• We found Rocchio performs strongly when a good validation corpus is available, and a preferred choice when optimizing Ctrk is the objective, favoring recall over precision to an extreme.\nFor future research we want to study explicit modeling of the temporal trends in topic distributions and content drifting.\nAcknowledgments This material is based upon work supported in parts by the National Science Foundation (NSF) under grant IIS-0434035, by the DoD under award 114008-N66001992891808 and by the Defense Advanced Research Project Agency (DARPA) under Contract No.\nNBCHD030010.\nAny opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the sponsors.\n7.\nREFERENCES [1] J. Allan.\nIncremental relevance feedback for information filtering.\nIn SIGIR-96, 1996.\n[2] J. Callan.\nLearning while filtering documents.\nIn SIGIR-98, 224-231, 1998.\n[3] J. Fiscus and G. Duddington.\nTopic detection and tracking overview.\nIn Topic detection and tracking: event-based information organization, 17-31, 2002.\n[4] J. Fiscus and B. Wheatley.\nOverview of the TDT 2004 Evaluation and Results.\nIn TDT-04, 2004.\n[5] T. Hastie, R. Tibshirani and J. Friedman.\nElements of Statistical Learning.\nSpringer, 2001.\n[6] S. Robertson and D. Hull.\nThe TREC-9 filtering track final report.\nIn TREC-9, 2000.\n[7] S. Robertson and I. Soboroff.\nThe TREC-10 filtering track final report.\nIn TREC-10, 2001.\n[8] S. Robertson and I. Soboroff.\nThe TREC 2002 filtering track report.\nIn TREC-11, 2002.\n[9] S. Robertson and S. Walker.\nMicrosoft Cambridge at TREC-9.\nIn TREC-9, 2000.\n[10] R. Schapire, Y. Singer and A. Singhal.\nBoosting and Rocchio applied to text filtering.\nIn SIGIR-98, 215-223, 1998.\n[11] Y. Yang and B. Kisiel.\nMargin-based local regression for adaptive filtering.\nIn CIKM-03, 2003.\n[12] Y. Zhang and J. Callan.\nMaximum likelihood estimation for filtering thresholds.\nIn SIGIR-01, 2001.\n[13] Y. Zhang.\nUsing Bayesian priors to combine classifiers for adaptive filtering.\nIn SIGIR-04, 2004.\n[14] J. Zhang and Y. Yang.\nRobustness of regularized linear classification methods in text categorization.\nIn SIGIR-03: 190-197, 2003.\n[15] T. Zhang, F. J. Oles.\nText Categorization Based on Regularized Linear Classification Methods.\nInf.\nRetr.\n4(1): 5-31 (2001)."},"lvl-2":{"kind":"string","value":"Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation\nABSTRACT\nThis paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering.\nUsing four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granularity levels, and measured performance with different utility settings.\nWe found that LR performs strongly and robustly in optimizing T11SU (a TREC utility function) while Rocchio is better for optimizing Ctrk (the TDT tracking cost), a high-recall oriented objective function.\nUsing systematic cross-corpus parameter optimization with both methods, we obtained the best results ever reported on TDT5, TREC10 and TREC11.\nRelevance feedback on a small portion (0.05 ~ 0.2%) of the TDT5 test documents yielded significant performance improvements, measuring up to a 54% reduction in Ctrk and a 20.9% increase in T11SU (with β = 0.1), compared to the results of the top-performing system in TDT2004 without relevance feedback information.\n1.\nINTRODUCTION\nAdaptive filtering (AF) has been a challenging research topic in information retrieval.\nThe task is for the system to make an online topic membership decision (yes or no) for every document, as soon as it arrives, with respect to each pre-defined\ntopic of interest.\nStarting from 1997 in the Topic Detection and Tracking (TDT) area and 1998 in the Text Retrieval Conferences (TREC), benchmark evaluations have been conducted by NIST under the following conditions [6] [7] [8] [3] [4]: • A very small number (1 to 4) of positive training examples was provided for each topic at the starting point.\n• Relevance feedback was available but only for the systemaccepted documents (with a \"yes\" decision) in the TREC evaluations for AF.\n• Relevance feedback (RF) was not allowed in the TDT evaluations for AF (or topic tracking in the TDT terminology) until 2004.\n• TDT2004 was the first time that TREC and TDT metrics were jointly used in evaluating AF methods on the same benchmark (the TDT5 corpus) where non-stationary topics dominate.\nThe above conditions attempt to mimic realistic situations where an AF system would be used.\nThat is, the user would be willing to provide a few positive examples for each topic of interest at the start, and might or might not be able to provide additional labeling on a small portion of incoming documents through relevance feedback.\nFurthermore, topics of interest might change over time, with new topics appearing and growing, and old topics shrinking and diminishing.\nThese conditions make adaptive filtering a difficult task in statistical learning (online classification), for the following reasons:\n1) it is difficult to learn accurate models for prediction based on extremely sparse training data; 2) it is not obvious how to correct the sampling bias (i.e., relevance feedback on system-accepted documents only) during the adaptation process; 3) it is not well understood how to effectively tune parameters in AF methods using cross-corpus validation where the validation and evaluation topics do not overlap, and the documents may be from different sources or different epochs.\nNone of these problems is addressed in the literature of statistical learning for batch classification where all the training data are given at once.\nThe first two problems have been studied in the adaptive filtering literature, including topic profile adaptation using incremental Rocchio, Gaussian-Exponential density models, logistic regression in a Bayesian framework, etc., and threshold optimization strategies using probabilistic\ncalibration or local fitting techniques [1] [2] [9] [10] [11] [12] [13].\nAlthough these works provide valuable insights for understanding the problems and possible solutions, it is difficult to draw conclusions regarding the effectiveness and robustness of current methods because the third problem has not been thoroughly investigated.\nAddressing the third issue is the main focus in this paper.\nWe argue that robustness is an important measure for evaluating and comparing AF methods.\nBy \"robust\" we mean consistent and strong performance across benchmark corpora with a systematic method for parameter tuning across multiple corpora.\nMost AF methods have pre-specified parameters that may influence the performance significantly and that must be determined before the test process starts.\nAvailable training examples, on the other hand, are often insufficient for tuning the parameters.\nIn TDT5, for example, there is only one labeled training example per topic at the start; parameter optimization on such training data is doomed to be ineffective.\nThis leaves only one option (assuming tuning on the test set is not an alternative), that is, choosing an external corpus as the validation set.\nNotice that the validation-set topics often do not overlap with the test-set topics, thus the parameter optimization is performed under the tough condition that the validation data and the test data may be quite different from each other.\nNow the important question is: which methods (if any) are robust under the condition of using cross-corpus validation to tune parameters?\nCurrent literature does not offer an answer because no thorough investigation on the robustness of AF methods has been reported.\nIn this paper we address the above question by conducting a cross-benchmark evaluation with two effective approaches in AF: incremental Rocchio and regularized logistic regression (LR).\nRocchio-style classifiers have been popular in AF, with good performance in benchmark evaluations (TREC and TDT) if appropriate parameters were used and if combined with an effective threshold calibration strategy [2] [4] [7] [8] [9] [11] [13].\nLogistic regression is a classical method in statistical learning, and one of the best in batch-mode text categorization [15] [14].\nIt was recently evaluated in adaptive filtering and was found to have relatively strong performance (Section 5.1).\nFurthermore, a recent paper [13] reported that the joint use of Rocchio and LR in a Bayesian framework outperformed the results of using each method alone on the TREC11 corpus.\nStimulated by those findings, we decided to include Rocchio and LR in our crossbenchmark evaluation for robustness testing.\nSpecifically, we focus on how much the performance of these methods depends on parameter tuning, what the most influential parameters are in these methods, how difficult (or how easy) to optimize these influential parameters using cross-corpus validation, how strong these methods perform on multiple benchmarks with the systematic tuning of parameters on other corpora, and how efficient these methods are in running AF on large benchmark corpora.\nThe organization of the paper is as follows: Section 2 introduces the four benchmark corpora (TREC10 and TREC11, TDT3 and TDT5) used in this study.\nSection 3 analyzes the differences among the TREC and TDT metrics (utilities and tracking cost) and the potential implications of those differences.\nSection 4 outlines the Rocchio and LR approaches to AF, respectively.\nSection 5 reports the experiments and results.\nSection 6 concludes the main findings in this study.\n2.\nBENCHMARK CORPORA\nWe used four benchmark corpora in our study.\nTable 1 shows the statistics about these data sets.\nTREC10 was the evaluation benchmark for adaptive filtering in TREC 2001, consisting of roughly 806,791 Reuters news stories from August 1996 to August 1997 with 84 topic labels (subject categories) [7].\nThe first two weeks (August 20th to 31st, 1996) of documents is the training set, and the remaining 11 & 1/2 months (from September 1st, 1996 to August 19th, 1997) is the test set.\nTREC11 was the evaluation benchmark for adaptive filtering in TREC 2002, consisting of the same set of documents as those in TREC10 but with a slightly different splitting point for the training and test sets.\nThe TREC11 topics (50) are quite different from those in TREC10; they are queries for retrieval with relevance judgments by NIST assessors [8].\nTDT3 was the evaluation benchmark in the TDT2001 dry run1.\nThe tracking part of the corpus consists of 71,388 news stories from multiple sources in English and Mandarin (AP, NYT, CNN, ABC, NBC, MSNBC, Xinhua, Zaobao, Voice of America and PRI the World) in the period of October to December 1998.\nMachine-translated versions of the non-English stories (Xinhua, Zaobao and VOA Mandarin) are provided as well.\nThe splitting point for training-test sets is different for each topic in TDT.\nTDT5 was the evaluation benchmark in TDT2004 [4].\nThe tracking part of the corpus consists of 407,459 news stories in the period of April to September, 2003 from 15 news agents or broadcast sources in English, Arabic and Mandarin, with machine-translated versions of the non-English stories.\nWe only used the English versions of those documents in our experiments for this paper.\nThe TDT \"topics\" differ from TREC topics both conceptually and statistically.\nInstead of generic, ever-lasting subject categories (as those in TREC), TDT topics are defined at a finer level of granularity, for events that happen at certain times and locations, and that are \"born\" and \"die\", typically associated with a bursty distribution over chronologically ordered news stories.\nThe average size of TDT topics (events) is two orders of magnitude smaller than that of the TREC10 topics.\nFigure 1 compares the document densities of a TREC topic (\"Civil Wars\") and two TDT topics (\"Gunshot\" and \"APEC Summit Meeting\", respectively) over a 3-month time period, where the area under each curve is normalized to one.\nThe granularity differences among topics and the corresponding non-stationary distributions make the cross-benchmark evaluation interesting.\nFor example, algorithms favoring large and stable topics may not work well for short-lasting and nonstationary topics, and vice versa.\nCross-benchmark evaluations allow us to test this hypothesis and possibly identify the weaknesses in current approaches to adaptive filtering in tracking the drifting trends of topics.\nTable 1: Statistics of benchmark corpora for adaptive filtering evaluations\nFigure 1: The temporal nature of topics\n3.\nMETRICS\nTo make our results comparable to the literature, we decided to use both TREC-conventional and TDT-conventional metrics in our evaluation.\n3.1 TREC11 metrics\nLet A, B, C and D be, respectively, the numbers of true positives, false alarms, misses and true negatives for a specific topic, and N = A + B + C + D be the total number of test documents.\nThe TREC-conventional metrics are defined as:\nwhere parameters β and η were set to 0.5 and -0.5 respectively in TREC10 (2001) and TREC11 (2002).\nFor evaluating the performance of a system, the performance scores are computed\n3.3 The correlations and the differences\nFrom an optimization point of view, TDT5SU and T11SU are both utility functions while Ctrk is a cost function.\nOur objective for individual topics first and then averaged over topics (macroaveraging).\n3.2 TDT metrics\nThe TDT-conventional metric for topic tracking is defined as:\nwhere P (T) is the percentage of documents on topic T, Pmiss is the miss rate by the system on that topic, Pfa is the false alarm rate, and w1 and w2 are the costs (pre-specified constants) for a miss and a false alarm, respectively.\nThe TDT benchmark evaluations (since 1997) have used the settings of w1 = 1, w2 = 0.\n1 and P (T) = 0.02 for all topics.\nFor evaluating the performance of a system, Ctrk is computed for each topic first and then the resulting scores are averaged for a single measure (the topic-weighted Ctrk).\nTo make the intuition behind this measure transparent, we substitute the terms in the definition of Ctrk as follows:\nClearly, Ctrk is the average cost per error on topic T, with w1 and w2 controlling the penalty ratio for misses vs. false alarms.\nIn addition to Ctrk, TDT2004 also employed T 11 SUβ = 0.\n1 as a utility metric.\nTo distinguish this from the T 11SUβ = 0 .5 in TREC11, we call former TDT5SU in the rest of this paper.\nis to maximize the former or to minimize the latter on test documents.\nThe differences and correlations among these objective functions can be analyzed through the shared counts of A, B, C and D in their definitions.\nFor example, both TDT5SU\nand T11SU are positively correlated to the values of A and D, and negatively correlated to the values of B and C; the only difference between them is in their penalty ratios for misses vs. false alarms, i.e., 10:1 in TDT5SU and 2:1 in T11SU.\nThe Ctrk function, on the other hand, is positively correlated to the values of C and B, and negatively correlated to the values of A and D; hence, it is negatively correlated to T11SU and TDT5SU.\nMore importantly, there is a subtle and major difference between Ctrk and the utility functions: T11SU and TDT5SU.\nThat is, Ctrk has a very different penalty ratio for misses vs. false alarms: it favors recall-oriented systems to an extreme.\nAt first glance, one would think that the penalty ratio in Ctrk is 10:1 since w1 = 1 and w2 = 0.\n1.\nHowever, this is not true if P (T) = 0 .02 is an inaccurate estimate of the on-topic documents on average for the test corpus.\nUsing TDT3 as an example, the true percentage is:\nwhere N is the average size of the test sets in TDT3, and n + is the average number of positive examples per topic in the test sets.\nUsing Pˆ (T) = 0.02 as an (inaccurate) estimate of 0.002 enlarges the intended penalty ratio of 10:1 to 100:1, roughly speaking.\nTo wit:\nestimation of P (T) compared to the truth.\nComparing the above result to formula 2, we can see the actual penalty ratio for misses vs. false alarms was 100:1 in the evaluations on TDT3 using Ctrk.\nSimilarly, we can compute the enlargement factor for TDT5 using the statistics in Table 1 as follows: which means the actual penalty ratio for misses vs. false alarms in the evaluation on TDT5 using Ctrk was approximately 583:1.\nThe implications of the above analysis are rather significant:\n• Ctrk defined in the same formula does not necessarily mean the same objective function in evaluation; instead, the optimization criterion depends on the test corpus.\n• Systems optimized for Ctrk would not optimize TDT5SU (and T11SU) because the former favors high-recall oriented to an extreme while the latter does not.\n• Parameters tuned on one corpus (e.g., TDT3) might not work for an evaluation on another corpus (say, TDT5) unless we account for the previously-unknown subtle dependency of Ctrk on data.\n• Results in Ctrk in the past years of TDT evaluations may not be directly comparable to each other because the evaluation collections changed most years and hence the penalty ratio in Ctrk varied.\nAlthough these problems with Ctrk were not originally anticipated, it offered an opportunity to examine the ability of systems in trading off precision for extreme recall.\nThis was a challenging part of the TDT2004 evaluation for AF.\nComparing the metrics in TDT and TREC from a utility or cost optimization point of view is important for understanding the evaluation results of adaptive filtering methods.\nThis is the first time this issue is explicitly analyzed, to our knowledge.\n4.\nMETHODS\n4.1 Incremental Rocchio for AF\nWe employed a common version of Rocchio-style classifiers which computes a prototype vector per topic (T) as follows:\nThe first term on the RHS is the weighted vector representation of topic description whose elements are terms weights.\nThe second term is the weighted centroid of the set D + (T) of positive training examples, each of which is a vector of withindocument term weights.\nThe third term is the weighted centroid of the set D − (T) of negative training examples which are the nearest neighbors of the positive centroid.\nThe three terms are given pre-specified weights of α, β and γ, controlling the relative influence of these components in the prototype.\nThe prototype of a topic is updated each time the system makes a \"yes\" decision on a new document for that topic.\nIf relevance feedback is available (as is the case in TREC adaptive filtering), the new document is added to the pool of either D + (T) or D − (T), and the prototype is recomputed accordingly; if relevance feedback is not available (as is the case in TDT event tracking), the system's prediction (\"yes\") is treated as the truth, and the new document is added to D + (T) for updating the prototype.\nBoth cases are part of our experiments in this paper (and part of the TDT 2004 evaluations for AF).\nTo distinguish the two, we call the first case simply \"Rocchio\" and the second case \"PRF Rocchio\" where PRF stands for pseudorelevance feedback.\nThe predictions on a new document are made by computing the cosine similarity between each topic prototype and the document vector, and then comparing the resulting scores against a threshold:\nThreshold calibration in incremental Rocchio is a challenging research topic.\nMultiple approaches have been developed.\nThe simplest is to use a universal threshold for all topics, tuned on a validation set and fixed during the testing phase.\nMore elaborate methods include probabilistic threshold calibration which converts the non-probabilistic similarity scores to probabilities r (i.e., P (T | d)) for utility optimization [9] [13], and margin-based local regression for risk reduction [11].\nIt is beyond the scope of this paper to compare all the different ways to adapt Rocchio-style methods for AF.\nInstead, our focus here is to investigate the robustness of Rocchio-style methods in terms of how much their performance depends on elaborate system tuning, and how difficult (or how easy) it is to get good performance through cross-corpus parameter optimization.\nHence, we decided to use a relatively simple version of Rocchio as the baseline, i.e., with a universal threshold tuned on a validation corpus and fixed for all topics in the testing phase.\nThis simple version of Rocchio has been commonly used in the past TDT benchmark evaluations for topic tracking, and had strong performance in the TDT2004 evaluations for adaptive filtering with and without relevance feedback (Section 5.1).\nResults of more complex variants of Rocchio are also discussed when relevant.\n4.2 Logistic Regression for AF\nLogistic regression (LR) estimates the posterior probability of a topic given a document using a sigmoid function\nwhere xr is the document vector whose elements are term r weights, w is the vector of regression coefficients, and y ∈ {+1, − 1} is the output variable corresponding to \"yes\" or \"no\" with respect to a particular topic.\nGiven a training set of labeled documents D {(x1, y1),, (xrn, yn)} r = L, the standard regression problem is defined as to find the maximum likelihood estimates of the regression coefficients (\"the model parameters\"):\nThis is a convex optimization problem which can be solved using a standard conjugate gradient algorithm in O (INF) time for training per topic, where I is the average number of iterations needed for convergence, and N and F are the number of training documents and number of features respectively [14].\nOnce the regression coefficients are optimized on the training data, the filtering prediction on each incoming document is made as:\nNote that wr is constantly updated whenever a new relevance judgment is available in the testing phase of AF, while the optimal threshold θopt is constant, depending only on the predefined utility (or cost) function for evaluation.\nIf T11SU is the metric, for example, with the penalty ratio of 2:1 for misses and false alarms (Section 3.1), the optimal threshold for LR is 1 / (2 + 1) = 0.\n3 3 for all topics.\nWe modified the standard (above) version of LR to allow more flexible optimization criteria as follows:\nwhere s (yi) is taken to be α, β and γ for query, positive and negative documents respectively, which are similar to those in Rocchio, giving different weights to the three kinds of training examples: topic descriptions (\"queries\"), on-topic documents and off-topic documents.\nThe second term in the objective function is for regularization, equivalent to adding a Gaussian prior to the regression coefficients with mean µ r and covariance variance matrix 1 / 2λ ⋅ Ι, where Ι is the identity matrix.\nTuning λ (≥ 0) is theoretically justified for reducing model complexity (\"the effective degree of freedom\") and avoiding over-fitting on training data [5].\nHow to find an effective µr is an open issue for research, depending on the user's belief about the parameter space and the optimal range.\nThe solution of the modified objective function is called the Maximum A Posteriori (MAP) estimate, which reduces to the maximum likelihood solution for standard LR if λ = 0.\n5.\nEVALUATIONS\nWe report our empirical findings in four parts: the TDT2004 official evaluation results, the cross-corpus parameter optimization results, and the results corresponding to the amounts of relevance feedback.\n5.1 TDT2004 benchmark results\nThe TDT2004 evaluations for adaptive filtering were conducted by NIST in November 2004.\nMultiple research teams participated and multiple runs from each team were allowed.\nCtrk and TDT5SU were used as the metrics.\nFigure 2 and Figure 3 show the results; the best run from each team was selected with respect to Ctrk or TDT5SU, respectively.\nOur Rocchio (with adaptive profiles but fixed universal threshold for all topics) had the best result in Ctrk, and our logistic regression had the best result in TDT5SU.\nAll the parameters of our runs were tuned on the TDT3 corpus.\nResults for other sites are also listed anonymously for comparison.\nFigure 2: TDT2004 results in Ctrk of systems using true relevance feedback.\n(\"Ours\" is the Rocchio method.)\nWe also put the 1st and 3rd quartiles as sticks for each site .2 Figure 3: TDT2004 results in TDT5SU of systems using true relevance feedback.\n(\"Ours\" is LR with µr = 0 and λ = 0.005).\nFigure 4: TDT2004 results in Ctrk of systems without using true relevance feedback.\n(\"Ours\" is PRF Rocchio.)\nAdaptive filtering without using true relevance feedback was also a part of the evaluations.\nIn this case, systems had only one labeled training example per topic during the entire training and testing processes, although unlabeled test documents could be used as soon as predictions on them were made.\nSuch a setting has been conventional for the Topic Tracking task in TDT until 2004.\nFigure 4 shows the summarized official submissions from each team.\nOur PRF Rocchio (with a fixed threshold for all the topics) had the best performance.\n2 We use quartiles rather than standard deviations since the former is more resistant to outliers.\n5.2 Cross-corpus parameter optimization\nHow much the strong performance of our systems depends on parameter tuning is an important question.\nBoth Rocchio and LR have parameters that must be prespecified before the AF process.\nThe shared parameters include the sample weights α, β and γ, the sample size of the negative training documents (i.e., D − (T)), the term-weighting scheme, and the maximal number of non-zero elements in each document vector.\nThe method-specific parameters include the decision threshold in Rocchio, and µr, λ and MI (the maximum number of iterations in training) in LR.\nGiven that we only have one labeled example per topic in the TDT5 training sets, it is impossible to effectively optimize these parameters on the training data, and we had to choose an external corpus for validation.\nAmong the choices of TREC10, TREC11 and TDT3, we chose TDT3 (c.f. Section 2) because it is most similar to TDT5 in terms of the nature of the topics (Section 2).\nWe optimized the parameters of our systems on TDT3, and fixed those parameters in the runs on TDT5 for our submissions to TDT2004.\nWe also tested our methods on TREC10 and TREC11 for further analysis.\nSince exhaustive testing of all possible parameter settings is computationally intractable, we followed a step-wise forward chaining procedure instead: we pre-specified an order of the parameters in a method (Rocchio or LR), and then tuned one parameter at the time while fixing the settings of the remaining parameters.\nWe repeated this procedure for several passes as time allowed.\nFigure 5: Performance curves of adaptive Rocchio\nFigure 5 compares the performance curves in TDT5SU for Rocchio on TDT3, TDT5, TREC10 and TREC11 when the decision threshold varied.\nThese curves peak at different locations: the TDT3-optimal is closest to the TDT5-optimal while the TREC10-optimal and TREC1-optimal are quite far away from the TDT5-optimal.\nIf we were using TREC10 or TREC11 instead of TDT3 as the validation corpus for TDT5, or if the TDT3 corpus were not available, we would have difficulty in obtaining strong performance for Rocchio in TDT2004.\nThe difficulty comes from the ad hoc (non-probabilistic) scores generated by the Rocchio method: the distribution of the scores depends on the corpus, making cross-corpus threshold optimization a tricky problem.\nLogistic regression has less difficulty with respect to threshold tuning because it produces probabilistic scores of Pr (y = 1 | x)\nupon which the optimal threshold can be directly computed if probability estimation is accurate.\nGiven the penalty ratio for misses vs. false alarms as 2:1 in T11SU, 10:1 in TDT5SU and 583:1 in Ctrk (Section 3.3), the corresponding optimal thresholds (t) are 0.33, 0.091 and 0.0017 respectively.\nAlthough the theoretical threshold could be inaccurate, it still suggests the range of near-optimal settings.\nWith these threshold settings in our experiments for LR, we focused on the cross-corpus validation of the Bayesian prior parameters, that is, μr and X. Table 2 summarizes the results3.\nWe measured the performance of the runs on TREC10 and TREC11 using T11SU, and the performance of the runs on TDT3 and TDT5 using TDT5SU.\nFor comparison we also include the best results of Rocchio-based methods on these corpora, which are our own results of Rocchio on TDT3 and TDT5, and the best results reported by NIST for TREC10 and TREC11.\nFrom this set of results, we see that LR significantly outperformed Rocchio on all the corpora, even in the runs of standard LR without any tuning, i.e. X = 0.\nThis empirical finding is consistent with a previous report [13] for LR on TREC11 although our results of LR (0.585 ~ 0.608 in T11SU) are stronger than the results (0.49 for standard LR and 0.54 for LR using Rocchio prototype as the prior) in that report.\nMore importantly, our cross-benchmark evaluation gives strong evidence for the robustness of LR.\nThe robustness, we believe, comes from the probabilistic nature of the system-generated scores.\nThat is, compared to the ad hoc scores in Rocchio, the normalized posterior probabilities make the threshold optimization in LR a much easier problem.\nMoreover, logistic regression is known to converge towards the Bayes classifier asymptotically while Rocchio classifiers' parameters do not.\nAnother interesting observation in these results is that the performance of LR did not improve when using a Rocchio prototype as the mean in the prior; instead, the performance decreased in some cases.\nThis observation does not support the previous report by [13], but we are not surprised because we are not convinced that Rocchio prototypes are more accurate than LR models for topics in the early stage of the AF process, and we believe that using a Rocchio prototype as the mean in the Gaussian prior would introduce undesirable bias to LR.\nWe also believe that variance reduction (in the testing phase) should be controlled by the choice of X (but not μr), for which we conducted the experiments as shown in Figure 6.\nTable 2: Results of LR with different Bayesian priors\n3 The LR results (0.77 ~ 0.78) on TDT5 in this table are better than our TDT2004 official result (0.73) because parameter optimization has been improved afterwards.\n4 The TREC10-best result (0.496 by Oracle) is only available in T10U which is not directly comparable to the scores in T11SU, just indicative.\n*: μr was set to the Rocchio prototype\nFigure 6: LR with varying lambda.\nThe performance of LR is summarized with respect to X tuning on the corpora of TREC10, TREC11 and TDT3.\nThe performance on each corpus was measured using the corresponding metrics, that is, T11SU for the runs on TREC10 and TREC11, and TDT5SU and Ctrk for the runs on TDT3,.\nIn the case of maximizing the utilities, the \"safe\" interval for X is between 0 and 0.01, meaning that the performance of regularized LR is stable, the same as or improved slightly over the performance of standard LR.\nIn the case of minimizing Ctrk, the safe range for X is between 0 and 0.1, and setting λ between 0.005 and 0.05 yielded relatively large improvements over the performance of standard LR because training a model for extremely high recall is statistically more tricky, and hence more regularization is needed.\nIn either case, tuning X is relatively safe, and easy to do successfully by cross-corpus tuning.\nAnother influential choice in our experiment settings is term weighting: we examined the choices of binary, TF and TF-IDF (the \"ltc\" version) schemes.\nWe found TF-IDF most effective for both Rocchio and LR, and used this setting in all our experiments.\n5.3 Percentages of labeled data\nHow much relevance feedback (RF) would be needed during the AF process is a meaningful question in real-world applications.\nTo answer it, we evaluated Rocchio and LR on TDT with the following settings:\n• Basic Rocchio, no adaptation at all • PRF Rocchio, updating topic profiles without using true relevance feedback; • Adaptive Rocchio, updating topic profiles using relevance feedback on system-accepted documents plus 10 • documents randomly sampled from the pool of systemrejected documents;\n• All the parameters in Rocchio tuned on TDT3.\nTable 3 summarizes the results in Ctrk: Adaptive Rocchio with relevance feedback on 0.6% of the test documents reduced the tracking cost by 54% over the result of the PRF Rocchio, the best system in the TDT2004 evaluation for topic tracking without relevance feedback information.\nIncremental LR, on the other hand, was weaker but still impressive.\nRecall that Ctrk is an extremely high-recall oriented metric, causing frequent updating of profiles and hence an efficiency problem in LR.\nFor this reason we set a higher threshold (0.004) instead of the theoretically optimal threshold (0.0017) in LR to avoid an untolerable computation cost.\nThe computation time in machinehours was 0.33 for the run of adaptive Rocchio and 14 for the run of LR on TDT5 when optimizing Ctrk.\nTable 4 summarizes the results in TDT5SU; adaptive LR was the winner in this case, with relevance feedback on 0.05% of the test documents improving the utility by 20.9% over the results of PRF Rocchio.\nTable 3: AF methods on TDT5 (Performance in Ctrk)\nTable 4: AF methods on TDT5 (Performance in TDT5SU)\nEvidently, both Rocchio and LR are highly effective in adaptive filtering, in terms of using of a small amount of labeled data to significantly improve the model accuracy in statistical learning, which is the main goal of AF.\n5.4 Summary of Adaptation Process\nAfter we decided the parameter settings using validation, we perform the adaptive filtering in the following steps for each topic: 1) Train the LR/Rocchio model using the provided positive training examples and 30 randomly sampled negative examples; 2) For each document in the test corpus: we first make a prediction about relevance, and then get relevance feedback for those (predicted) positive documents.\n3) Model and IDF statistics will be incrementally updated if we obtain its true relevance feedback.\n6.\nCONCLUDING REMARKS\nWe presented a cross-benchmark evaluation of incremental Rocchio and incremental LR in adaptive filtering, focusing on their robustness in terms of performance consistency with respect to cross-corpus parameter optimization.\nOur main conclusions from this study are the following:\n• Parameter optimization in AF is an open challenge but has not been thoroughly studied in the past.\n• Robustness in cross-corpus parameter tuning is important for evaluation and method comparison.\n• We found LR more robust than Rocchio; it had the best results (in T11SU) ever reported on TDT5, TREC10 and TREC11 without extensive tuning.\n• We found Rocchio performs strongly when a good\nvalidation corpus is available, and a preferred choice when optimizing Ctrk is the objective, favoring recall over precision to an extreme.\nFor future research we want to study explicit modeling of the temporal trends in topic distributions and content drifting."},"lvl-3":{"kind":"string","value":"Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation\nABSTRACT\nThis paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering.\nUsing four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granularity levels, and measured performance with different utility settings.\nWe found that LR performs strongly and robustly in optimizing T11SU (a TREC utility function) while Rocchio is better for optimizing Ctrk (the TDT tracking cost), a high-recall oriented objective function.\nUsing systematic cross-corpus parameter optimization with both methods, we obtained the best results ever reported on TDT5, TREC10 and TREC11.\nRelevance feedback on a small portion (0.05 ~ 0.2%) of the TDT5 test documents yielded significant performance improvements, measuring up to a 54% reduction in Ctrk and a 20.9% increase in T11SU (with β = 0.1), compared to the results of the top-performing system in TDT2004 without relevance feedback information.\n1.\nINTRODUCTION\nAdaptive filtering (AF) has been a challenging research topic in information retrieval.\nThe task is for the system to make an online topic membership decision (yes or no) for every document, as soon as it arrives, with respect to each pre-defined\ntopic of interest.\nStarting from 1997 in the Topic Detection and Tracking (TDT) area and 1998 in the Text Retrieval Conferences (TREC), benchmark evaluations have been conducted by NIST under the following conditions [6] [7] [8] [3] [4]: • A very small number (1 to 4) of positive training examples was provided for each topic at the starting point.\n• Relevance feedback was available but only for the systemaccepted documents (with a \"yes\" decision) in the TREC evaluations for AF.\n• Relevance feedback (RF) was not allowed in the TDT evaluations for AF (or topic tracking in the TDT terminology) until 2004.\n• TDT2004 was the first time that TREC and TDT metrics were jointly used in evaluating AF methods on the same benchmark (the TDT5 corpus) where non-stationary topics dominate.\nThe above conditions attempt to mimic realistic situations where an AF system would be used.\nThat is, the user would be willing to provide a few positive examples for each topic of interest at the start, and might or might not be able to provide additional labeling on a small portion of incoming documents through relevance feedback.\nFurthermore, topics of interest might change over time, with new topics appearing and growing, and old topics shrinking and diminishing.\nThese conditions make adaptive filtering a difficult task in statistical learning (online classification), for the following reasons:\n1) it is difficult to learn accurate models for prediction based on extremely sparse training data; 2) it is not obvious how to correct the sampling bias (i.e., relevance feedback on system-accepted documents only) during the adaptation process; 3) it is not well understood how to effectively tune parameters in AF methods using cross-corpus validation where the validation and evaluation topics do not overlap, and the documents may be from different sources or different epochs.\nNone of these problems is addressed in the literature of statistical learning for batch classification where all the training data are given at once.\nThe first two problems have been studied in the adaptive filtering literature, including topic profile adaptation using incremental Rocchio, Gaussian-Exponential density models, logistic regression in a Bayesian framework, etc., and threshold optimization strategies using probabilistic\ncalibration or local fitting techniques [1] [2] [9] [10] [11] [12] [13].\nAlthough these works provide valuable insights for understanding the problems and possible solutions, it is difficult to draw conclusions regarding the effectiveness and robustness of current methods because the third problem has not been thoroughly investigated.\nAddressing the third issue is the main focus in this paper.\nWe argue that robustness is an important measure for evaluating and comparing AF methods.\nBy \"robust\" we mean consistent and strong performance across benchmark corpora with a systematic method for parameter tuning across multiple corpora.\nMost AF methods have pre-specified parameters that may influence the performance significantly and that must be determined before the test process starts.\nAvailable training examples, on the other hand, are often insufficient for tuning the parameters.\nIn TDT5, for example, there is only one labeled training example per topic at the start; parameter optimization on such training data is doomed to be ineffective.\nThis leaves only one option (assuming tuning on the test set is not an alternative), that is, choosing an external corpus as the validation set.\nNotice that the validation-set topics often do not overlap with the test-set topics, thus the parameter optimization is performed under the tough condition that the validation data and the test data may be quite different from each other.\nNow the important question is: which methods (if any) are robust under the condition of using cross-corpus validation to tune parameters?\nCurrent literature does not offer an answer because no thorough investigation on the robustness of AF methods has been reported.\nIn this paper we address the above question by conducting a cross-benchmark evaluation with two effective approaches in AF: incremental Rocchio and regularized logistic regression (LR).\nRocchio-style classifiers have been popular in AF, with good performance in benchmark evaluations (TREC and TDT) if appropriate parameters were used and if combined with an effective threshold calibration strategy [2] [4] [7] [8] [9] [11] [13].\nLogistic regression is a classical method in statistical learning, and one of the best in batch-mode text categorization [15] [14].\nIt was recently evaluated in adaptive filtering and was found to have relatively strong performance (Section 5.1).\nFurthermore, a recent paper [13] reported that the joint use of Rocchio and LR in a Bayesian framework outperformed the results of using each method alone on the TREC11 corpus.\nStimulated by those findings, we decided to include Rocchio and LR in our crossbenchmark evaluation for robustness testing.\nSpecifically, we focus on how much the performance of these methods depends on parameter tuning, what the most influential parameters are in these methods, how difficult (or how easy) to optimize these influential parameters using cross-corpus validation, how strong these methods perform on multiple benchmarks with the systematic tuning of parameters on other corpora, and how efficient these methods are in running AF on large benchmark corpora.\nThe organization of the paper is as follows: Section 2 introduces the four benchmark corpora (TREC10 and TREC11, TDT3 and TDT5) used in this study.\nSection 3 analyzes the differences among the TREC and TDT metrics (utilities and tracking cost) and the potential implications of those differences.\nSection 4 outlines the Rocchio and LR approaches to AF, respectively.\nSection 5 reports the experiments and results.\nSection 6 concludes the main findings in this study.\n2.\nBENCHMARK CORPORA\n3.\nMETRICS\n3.1 TREC11 metrics\n3.3 The correlations and the differences\n3.2 TDT metrics\n4.\nMETHODS\n4.1 Incremental Rocchio for AF\n4.2 Logistic Regression for AF\n5.\nEVALUATIONS\n5.1 TDT2004 benchmark results\n5.2 Cross-corpus parameter optimization\n5.3 Percentages of labeled data\n5.4 Summary of Adaptation Process\n6.\nCONCLUDING REMARKS\nWe presented a cross-benchmark evaluation of incremental Rocchio and incremental LR in adaptive filtering, focusing on their robustness in terms of performance consistency with respect to cross-corpus parameter optimization.\nOur main conclusions from this study are the following:\n• Parameter optimization in AF is an open challenge but has not been thoroughly studied in the past.\n• Robustness in cross-corpus parameter tuning is important for evaluation and method comparison.\n• We found LR more robust than Rocchio; it had the best results (in T11SU) ever reported on TDT5, TREC10 and TREC11 without extensive tuning.\n• We found Rocchio performs strongly when a good\nvalidation corpus is available, and a preferred choice when optimizing Ctrk is the objective, favoring recall over precision to an extreme.\nFor future research we want to study explicit modeling of the temporal trends in topic distributions and content drifting."},"lvl-4":{"kind":"string","value":"Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation\nABSTRACT\nThis paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering.\nUsing four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granularity levels, and measured performance with different utility settings.\nWe found that LR performs strongly and robustly in optimizing T11SU (a TREC utility function) while Rocchio is better for optimizing Ctrk (the TDT tracking cost), a high-recall oriented objective function.\nUsing systematic cross-corpus parameter optimization with both methods, we obtained the best results ever reported on TDT5, TREC10 and TREC11.\nRelevance feedback on a small portion (0.05 ~ 0.2%) of the TDT5 test documents yielded significant performance improvements, measuring up to a 54% reduction in Ctrk and a 20.9% increase in T11SU (with β = 0.1), compared to the results of the top-performing system in TDT2004 without relevance feedback information.\n1.\nINTRODUCTION\nAdaptive filtering (AF) has been a challenging research topic in information retrieval.\nThe task is for the system to make an online topic membership decision (yes or no) for every document, as soon as it arrives, with respect to each pre-defined\ntopic of interest.\n• Relevance feedback was available but only for the systemaccepted documents (with a \"yes\" decision) in the TREC evaluations for AF.\n• Relevance feedback (RF) was not allowed in the TDT evaluations for AF (or topic tracking in the TDT terminology) until 2004.\n• TDT2004 was the first time that TREC and TDT metrics were jointly used in evaluating AF methods on the same benchmark (the TDT5 corpus) where non-stationary topics dominate.\nThe above conditions attempt to mimic realistic situations where an AF system would be used.\nFurthermore, topics of interest might change over time, with new topics appearing and growing, and old topics shrinking and diminishing.\nThese conditions make adaptive filtering a difficult task in statistical learning (online classification), for the following reasons:\nNone of these problems is addressed in the literature of statistical learning for batch classification where all the training data are given at once.\nThe first two problems have been studied in the adaptive filtering literature, including topic profile adaptation using incremental Rocchio, Gaussian-Exponential density models, logistic regression in a Bayesian framework, etc., and threshold optimization strategies using probabilistic\nAddressing the third issue is the main focus in this paper.\nWe argue that robustness is an important measure for evaluating and comparing AF methods.\nBy \"robust\" we mean consistent and strong performance across benchmark corpora with a systematic method for parameter tuning across multiple corpora.\nMost AF methods have pre-specified parameters that may influence the performance significantly and that must be determined before the test process starts.\nAvailable training examples, on the other hand, are often insufficient for tuning the parameters.\nIn TDT5, for example, there is only one labeled training example per topic at the start; parameter optimization on such training data is doomed to be ineffective.\nNotice that the validation-set topics often do not overlap with the test-set topics, thus the parameter optimization is performed under the tough condition that the validation data and the test data may be quite different from each other.\nNow the important question is: which methods (if any) are robust under the condition of using cross-corpus validation to tune parameters?\nCurrent literature does not offer an answer because no thorough investigation on the robustness of AF methods has been reported.\nIn this paper we address the above question by conducting a cross-benchmark evaluation with two effective approaches in AF: incremental Rocchio and regularized logistic regression (LR).\nRocchio-style classifiers have been popular in AF, with good performance in benchmark evaluations (TREC and TDT) if appropriate parameters were used and if combined with an effective threshold calibration strategy [2] [4] [7] [8] [9] [11] [13].\nLogistic regression is a classical method in statistical learning, and one of the best in batch-mode text categorization [15] [14].\nIt was recently evaluated in adaptive filtering and was found to have relatively strong performance (Section 5.1).\nFurthermore, a recent paper [13] reported that the joint use of Rocchio and LR in a Bayesian framework outperformed the results of using each method alone on the TREC11 corpus.\nStimulated by those findings, we decided to include Rocchio and LR in our crossbenchmark evaluation for robustness testing.\nThe organization of the paper is as follows: Section 2 introduces the four benchmark corpora (TREC10 and TREC11, TDT3 and TDT5) used in this study.\nSection 3 analyzes the differences among the TREC and TDT metrics (utilities and tracking cost) and the potential implications of those differences.\nSection 4 outlines the Rocchio and LR approaches to AF, respectively.\nSection 5 reports the experiments and results.\nSection 6 concludes the main findings in this study.\n6.\nCONCLUDING REMARKS\nWe presented a cross-benchmark evaluation of incremental Rocchio and incremental LR in adaptive filtering, focusing on their robustness in terms of performance consistency with respect to cross-corpus parameter optimization.\nOur main conclusions from this study are the following:\n• Parameter optimization in AF is an open challenge but has not been thoroughly studied in the past.\n• Robustness in cross-corpus parameter tuning is important for evaluation and method comparison.\n• We found LR more robust than Rocchio; it had the best results (in T11SU) ever reported on TDT5, TREC10 and TREC11 without extensive tuning.\n• We found Rocchio performs strongly when a good\nvalidation corpus is available, and a preferred choice when optimizing Ctrk is the objective, favoring recall over precision to an extreme.\nFor future research we want to study explicit modeling of the temporal trends in topic distributions and content drifting."}}},{"rowIdx":94,"cells":{"id":{"kind":"string","value":"C-65"},"title":{"kind":"string","value":"Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors"},"abstract":{"kind":"string","value":"The paper presents a wireless sensor network-based mobile countersniper system. A sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implements the acoustic detection and Time of Arrival (ToA) estimation algorithms on an FPGA. A 3-axis compass provides self orientation and Bluetooth is used for communication with the soldier's PDA running the data fusion and the user interface. The heterogeneous sensor fusion algorithm can work with data from a single sensor or it can fuse ToA or Angle of Arrival (AoA) observations of muzzle blasts and ballistic shockwaves from multiple sensors. The system estimates the trajectory, the range, the caliber and the weapon type. The paper presents the system design and the results from an independent evaluation at the US Army Aberdeen Test Center. The system performance is characterized by 1-degree trajectory precision and over 95% caliber estimation accuracy for all shots, and close to 100% weapon estimation accuracy for 4 out of 6 guns tested."},"keyphrases":{"kind":"list like","value":["weapon classif","internod commun","sensorboard","self orient","data fusion","trajectori","rang","calib","weapon type","calib estim accuraci","calib estim","wireless sensor network-base mobil countersnip system","helmetmount microphon arrai","1degre trajectori precis","sensor network","acoust sourc local"],"string":"[\n \"weapon classif\",\n \"internod commun\",\n \"sensorboard\",\n \"self orient\",\n \"data fusion\",\n \"trajectori\",\n \"rang\",\n \"calib\",\n \"weapon type\",\n \"calib estim accuraci\",\n \"calib estim\",\n \"wireless sensor network-base mobil countersnip system\",\n \"helmetmount microphon arrai\",\n \"1degre trajectori precis\",\n \"sensor network\",\n \"acoust sourc local\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","P","P","P","P","P","P","P","P","M","M","M","R","M"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"M\",\n \"M\",\n \"M\",\n \"R\",\n \"M\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors Peter Volgyesi, Gyorgy Balogh, Andras Nadas, Christopher B. Nash, Akos Ledeczi Institute for Software Integrated Systems, Vanderbilt University Nashville, TN, USA akos.ledeczi@vanderbilt.edu ABSTRACT The paper presents a wireless sensor network-based mobile countersniper system.\nA sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implements the acoustic detection and Time of Arrival (ToA) estimation algorithms on an FPGA.\nA 3-axis compass provides self orientation and Bluetooth is used for communication with the soldier``s PDA running the data fusion and the user interface.\nThe heterogeneous sensor fusion algorithm can work with data from a single sensor or it can fuse ToA or Angle of Arrival (AoA) observations of muzzle blasts and ballistic shockwaves from multiple sensors.\nThe system estimates the trajectory, the range, the caliber and the weapon type.\nThe paper presents the system design and the results from an independent evaluation at the US Army Aberdeen Test Center.\nThe system performance is characterized by 1degree trajectory precision and over 95% caliber estimation accuracy for all shots, and close to 100% weapon estimation accuracy for 4 out of 6 guns tested.\nCategories and Subject Descriptors C.2.4 [Computer-Communications Networks]: Distributed Systems; J.7 [Computers in Other Systems]: Military General Terms: Design, Measurement, Performance 1.\nINTRODUCTION The importance of countersniper systems is underscored by the constant stream of news reports coming from the Middle East.\nIn October 2006 CNN reported on a new tactic employed by insurgents.\nA mobile sniper team moves around busy city streets in a car, positions itself at a good standoff distance from dismounted US military personnel, takes a single well-aimed shot and immediately melts in the city traffic.\nBy the time the soldiers can react, they are gone.\nA countersniper system that provides almost immediate shooter location to every soldier in the vicinity would provide clear benefits to the warfigthers.\nOur team introduced PinPtr, the first sensor networkbased countersniper system [17, 8] in 2003.\nThe system is based on potentially hundreds of inexpensive sensor nodes deployed in the area of interest forming an ad-hoc multihop network.\nThe acoustic sensors measure the Time of Arrival (ToA) of muzzle blasts and ballistic shockwaves, pressure waves induced by the supersonic projectile, send the data to a base station where a sensor fusion algorithm determines the origin of the shot.\nPinPtr is characterized by high precision: 1m average 3D accuracy for shots originating within or near the sensor network and 1 degree bearing precision for both azimuth and elevation and 10% accuracy in range estimation for longer range shots.\nThe truly unique characteristic of the system is that it works in such reverberant environments as cluttered urban terrain and that it can resolve multiple simultaneous shots at the same time.\nThis capability is due to the widely distributed sensing and the unique sensor fusion approach [8].\nThe system has been tested several times in US Army MOUT (Military Operations in Urban Terrain) facilities.\nThe obvious disadvantage of such a system is its static nature.\nOnce the sensors are distributed, they cover a certain area.\nDepending on the operation, the deployment may be needed for an hour or a month, but eventually the area looses its importance.\nIt is not practical to gather and reuse the sensors, especially under combat conditions.\nEven if the sensors are cheap, it is still a waste and a logistical problem to provide a continuous stream of sensors as the operations move from place to place.\nAs it is primarily the soldiers that the system protects, a natural extension is to mount the sensors on the soldiers themselves.\nWhile there are vehiclemounted countersniper systems [1] available commercially, we are not aware of a deployed system that protects dismounted soldiers.\nA helmet-mounted system was developed in the mid 90s by BBN [3], but it was not continued beyond the Darpa program that funded it.\n113 To move from a static sensor network-based solution to a highly mobile one presents significant challenges.\nThe sensor positions and orientation need to be constantly monitored.\nAs soldiers may work in groups of as little as four people, the number of sensors measuring the acoustic phenomena may be an order of magnitude smaller than before.\nMoreover, the system should be useful to even a single soldier.\nFinally, additional requirements called for caliber estimation and weapon classification in addition to source localization.\nThe paper presents the design and evaluation of our soldierwearable mobile countersniper system.\nIt describes the hardware and software architecture including the custom sensor board equipped with a small microphone array and connected to a COTS MICAz mote [12].\nSpecial emphasis is paid to the sensor fusion technique that estimates the trajectory, range, caliber and weapon type simultaneously.\nThe results and analysis of an independent evaluation of the system at the US Army Aberdeen Test Center are also presented.\n2.\nAPPROACH The firing of a typical military rifle, such as the AK47 or M16, produces two distinct acoustic phenomena.\nThe muzzle blast is generated at the muzzle of the gun and travels at the speed sound.\nThe supersonic projectile generates an acoustic shockwave, a kind of sonic boom.\nThe wavefront has a conical shape, the angle of which depends on the Mach number, the speed of the bullet relative to the speed of sound.\nThe shockwave has a characteristic shape resembling a capital N.\nThe rise time at both the start and end of the signal is very fast, under 1 μsec.\nThe length is determined by the caliber and the miss distance, the distance between the trajectory and the sensor.\nIt is typically a few hundred μsec.\nOnce a trajectory estimate is available, the shockwave length can be used for caliber estimation.\nOur system is based on four microphones connected to a sensorboard.\nThe board detects shockwaves and muzzle blasts and measures their ToA.\nIf at least three acoustic channels detect the same event, its AoA is also computed.\nIf both the shockwave and muzzle blast AoA are available, a simple analytical solution gives the shooter location as shown in Section 6.\nAs the microphones are close to each other, typically 2-4, we cannot expect very high precision.\nAlso, this method does not estimate a trajectory.\nIn fact, an infinite number of trajectory-bullet speed pairs satisfy the observations.\nHowever, the sensorboards are also connected to COTS MICAz motes and they share their AoA and ToA measurements, as well as their own location and orientation, with each other using a multihop routing service [9].\nA hybrid sensor fusion algorithm then estimates the trajectory, the range, the caliber and the weapon type based on all available observations.\nThe sensorboard is also Bluetooth capable for communication with the soldier``s PDA or laptop computer.\nA wired USB connection is also available.\nThe sensorfusion algorithm and the user interface get their data through one of these channels.\nThe orientation of the microphone array at the time of detection is provided by a 3-axis digital compass.\nCurrently the system assumes that the soldier``s PDA is GPS-capable and it does not provide self localization service itself.\nHowever, the accuracy of GPS is a few meters degrading the Figure 1: Acoustic sensorboard/mote assembly .\noverall accuracy of the system.\nRefer to Section 7 for an analysis.\nThe latest generation sensorboard features a Texas Instruments CC-1000 radio enabling the high-precision radio interferometric self localization approach we have developed separately [7].\nHowever, we leave the integration of the two technologies for future work.\n3.\nHARDWARE Since the first static version of our system in 2003, the sensor nodes have been built upon the UC Berkeley/Crossbow MICA product line [11].\nAlthough rudimentary acoustic signal processing can be done on these microcontroller-based boards, they do not provide the required computational performance for shockwave detection and angle of arrival measurements, where multiple signals from different microphones need to be processed in parallel at a high sampling rate.\nOur 3rd generation sensorboard is designed to be used with MICAz motes-in fact it has almost the same size as the mote itself (see Figure 1).\nThe board utilizes a powerful Xilinx XC3S1000 FPGA chip with various standard peripheral IP cores, multiple soft processor cores and custom logic for the acoustic detectors (Figure 2).\nThe onboard Flash (4MB) and PSRAM (8MB) modules allow storing raw samples of several acoustic events, which can be used to build libraries of various acoustic signatures and for refining the detection cores off-line.\nAlso, the external memory blocks can store program code and data used by the soft processor cores on the FPGA.\nThe board supports four independent analog channels sampled at up to 1 MS/s (million samples per seconds).\nThese channels, featuring an electret microphone (Panasonic WM64PNT), amplifiers with controllable gain (30-60 dB) and a 12-bit serial ADC (Analog Devices AD7476), reside on separate tiny boards which are connected to the main sensorboard with ribbon cables.\nThis partitioning enables the use of truly different audio channels (eg.: slower sampling frequency, different gain or dynamic range) and also results in less noisy measurements by avoiding long analog signal paths.\nThe sensor platform offers a rich set of interfaces and can be integrated with existing systems in diverse ways.\nAn RS232 port and a Bluetooth (BlueGiga WT12) wireless link with virtual UART emulation are directly available on the board and provide simple means to connect the sensor to PCs and PDAs.\nThe mote interface consists of an I2 C bus along with an interrupt and GPIO line (the latter one is used 114 Figure 2: Block diagram of the sensorboard.\nfor precise time synchronization between the board and the mote).\nThe motes are equipped with IEEE 802.15.4 compliant radio transceivers and support ad-hoc wireless networking among the nodes and to/from the base station.\nThe sensorboard also supports full-speed USB transfers (with custom USB dongles) for uploading recorded audio samples to the PC.\nThe on-board JTAG chain-directly accessible through a dedicated connector-contains the FPGA part and configuration memory and provides in-system programming and debugging facilities.\nThe integrated Honeywell HMR3300 digital compass module provides heading, pitch and roll information with 1◦ accuracy, which is essential for calculating and combining directional estimates of the detected events.\nDue to the complex voltage requirements of the FPGA, the power supply circuitry is implemented on the sensorboard and provides power both locally and to the mote.\nWe used a quad pack of rechargeable AA batteries as the power source (although any other configuration is viable that meets the voltage requirements).\nThe FPGA core (1.2 V) and I/O (3.3 V) voltages are generated by a highly efficient buck switching regulator.\nThe FPGA configuration (2.5 V) and a separate 3.3 V power net are fed by low current LDOs, the latter one is used to provide independent power to the mote and to the Bluetooth radio.\nThe regulators-except the last one-can be turned on/off from the mote or through the Bluetooth radio (via GPIO lines) to save power.\nThe first prototype of our system employed 10 sensor nodes.\nSome of these nodes were mounted on military kevlar helmets with the microphones directly attached to the surface at about 20 cm separation as shown in Figure 3(a).\nThe rest of the nodes were mounted in plastic enclosures (Figure 3(b)) with the microphones placed near the corners of the boxes to form approximately 5 cm×10 cm rectangles.\n4.\nSOFTWARE ARCHITECTURE The sensor application relies on three subsystems exploiting three different computing paradigms as they are shown in Figure 4.\nAlthough each of these execution models suit their domain specific tasks extremely well, this diversity (a) (b) Figure 3: Sensor prototypes mounted on a kevlar helmet (a) and in a plastic box on a tripod (b).\npresents a challenge for software development and system integration.\nThe sensor fusion and user interface subsystem is running on PDAs and were implemented in Java.\nThe sensing and signal processing tasks are executed by an FPGA, which also acts as a bridge between various wired and wireless communication channels.\nThe ad-hoc internode communication, time synchronization and data sharing are the responsibilities of a microcontroller based radio module.\nSimilarly, the application employs a wide variety of communication protocols such as Bluetooth and IEEE 802.14.5 wireless links, as well as optional UARTs, I2 C and/or USB buses.\nSoldier Operated Device (PDA/Laptop) FPGA Sensor Board Mica Radio Module 2.4 GHz Wireless Link Radio Control Message Routing Acoustic Event Encoder Sensor Time Synch.\nNetwork Time Synch.Remote Control Time stamping Interrupts Virtual Register Interface C O O R D I N A T O R A n a l o g c h a n n e l s Compass PicoBlaze Comm.\nInterface PicoBlaze WT12 Bluetooth Radio MOTE IF:I2C,Interrupts USB PSRAM U A R T U A R T MB det SW det REC Bluetooth Link User Interface Sensor Fusion Location Engine GPS Message (Dis-)AssemblerSensor Control Figure 4: Software architecture diagram.\nThe sensor fusion module receives and unpacks raw measurements (time stamps and feature vectors) from the sensorboard through the Bluetooth link.\nAlso, it fine tunes the execution of the signal processing cores by setting parameters through the same link.\nNote that measurements from other nodes along with their location and orientation information also arrive from the sensorboard which acts as a gateway between the PDA and the sensor network.\nThe handheld device obtains its own GPS location data and di115 rectly receives orientation information through the sensorboard.\nThe results of the sensor fusion are displayed on the PDA screen with low latency.\nSince, the application is implemented in pure Java, it is portable across different PDA platforms.\nThe border between software and hardware is considerably blurred on the sensor board.\nThe IP cores-implemented in hardware description languages (HDL) on the reconfigurable FPGA fabric-closely resemble hardware building blocks.\nHowever, some of them-most notably the soft processor cores-execute true software programs.\nThe primary tasks of the sensor board software are 1) acquiring data samples from the analog channels, 2) processing acoustic data (detection), and 3) providing access to the results and run-time parameters through different interfaces.\nAs it is shown in Figure 4, a centralized virtual register file contains the address decoding logic, the registers for storing parameter values and results and the point to point data buses to and from the peripherals.\nThus, it effectively integrates the building blocks within the sensorboard and decouples the various communication interfaces.\nThis architecture enabled us to deploy the same set of sensors in a centralized scenario, where the ad-hoc mote network (using the I2 C interface) collected and forwarded the results to a base station or to build a decentralized system where the local PDAs execute the sensor fusion on the data obtained through the Bluetooth interface (and optionally from other sensors through the mote interface).\nThe same set of registers are also accessible through a UART link with a terminal emulation program.\nAlso, because the low-level interfaces are hidden by the register file, one can easily add/replace these with new ones (eg.: the first generation of motes supported a standard μP interface bus on the sensor connector, which was dropped in later designs).\nThe most important results are the time stamps of the detected events.\nThese time stamps and all other timing information (parameters, acoustic event features) are based on a 1 MHz clock and an internal timer on the FPGA.\nThe time conversion and synchronization between the sensor network and the board is done by the mote by periodically requesting the capture of the current timer value through a dedicated GPIO line and reading the captured value from the register file through the I2 C interface.\nBased on the the current and previous readings and the corresponding mote local time stamps, the mote can calculate and maintain the scaling factor and offset between the two time domains.\nThe mote interface is implemented by the I2 C slave IP core and a thin adaptation layer which provides a data and address bus abstraction on top of it.\nThe maximum effective bandwidth is 100 Kbps through this interface.\nThe FPGA contains several UART cores as well: for communicating with the on-board Bluetooth module, for controlling the digital compass and for providing a wired RS232 link through a dedicated connector.\nThe control, status and data registers of the UART modules are available through the register file.\nThe higher level protocols on these lines are implemented by Xilinx PicoBlaze microcontroller cores [13] and corresponding software programs.\nOne of them provides a command line interface for test and debug purposes, while the other is responsible for parsing compass readings.\nBy default, they are connected to the RS232 port and to the on-board digital compass line respectively, however, they can be rewired to any communication interface by changing the register file base address in the programs (e.g. the command line interface can be provided through the Bluetooth channel).\nTwo of the external interfaces are not accessible through the register file: a high speed USB link and the SRAM interface are tied to the recorder block.\nThe USB module implements a simple FIFO with parallel data lines connected to an external FT245R USB device controller.\nThe RAM driver implements data read/write cycles with correct timing and is connected to the on-board pseudo SRAM.\nThese interfaces provide 1 MB/s effective bandwidth for downloading recorded audio samples, for example.\nThe data acquisition and signal processing paths exhibit clear symmetry: the same set of IP cores are instantiated four times (i.e. the number of acoustic channels) and run independently.\nThe signal paths meet only just before the register file.\nEach of the analog channels is driven by a serial A/D core for providing a 20 MHz serial clock and shifting in 8-bit data samples at 1 MS/s and a digital potentiometer driver for setting the required gain.\nEach channel has its own shockwave and muzzle blast detector, which are described in Section 5.\nThe detectors fetch run-time parameter values from the register file and store their results there as well.\nThe coordinator core constantly monitors the detection results and generates a mote interrupt promptly upon full detection or after a reasonable timeout after partial detection.\nThe recorder component is not used in the final deployment, however, it is essential for development purposes for refining parameter values for new types of weapons or for other acoustic sources.\nThis component receives the samples from all channels and stores them in circular buffers in the PSRAM device.\nIf the signal amplitude on one of the channels crosses a predefined threshold, the recorder component suspends the sample collection with a predefined delay and dumps the contents of the buffers through the USB link.\nThe length of these buffers and delays, the sampling rate, the threshold level and the set of recorded channels can be (re)configured run-time through the register file.\nNote that the core operates independently from the other signal processing modules, therefore, it can be used to validate the detection results off-line.\nThe FPGA cores are implemented in VHDL, the PicoBlaze programs are written in assembly.\nThe complete configuration occupies 40% of the resources (slices) of the FPGA and the maximum clock speed is 30 MHz, which is safely higher than the speed used with the actual device (20MHz).\nThe MICAz motes are responsible for distributing measurement data across the network, which drastically improves the localization and classification results at each node.\nBesides a robust radio (MAC) layer, the motes require two essential middleware services to achieve this goal.\nThe messages need to be propagated in the ad-hoc multihop network using a routing service.\nWe successfully integrated the Directed Flood-Routing Framework (DFRF) [9] in our application.\nApart from automatic message aggregation and efficient buffer management, the most unique feature of DFRF is its plug-in architecture, which accepts custom routing policies.\nRouting policies are state machines that govern how received messages are stored, resent or discarded.\nExample policies include spanning tree routing, broadcast, geographic routing, etc..\nDifferent policies can be used for different messages concurrently, and the application is able to 116 change the underlying policies at run-time (eg.: because of the changing RF environment or power budget).\nIn fact, we switched several times between a simple but lavish broadcast policy and a more efficient gradient routing on the field.\nCorrelating ToA measurements requires a common time base and precise time synchronization in the sensor network.\nThe Routing Integrated Time Synchronization (RITS) [15] protocol relies on very accurate MAC-layer time-stamping to embed the cumulative delay that a data message accrued since the time of the detection in the message itself.\nThat is, at every node it measures the time the message spent there and adds this to the number in the time delay slot of the message, right before it leaves the current node.\nEvery receiving node can subtract the delay from its current time to obtain the detection time in its local time reference.\nThe service provides very accurate time conversion (few μs per hop error), which is more than adequate for this application.\nNote, that the motes also need to convert the sensorboard time stamps to mote time as it is described earlier.\nThe mote application is implemented in nesC [5] and is running on top of TinyOS [6].\nWith its 3 KB RAM and 28 KB program space (ROM) requirement, it easily fits on the MICAz motes.\n5.\nDETECTION ALGORITHM There are several characteristics of acoustic shockwaves and muzzle blasts which distinguish their detection and signal processing algorithms from regular audio applications.\nBoth events are transient by their nature and present very intense stimuli to the microphones.\nThis is increasingly problematic with low cost electret microphones-designed for picking up regular speech or music.\nAlthough mechanical damping of the microphone membranes can mitigate the problem, this approach is not without side effects.\nThe detection algorithms have to be robust enough to handle severe nonlinear distortion and transitory oscillations.\nSince the muzzle blast signature closely follows the shockwave signal and because of potential automatic weapon bursts, it is extremely important to settle the audio channels and the detection logic as soon as possible after an event.\nAlso, precise angle of arrival estimation necessitates high sampling frequency (in the MHz range) and accurate event detection.\nMoreover, the detection logic needs to process multiple channels in parallel (4 channels on our existing hardware).\nThese requirements dictated simple and robust algorithms both for muzzle blast and shockwave detections.\nInstead of using mundane energy detectors-which might not be able to distinguish the two different events-the applied detectors strive to find the most important characteristics of the two signals in the time-domain using simple state machine logic.\nThe detectors are implemented as independent IP cores within the FPGA-one pair for each channel.\nThe cores are run-time configurable and provide detection event signals with high precision time stamps and event specific feature vectors.\nAlthough the cores are running independently and in parallel, a crude local fusion module integrates them by shutting down those cores which missed their events after a reasonable timeout and by generating a single detection message towards the mote.\nAt this point, the mote can read and forward the detection times and features and is responsible to restart the cores afterwards.\nThe most conspicuous characteristics of an acoustic shockwave (see Figure 5(a)) are the steep rising edges at the be0 200 400 600 800 1000 1200 1400 1600 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Shockwave (M16) Time (µs) Amplitude 1 3 5 2 4 len (a) s[t] - s[t-D] > E tstart := t s[t] - s[t-D] < E s[t] - s[t-D] > E & t - t_start > Lmin s[t] - s[t-D] < E len := t - tstart IDLE 1 FIRST EDGE DONE 3 SECOND EDGE 4 FIRST EDGE 2 FOUND 5 t - tstart ≥ Lmax t - tstart ≥ Lmax (b) Figure 5: Shockwave signal generated by a 5.56 × 45 mm NATO projectile (a) and the state machine of the detection algorithm (b).\nginning and end of the signal.\nAlso, the length of the N-wave is fairly predictable-as it is described in Section 6.5-and is relatively short (200-300 μs).\nThe shockwave detection core is continuously looking for two rising edges within a given interval.\nThe state machine of the algorithm is shown in Figure 5(b).\nThe input parameters are the minimum steepness of the edges (D, E), and the bounds on the length of the wave (Lmin, Lmax).\nThe only feature calculated by the core is the length of the observed shockwave signal.\nIn contrast to shockwaves, the muzzle blast signatures are characterized by a long initial period (1-5 ms) where the first half period is significantly shorter than the second half [4].\nDue to the physical limitations of the analog circuitry described at the beginning of this section, irregular oscillations and glitches might show up within this longer time window as they can be clearly seen in Figure 6(a).\nTherefore, the real challenge for the matching detection core is to identify the first and second half periods properly.\nThe state machine (Figure 6(b)) does not work on the raw samples directly but is fed by a zero crossing (ZC) encoder.\nAfter the initial triggering, the detector attempts to collect those ZC segments which belong to the first period (positive amplitude) while discarding too short (in our terminology: garbage) segments-effectively implementing a rudimentary low-pass filter in the ZC domain.\nAfter it encounters a sufficiently long negative segment, it runs the same collection logic for the second half period.\nIf too much garbage is discarded in the collection phases, the core resets itself to prevent the (false) detection of the halves from completely different periods separated by rapid oscillation or noise.\nFinally, if the constraints on the total length and on the length ratio hold, the core generates a detection event along with the actual length, amplitude and energy of the period calculated concurrently.\nThe initial triggering mechanism is based on two amplitude thresholds: one static (but configurable) amplitude level and a dynamically computed one.\nThe latter one is essential to adapt the sensor to different ambient noise environments and to temporarily suspend the muzzle blast detector after a shock wave event (oscillations in the analog section or reverberations in the sensor enclosure might otherwise trigger false muzzle blast detections).\nThe dynamic noise level is estimated by a single pole recursive low-pass filter (cutoff @ 0.5 kHz ) on the FPGA.\n117 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Time (µs) Amplitude Muzzle blast (M16) 1 2 3 4 5 len2 + len1 (a) IDLE 1 SECOND ZC 3 PENDING ZC 4 FIRST ZC 2 FOUND 5 amplitude threshold long positive ZC long negative ZC valid full period max garbage wrong sign garbage collect first period garbage collect first period garbage (b) Figure 6: Muzzle blast signature (a) produced by an M16 assault rifle and the corresponding detection logic (b).\nThe detection cores were originally implemented in Java and evaluated on pre-recorded signals because of much faster test runs and more convenient debugging facilities.\nLater on, they were ported to VHDL and synthesized using the Xilinx ISE tool suite.\nThe functional equivalence between the two implementations were tested by VHDL test benches and Python scripts which provided an automated way to exercise the detection cores on the same set of pre-recorded signals and to compare the results.\n6.\nSENSOR FUSION The sensor fusion algorithm receives detection messages from the sensor network and estimates the bullet trajectory, the shooter position, the caliber of the projectile and the type of the weapon.\nThe algorithm consists of well separated computational tasks outlined below: 1.\nCompute muzzle blast and shockwave directions of arrivals for each individual sensor (see 6.1).\n2.\nCompute range estimates.\nThis algorithm can analytically fuse a pair of shockwave and muzzle blast AoA estimates.\n(see 6.2).\n3.\nCompute a single trajectory from all shockwave measurements (see 6.3).\n4.\nIf trajectory available compute range (see 6.4).\nelse compute shooter position first and then trajectory based on it.\n(see 6.4) 5.\nIf trajectory available compute caliber (see 6.5).\n6.\nIf caliber available compute weapon type (see 6.6).\nWe describe each step in the following sections in detail.\n6.1 Direction of arrival The first step of the sensor fusion is to calculate the muzzle blast and shockwave AoA-s for each sensorboard.\nEach sensorboard has four microphones that measure the ToA-s.\nSince the microphone spacing is orders of magnitude smaller than the distance to the sound source, we can approximate the approaching sound wave front with a plane (far field assumption).\nLet us formalize the problem for 3 microphones first.\nLet P1, P2 and P3 be the position of the microphones ordered by time of arrival t1 < t2 < t3.\nFirst we apply a simple geometry validation step.\nThe measured time difference between two microphones cannot be larger than the sound propagation time between the two microphones: |ti − tj| <= |Pi − Pj |/c + ε Where c is the speed of sound and ε is the maximum measurement error.\nIf this condition does not hold, the corresponding detections are discarded.\nLet v(x, y, z) be the normal vector of the unknown direction of arrival.\nWe also use r1(x1, y1, z1), the vector from P1 to P2 and r2(x2, y2, z2), the vector from P1 to P3.\nLet``s consider the projection of the direction of the motion of the wave front (v) to r1 divided by the speed of sound (c).\nThis gives us how long it takes the wave front to propagate form P1 to P2: vr1 = c(t2 − t1) The same relationship holds for r2 and v: vr2 = c(t3 − t1) We also know that v is a normal vector: vv = 1 Moving from vectors to coordinates using the dot product definition leads to a quadratic system: xx1 + yy1 + zz1 = c(t2 − t1) xx2 + yy2 + zz2 = c(t3 − t1) x2 + y2 + z2 = 1 We omit the solution steps here, as they are straightforward, but long.\nThere are two solutions (if the source is on the P1P2P3 plane the two solutions coincide).\nWe use the fourth microphone``s measurement-if there is one-to eliminate one of them.\nOtherwise, both solutions are considered for further processing.\n6.2 Muzzle-shock fusion u v 11,tP 22,tP tP, 2P′ Bullet trajectory Figure 7: Section plane of a shot (at P) and two sensors (at P1 and at P2).\nOne sensor detects the muzzle blast``s, the other the shockwave``s time and direction of arrivals.\nConsider the situation in Figure 7.\nA shot was fired from P at time t. Both P and t are unknown.\nWe have one muzzle blast and one shockwave detections by two different sensors 118 with AoA and hence, ToA information available.\nThe muzzle blast detection is at position P1 with time t1 and AoA u.\nThe shockwave detection is at P2 with time t2 and AoA v. u and v are normal vectors.\nIt is shown below that these measurements are sufficient to compute the position of the shooter (P).\nLet P2 be the point on the extended shockwave cone surface where PP2 is perpendicular to the surface.\nNote that PP2 is parallel with v.\nSince P2 is on the cone surface which hits P2, a sensor at P2 would detect the same shockwave time of arrival (t2).\nThe cone surface travels at the speed of sound (c), so we can express P using P2: P = P2 + cv(t2 − t).\nP can also be expressed from P1: P = P1 + cu(t1 − t) yielding P1 + cu(t1 − t) = P2 + cv(t2 − t).\nP2P2 is perpendicular to v: (P2 − P2)v = 0 yielding (P1 + cu(t1 − t) − cv(t2 − t) − P2)v = 0 containing only one unknown t.\nOne obtains: t = (P1−P2)v c +uvt1−t2 uv−1 .\nFrom here we can calculate the shoter position P. Let``s consider the special single sensor case where P1 = P2 (one sensor detects both shockwave and muzzle blast AoA).\nIn this case: t = uvt1−t2 uv−1 .\nSince u and v are not used separately only uv, the absolute orientation of the sensor can be arbitrary, we still get t which gives us the range.\nHere we assumed that the shockwave is a cone which is only true for constant projectile speeds.\nIn reality, the angle of the cone slowly grows; the surface resembles one half of an American football.\nThe decelerating bullet results in a smaller time difference between the shockwave and the muzzle blast detections because the shockwave generation slows down with the bullet.\nA smaller time difference results in a smaller range, so the above formula underestimates the true range.\nHowever, it can still be used with a proper deceleration correction function.\nWe leave this for future work.\n6.3 Trajectory estimation Danicki showed that the bullet trajectory and speed can be computed analytically from two independent shockwave measurements where both ToA and AoA are measured [2].\nThe method gets more sensitive to measurement errors as the two shockwave directions get closer to each other.\nIn the special case when both directions are the same, the trajectory cannot be computed.\nIn a real world application, the sensors are typically deployed on a plane approximately.\nIn this case, all sensors located on one side of the trajectory measure almost the same shockwave AoA.\nTo avoid this error sensitivity problem, we consider shockwave measurement pairs only if the direction of arrival difference is larger than a certain threshold.\nWe have multiple sensors and one sensor can report two different directions (when only three microphones detect the shockwave).\nHence, we typically have several trajectory candidates, i.e. one for each AoA pair over the threshold.\nWe applied an outlier filtering and averaging method to fuse together the shockwave direction and time information and come up with a single trajectory.\nAssume that we have N individual shockwave AoA measurements.\nLet``s take all possible unordered pairs where the direction difference is above the mentioned threshold and compute the trajectory for each.\nThis gives us at most N(N−1) 2 trajectories.\nA trajectory is represented by one point pi and the normal vector vi (where i is the trajectory index).\nWe define the distance of two trajectories as the dot product of their normal vectors: D(i, j) = vivj For each trajectory a neighbor set is defined: N(i) := {j|D(i, j) < R} where R is a radius parameter.\nThe largest neighbor set is considered to be the core set C, all other trajectories are outliers.\nThe core set can be found in O(N2 ) time.\nThe trajectories in the core set are then averaged to get the final trajectory.\nIt can happen that we cannot form any sensor pairs because of the direction difference threshold.\nIt means all sensors are on the same side of the trajectory.\nIn this case, we first compute the shooter position (described in the next section) that fixes p making v the only unknown.\nTo find v in this case, we use a simple high resolution grid search and minimize an error function based on the shockwave directions.\nWe have made experiments to utilize the measured shockwave length in the trajectory estimation.\nThere are some promising results, but it needs further research.\n6.4 Shooter position estimation The shooter position estimation algorithm aggregates the following heterogenous information generated by earlier computational steps: 1.\ntrajectory, 2.\nmuzzle blast ToA at a sensor, 3.\nmuzzle blast AoA at a sensor, which is effectively a bearing estimate to the shooter, and 4.\nrange estimate at a sensor (when both shockwave and muzzle blast AoA are available).\nSome sensors report only ToA, some has bearing estimate(s) also and some has range estimate(s) as well, depending on the number of successful muzzle blast and shockwave detections by the sensor.\nFor an example, refer to Figure 8.\nNote that a sensor may have two different bearing and range estimates.\n3 detections gives two possible AoA-s for muzzle blast (i.e. bearing) and/or shockwave.\nFurthermore, the combination of two different muzzle blast and shockwave AoA-s may result in two different ranges.\n119 11111 ,,,, rrvvt ′′ 22 ,vt 333 ,, vvt ′ 4t 5t 6t bullet trajectory shooter position Figure 8: Example of heterogenous input data for the shooter position estimation algorithm.\nAll sensors have ToA measurements (t1, t2, t3, t4, t5), one sensor has a single bearing estimate (v2), one sensor has two possible bearings (v3, v3) and one sensor has two bearing and two range estimates (v1, v1,r1, r1) In a multipath environment, these detections will not only contain gaussian noise, but also possibly large errors due to echoes.\nIt has been showed in our earlier work that a similar problem can be solved efficiently with an interval arithmetic based bisection search algorithm [8].\nThe basic idea is to define a discrete consistency function over the area of interest and subdivide the space into 3D boxes.\nFor any given 3D box, this function gives the number of measurements supporting the hypothesis that the shooter was within that box.\nThe search starts with a box large enough to contain the whole area of interest, then zooms in by dividing and evaluating boxes.\nThe box with the maximum consistency is divided until the desired precision is reached.\nBacktracking is possible to avoid getting stuck in a local maximum.\nThis approach has been shown to be fast enough for online processing.\nNote, however, that when the trajectory has already been calculated in previous steps, the search needs to be done only on the trajectory making it orders of magnitude faster.\nNext let us describe how the consistency function is calculated in detail.\nConsider B, a three dimensional box, we would like to compute the consistency value of.\nFirst we consider only the ToA information.\nIf one sensor has multiple ToA detections, we use the average of those times, so one sensor supplies at most one ToA estimate.\nFor each ToA, we can calculate the corresponding time of the shot, since the origin is assumed to be in box B.\nSince it is a box and not a single point, this gives us an interval for the shot time.\nThe maximum number of overlapping time intervals gives us the value of the consistency function for B. For a detailed description of the consistency function and search algorithm, refer to [8].\nHere we extend the approach the following way.\nWe modify the consistency function based on the bearing and range data from individual sensors.\nA bearing estimate supports B if the line segment starting from the sensor with the measured direction intersects the B box.\nA range supports B, if the sphere with the radius of the range and origin of the sensor intersects B. Instead of simply checking whether the position specified by the corresponding bearing-range pairs falls within B, this eliminates the sensor``s possible orientation error.\nThe value of the consistency function is incremented by one for each bearing and range estimate that is consistent with B. 6.5 Caliber estimation The shockwave signal characteristics has been studied before by Whitham [20].\nHe showed that the shockwave period T is related to the projectile diameter d, the length l, the perpendicular miss distance b from the bullet trajectory to the sensor, the Mach number M and the speed of sound c. T = 1.82Mb1/4 c(M2−1)3/8 d l1/4 ≈ 1.82d c (Mb l )1/4 0 100 200 300 400 500 600 0 10 20 30 miss distance (m)shockwavelength(microseconds) .50 cal 5.56 mm 7.62 mm Figure 9: Shockwave length and miss distance relationship.\nEach data point represents one sensorboard after an aggregation of the individual measurements of the four acoustic channels.\nThree different caliber projectiles have been tested (196 shots, 10 sensors).\nTo illustrate the relationship between miss distance and shockwave length, here we use all 196 shots with three different caliber projectiles fired during the evaluation.\n(During the evaluation we used data obtained previously using a few practice shots per weapon.)\n10 sensors (4 microphones by sensor) measured the shockwave length.\nFor each sensor, we considered the shockwave length estimation valid if at least three out of four microphones agreed on a value with at most 5 microsecond variance.\nThis filtering leads to a 86% report rate per sensor and gets rid of large measurement errors.\nThe experimental data is shown in Figure 9.\nWhitham``s formula suggests that the shockwave length for a given caliber can be approximated with a power function of the miss distance (with a 1/4 exponent).\nBest fit functions on our data are: .50 cal: T = 237.75b0.2059 7.62 mm: T = 178.11b0.1996 5.56 mm: T = 144.39b0.1757 To evaluate a shot, we take the caliber whose approximation function results in the smallest RMS error of the filtered sensor readings.\nThis method has less than 1% caliber estimation error when an accurate trajectory estimate is available.\nIn other words, caliber estimation only works if enough shockwave detections are made by the system to compute a trajectory.\n120 6.6 Weapon estimation We analyzed all measured signal characteristics to find weapon specific information.\nUnfortunately, we concluded that the observed muzzle blast signature is not characteristic enough of the weapon for classification purposes.\nThe reflections of the high energy muzzle blast from the environment have much higher impact on the muzzle blast signal shape than the weapon itself.\nShooting the same weapon from different places caused larger differences on the recorded signal than shooting different weapons from the same place.\n0 100 200 300 400 500 600 700 800 900 0 100 200 300 400 range (m) speed(m/s) AK-47 M240 Figure 10: AK47 and M240 bullet deceleration measurements.\nBoth weapons have the same caliber.\nData is approximated using simple linear regression.\n0 100 200 300 400 500 600 700 800 900 1000 0 50 100 150 200 250 300 350 range (m) speed(m/s) M16 M249 M4 Figure 11: M16, M249 and M4 bullet deceleration measurements.\nAll weapons have the same caliber.\nData is approximated using simple linear regression.\nHowever, the measured speed of the projectile and its caliber showed good correlation with the weapon type.\nThis is because for a given weapon type and ammunition pair, the muzzle velocity is nearly constant.\nIn Figures 10 and 11 we can see the relationship between the range and the measured bullet speed for different calibers and weapons.\nIn the supersonic speed range, the bullet deceleration can be approximated with a linear function.\nIn case of the 7.62 mm caliber, the two tested weapons (AK47, M240) can be clearly separated (Figure 10).\nUnfortunately, this is not necessarily true for the 5.56 mm caliber.\nThe M16 with its higher muzzle speed can still be well classified, but the M4 and M249 weapons seem practically undistinguishable (Figure 11).\nHowever, this may be partially due to the limited number of practice shots we were able to take before the actual testing began.\nMore training data may reveal better separation between the two weapons since their published muzzle velocities do differ somewhat.\nThe system carries out weapon classification in the following manner.\nOnce the trajectory is known, the speed can be calculated for each sensor based on the shockwave geometry.\nTo evaluate a shot, we choose the weapon type whose deceleration function results in the smallest RMS error of the estimated range-speed pairs for the estimated caliber class.\n7.\nRESULTS An independent evaluation of the system was carried out by a team from NIST at the US Army Aberdeen Test Center in April 2006 [19].\nThe experiment was setup on a shooting range with mock-up wooden buildings and walls for supporting elevated shooter positions and generating multipath effects.\nFigure 12 shows the user interface with an aerial photograph of the site.\n10 sensor nodes were deployed on surveyed points in an approximately 30×30 m area.\nThere were five fixed targets behind the sensor network.\nSeveral firing positions were located at each of the firing lines at 50, 100, 200 and 300 meters.\nThese positions were known to the evaluators, but not to the operators of the system.\nSix different weapons were utilized: AK47 and M240 firing 7.62 mm projectiles, M16, M4 and M249 with 5.56mm ammunition and the .50 caliber M107.\nNote that the sensors remained static during the test.\nThe primary reason for this is that nobody is allowed downrange during live fire tests.\nUtilizing some kind of remote control platform would have been too involved for the limited time the range was available for the test.\nThe experiment, therefore, did not test the mobility aspect of the system.\nDuring the one day test, there were 196 shots fired.\nThe results are summarized in Table 1.\nThe system detected all shots successfully.\nSince a ballistic shockwave is a unique acoustic phenomenon, it makes the detection very robust.\nThere were no false positives for shockwaves, but there were a handful of false muzzle blast detections due to parallel tests of artillery at a nearby range.\nShooter Local- Caliber Trajectory Trajectory Distance No.\nRange ization Accu- Azimuth Distance Error of (m) Rate racy Error (deg) Error (m) (m) Shots 50 93% 100% 0.86 0.91 2.2 54 100 100% 100% 0.66 1.34 8.7 54 200 96% 100% 0.74 2.71 32.8 54 300 97% 97% 1.49 6.29 70.6 34 All 96% 99.5% 0.88 2.47 23.0 196 Table 1: Summary of results fusing all available sensor observations.\nAll shots were successfully detected, so the detection rate is omitted.\nLocalization rate means the percentage of shots that the sensor fusion was able to estimate the trajectory of.\nThe caliber accuracy rate is relative to the shots localized and not all the shots because caliber estimation requires the trajectory.\nThe trajectory error is broken down to azimuth in degrees and the actual distance of the shooter from the trajectory.\nThe distance error shows the distance between the real shooter position and the estimated shooter position.\nAs such, it includes the error caused by both the trajectory and that of the range estimation.\nNote that the traditional bearing and range measures are not good ones for a distributed system such as ours because of the lack of a single reference point.\n121 Figure 12: The user interface of the system showing the experimental setup.\nThe 10 sensor nodes are labeled by their ID and marked by dark circles.\nThe targets are black squares marked T-1 through T-5.\nThe long white arrows point to the shooter position estimated by each sensor.\nWhere it is missing, the corresponding sensor did not have enough detections to measure the AoA of either the muzzle blast, the shockwave or both.\nThe thick black line and large circle indicate the estimated trajectory and the shooter position as estimated by fusing all available detections from the network.\nThis shot from the 100-meter line at target T-3 was localized almost perfectly by the sensor network.\nThe caliber and weapon were also identified correctly.\n6 out of 10 nodes were able to estimate the location alone.\nTheir bearing accuracy is within a degree, while the range is off by less than 10% in the worst case.\nThe localization rate characterizes the system``s ability to successfully estimate the trajectory of shots.\nSince caliber estimation and weapon classification relies on the trajectory, non-localized shots are not classified either.\nThere were 7 shots out of 196 that were not localized.\nThe reason for missed shots is the trajectory ambiguity problem that occurs when the projectile passes on one side of all the sensors.\nIn this case, two significantly different trajectories can generate the same set of observations (see [8] and also Section 6.3).\nInstead of estimating which one is more likely or displaying both possibilities, we decided not to provide a trajectory at all.\nIt is better not to give an answer other than a shot alarm than misleading the soldier.\nLocalization accuracy is broken down to trajectory accuracy and range estimation precision.\nThe angle of the estimated trajectory was better than 1 degree except for the 300 m range.\nSince the range should not affect trajectory estimation as long as the projectile passes over the network, we suspect that the slightly worse angle precision for 300 m is due to the hurried shots we witnessed the soldiers took near the end of the day.\nThis is also indicated by another datapoint: the estimated trajectory distance from the actual targets has an average error of 1.3 m for 300 m shots, 0.75 m for 200 m shots and 0.6 m for all but 300 m shots.\nAs the distance between the targets and the sensor network was fixed, this number should not show a 2× improvement just because the shooter is closer.\nSince the angle of the trajectory itself does not characterize the overall error-there can be a translation alsoTable 1 also gives the distance of the shooter from the estimated trajectory.\nThese indicate an error which is about 1-2% of the range.\nTo put this into perspective, a trajectory estimate for a 100 m shot will very likely go through or very near the window the shooter is located at.\nAgain, we believe that the disproportionally larger errors at 300 m are due to human errors in aiming.\nAs the ground truth was obtained by knowing the precise location of the shooter and the target, any inaccuracy in the actual trajectory directly adds to the perceived error of the system.\nWe call the estimation of the shooter``s position on the calculated trajectory range estimation due to the lack of a better term.\nThe range estimates are better than 5% accurate from 50 m and 10% for 100 m. However, this goes to 20% or worse for longer distances.\nWe did not have a facility to test system before the evaluation for ranges beyond 100 m.\nDuring the evaluation, we ran into the problem of mistaking shockwave echoes for muzzle blasts.\nThese echoes reached the sensors before the real muzzle blast for long range shots only, since the projectile travels 2-3× faster than the speed of sound, so the time between the shockwave (and its possible echo from nearby objects) and the muzzle blast increases with increasing ranges.\nThis resulted in underestimating the range, since the system measured shorter times than the real ones.\nSince the evaluation we finetuned the muzzle blast detection algorithm to avoid this problem.\nDistance M16 AK47 M240 M107 M4 M249 M4-M249 50m 100% 100% 100% 100% 11% 25% 94% 100m 100% 100% 100% 100% 22% 33% 100% 200m 100% 100% 100% 100% 50% 22% 100% 300m 67% 100% 83% 100% 33% 0% 57% All 96% 100% 97% 100% 23% 23% 93% Table 2: Weapon classification results.\nThe percentages are relative to the number of shots localized and not all shots, as the classification algorithm needs to know the trajectory and the range.\nNote that the difference is small; there were 189 shots localized out of the total 196.\nThe caliber and weapon estimation accuracy rates are based on the 189 shots that were successfully localized.\nNote that there was a single shot that was falsely classified by the caliber estimator.\nThe 73% overall weapon classification accuracy does not seem impressive.\nBut if we break it down to the six different weapons tested, the picture changes dramatically as shown in Table 2.\nFor four of the weapons (AK14, M16, M240 and M107), the classification rate is almost 100%.\nThere were only two shots out of approximately 140 that were missed.\nThe M4 and M249 proved to be too similar and they were mistaken for each other most of the time.\nOne possible explanation is that we had only a limited number of test shots taken with these weapons right before the evaluation and used the wrong deceleration approximation function.\nEither this or a similar mistake was made 122 since if we simply used the opposite of the system``s answer where one of these weapons were indicated, the accuracy would have improved 3x.\nIf we consider these two weapons a single weapon class, then the classification accuracy for it becomes 93%.\nNote that the AK47 and M240 have the same caliber (7.62 mm), just as the M16, M4 and M249 do (5.56 mm).\nThat is, the system is able to differentiate between weapons of the same caliber.\nWe are not aware of any system that classifies weapons this accurately.\n7.1 Single sensor performance As was shown previously, a single sensor alone is able to localize the shooter if it can determine both the muzzle blast and the shockwave AoA, that is, it needs to measure the ToA of both on at least three acoustic channels.\nWhile shockwave detection is independent of the range-unless the projectile becomes subsonic-, the likelihood of muzzle blast detection beyond 150 meters is not enough for consistently getting at least three per sensor node for AoA estimation.\nHence, we only evaluate the single sensor performance for the 104 shots that were taken from 50 and 100 m. Note that we use the same test data as in the previous section, but we evaluate individually for each sensor.\nTable 3 summarizes the results broken down by the ten sensors utilized.\nSince this is now not a distributed system, the results are given relative to the position of the given sensor, that is, a bearing and range estimate is provided.\nNote that many of the common error sources of the networked system do not play a role here.\nTime synchronization is not applicable.\nThe sensor``s absolute location is irrelevant (just as the relative location of multiple sensors).\nThe sensor``s orientation is still important though.\nThere are several disadvantages of the single sensor case compared to the networked system: there is no redundancy to compensate for other errors and to perform outlier rejection, the localization rate is markedly lower, and a single sensor alone is not able to estimate the caliber or classify the weapon.\nSensor id 1 2 3 5 7 8 9 10 11 12 Loc.\nrate 44% 37% 53% 52% 19% 63% 51% 31% 23% 44% Bearing (deg) 0.80 1.25 0.60 0.85 1.02 0.92 0.73 0.71 1.28 1.44 Range (m) 3.2 6.1 4.4 4.7 4.6 4.6 4.1 5.2 4.8 8.2 Table 3: Single sensor accuracy for 108 shots fired from 50 and 100 meters.\nLocalization rate refers to the percentage of shots the given sensor alone was able to localize.\nThe bearing and range values are average errors.\nThey characterize the accuracy of localization from the given sensor``s perspective.\nThe data indicates that the performance of the sensors varied significantly especially considering the localization rate.\nOne factor has to be the location of the given sensor including how far it was from the firing lines and how obstructed its view was.\nAlso, the sensors were hand-built prototypes utilizing nowhere near production quality packaging/mounting.\nIn light of these factors, the overall average bearing error of 0.9 degrees and range error of 5 m with a microphone spacing of less than 10 cm are excellent.\nWe believe that professional manufacturing and better microphones could easily achieve better performance than the best sensor in our experiment (>60% localization rate and 3 m range error).\nInterestingly, the largest error in range was a huge 90 m clearly due to some erroneous detection, yet the largest bearing error was less than 12 degrees which is still a good indication for the soldier where to look.\nThe overall localization rate over all single sensors was 42%, while for 50 m shots only, this jumped to 61%.\nNote that the firing range was prepared to simulate an urban area to some extent: there were a few single- and two-storey wooden structures built both in and around the sensor deployment area and the firing lines.\nHence, not all sensors had line-of-sight to all shooting positions.\nWe estimate that 10% of the sensors had obstructed view to the shooter on average.\nHence, we can claim that a given sensor had about 50% chance of localizing a shot within 130 m. (Since the sensor deployment area was 30 m deep, 100 m shots correspond to actual distances between 100 and 130 m.) Again, we emphasize that localization needs at least three muzzle blast and three shockwave detections out of a possible four for each per sensor.\nThe detection rate for single sensors-corresponding to at least one shockwave detection per sensor-was practically 100%.\n0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 1 2 3 4 5 6 7 8 9 10 number of sensors percentageofshots Figure 13: Histogram showing what fraction of the 104 shots taken from 50 and 100 meters were localized by at most how many individual sensors alone.\n13% of the shots were missed by every single sensor, i.e., none of them had both muzzle blast and shockwave AoA detections.\nNote that almost all of these shots were still accurately localized by the networked system, i.e. the sensor fusion using all available observations in the sensor network.\nIt would be misleading to interpret these results as the system missing half the shots.\nAs soldiers never work alone and the sensor node is relatively cheap to afford having every soldier equipped with one, we also need to look at the overall detection rates for every shot.\nFigure 13 shows the histogram of the percentage of shots vs. the number of individual sensors that localized it.\n13% of shots were not localized by any sensor alone, but 87% was localized by at least one sensor out of ten.\n7.2 Error sources In this section, we analyze the most significant sources of error that affect the performance of the networked shooter localization and weapon classification system.\nIn order to correlate the distributed observations of the acoustic events, the nodes need to have a common time and space reference.\nHence, errors in the time synchronization, node localization and node orientation all degrade the overall accuracy of the system.\n123 Our time synchronization approach yields errors significantly less than 100 microseconds.\nAs the sound travels about 3 cm in that time, time synchronization errors have a negligible effect on the system.\nOn the other hand, node location and orientation can have a direct effect on the overall system performance.\nNotice that to analyze this, we do not have to resort to simulation, instead we can utilize the real test data gathered at Aberdeen.\nBut instead of using the real sensor locations known very accurately and the measured and calibrated almost perfect node orientations, we can add error terms to them and run the sensor fusion.\nThis exactly replicates how the system would have performed during the test using the imprecisely known locations and orientations.\nAnother aspect of the system performance that can be evaluated this way is the effect of the number of available sensors.\nInstead of using all ten sensors in the data fusion, we can pick any subset of the nodes to see how the accuracy degrades as we decrease the number of nodes.\nThe following experiment was carried out.\nThe number of sensors were varied from 2 to 10 in increments of 2.\nEach run picked the sensors randomly using a uniform distribution.\nAt each run each node was randomly moved to a new location within a circle around its true position with a radius determined by a zero-mean Gaussian distribution.\nFinally, the node orientations were perturbed using a zeromean Gaussian distribution.\nEach combination of parameters were generated 100 times and utilized for all 196 shots.\nThe results are summarized in Figure 14.\nThere is one 3D barchart for each of the experiment sets with the given fixed number of sensors.\nThe x-axis shows the node location error, that is, the standard deviation of the corresponding Gaussian distribution that was varied between 0 and 6 meters.\nThe y-axis shows the standard deviation of the node orientation error that was varied between 0 and 6 degrees.\nThe z-axis is the resulting trajectory azimuth error.\nNote that the elevation angles showed somewhat larger errors than the azimuth.\nSince all the sensors were in approximately a horizontal plane and only a few shooter positions were out of the same plane and only by 2 m or so, the test was not sufficient to evaluate this aspect of the system.\nThere are many interesting observation one can make by analyzing these charts.\nNode location errors in this range have a small effect on accuracy.\nNode orientation errors, on the other hand, noticeably degrade the performance.\nStill the largest errors in this experiment of 3.5 degrees for 6 sensors and 5 degrees for 2 sensors are still very good.\nNote that as the location and orientation errors increase and the number of sensors decrease, the most significantly affected performance metric is the localization rate.\nSee Table 4 for a summary.\nSuccessful localization goes down from almost 100% to 50% when we go from 10 sensors to 2 even without additional errors.\nThis is primarily caused by geometry: for a successful localization, the bullet needs to pass over the sensor network, that is, at least one sensor should be on the side of the trajectory other than the rest of the nodes.\n(This is a simplification for illustrative purposes.\nIf all the sensors and the trajectory are not coplanar, localization may be successful even if the projectile passes on one side of the network.\nSee Section 6.3.)\nAs the numbers of sensors decreased in the experiment by randomly selecting a subset, the probability of trajectories abiding by this rule decreased.\nThis also means that even if there are 0 2 4 6 0 2 4 6 0 1 2 3 4 5 6 azimutherror(degree) position error (m) orientation error (degree) 2 sensors 0 2 4 6 0 2 4 6 0 1 2 3 4 5 6 azimutherror(degree) position error (m) orientation error (degree) 4 sensors 0 2 4 6 0 2 4 6 0 1 2 3 4 5 6 azimutherror(degree) position error (m) orientation error (degree) 6 sensors 0 2 4 6 0 2 4 6 0 1 2 3 4 5 6 azimutherror(degree) position error (m) orientation error (degree) 8 sensors Figure 14: The effect of node localization and orientation errors on azimuth accuracy with 2, 4, 6 and 8 nodes.\nNote that the chart for 10 nodes is almost identical for the 8-node case, hence, it is omitted.\n124 many sensors (i.e. soldiers), but all of them are right next to each other, the localization rate will suffer.\nHowever, when the sensor fusion does provide a result, it is still accurate even with few available sensors and relatively large individual errors.\nA very few consistent observation lead to good accuracy as the inconsistent ones are discarded by the algorithm.\nThis is also supported by the observation that for the cases with the higher number of sensors (8 or 10), the localization rate is hardly affected by even large errors.\nErrors/Sensors 2 4 6 8 10 0 m, 0 deg 54% 87% 94% 95% 96% 2 m, 2 deg 53% 80% 91% 96% 96% 6 m, 0 deg 43% 79% 88% 94% 94% 0 m, 6 deg 44% 78% 90% 93% 94% 6 m, 6 deg 41% 73% 85% 89% 92% Table 4: Localization rate as a function of the number of sensors used, the sensor node location and orientation errors.\nOne of the most significant observations on Figure 14 and Table 4 is that there is hardly any difference in the data for 6, 8 and 10 sensors.\nThis means that there is little advantage of adding more nodes beyond 6 sensors as far as the accuracy is concerned.\nThe speed of sound depends on the ambient temperature.\nThe current prototype considers it constant that is typically set before a test.\nIt would be straightforward to employ a temperature sensor to update the value of the speed of sound periodically during operation.\nNote also that wind may adversely affect the accuracy of the system.\nThe sensor fusion, however, could incorporate wind speed into its calculations.\nIt would be more complicated than temperature compensation, but could be done.\nOther practical issues also need to be looked at before a real world deployment.\nSilencers reduce the muzzle blast energy and hence, the effective range the system can detect it at.\nHowever, silencers do not effect the shockwave and the system would still detect the trajectory and caliber accurately.\nThe range and weapon type could not be estimated without muzzle blast detections.\nSubsonic weapons do not produce a shockwave.\nHowever, this is not of great significance, since they have shorter range, lower accuracy and much less lethality.\nHence, their use is not widespread and they pose less danger in any case.\nAnother issue is the type of ammunition used.\nIrregular armies may use substandard, even hand manufactured bullets.\nThis effects the muzzle velocity of the weapon.\nFor weapon classification to work accurately, the system would need to be calibrated with the typical ammunition used by the given adversary.\n8.\nRELATED WORK Acoustic detection and recognition has been under research since the early fifties.\nThe area has a close relevance to the topic of supersonic flow mechanics [20].\nFansler analyzed the complex near-field pressure waves that occur within a foot of the muzzle blast.\nFansler``s work gives a good idea of the ideal muzzle blast pressure wave without contamination from echoes or propagation effects [4].\nExperiments with greater distances from the muzzle were conducted by Stoughton [18].\nThe measurements of the ballistic shockwaves using calibrated pressure transducers at known locations, measured bullet speeds, and miss distances of 355 meters for 5.56 mm and 7.62 mm projectiles were made.\nResults indicate that ground interaction becomes a problem for miss distances of 30 meters or larger.\nAnother area of research is the signal processing of gunfire acoustics.\nThe focus is on the robust detection and length estimation of small caliber acoustic shockwaves and muzzle blasts.\nPossible techniques for classifying signals as either shockwaves or muzzle blasts includes short-time Fourier Transform (STFT), the Smoothed Pseudo Wigner-Ville distribution (SPWVD), and a discrete wavelet transformation (DWT).\nJoint time-frequency (JTF) spectrograms are used to analyze the typical separation of the shockwave and muzzle blast transients in both time and frequency.\nMays concludes that the DWT is the best method for classifying signals as either shockwaves or muzzle blasts because it works well and is less expensive to compute than the SPWVD [10].\nThe edges of the shockwave are typically well defined and the shockwave length is directly related to the bullet characteristics.\nA paper by Sadler [14] compares two shockwave edge detection methods: a simple gradient-based detector, and a multi-scale wavelet detector.\nIt also demonstrates how the length of the shockwave, as determined by the edge detectors, can be used along with Whithams equations [20] to estimate the caliber of a projectile.\nNote that the available computational performance on the sensor nodes, the limited wireless bandwidth and real-time requirements render these approaches infeasible on our platform.\nA related topic is the research and development of experimental and prototype shooter location systems.\nResearchers at BBN have developed the Bullet Ears system [3] which has the capability to be installed in a fixed position or worn by soldiers.\nThe fixed system has tetrahedron shaped microphone arrays with 1.5 meter spacing.\nThe overall system consists of two to three of these arrays spaced 20 to 100 meters from each other.\nThe soldier-worn system has 12 microphones as well as a GPS antenna and orientation sensors mounted on a helmet.\nThere is a low speed RF connection from the helmet to the processing body.\nAn extensive test has been conducted to measure the performance of both type of systems.\nThe fixed systems performance was one order of magnitude better in the angle calculations while their range performance where matched.\nThe angle accuracy of the fixed system was dominantly less than one degree while it was around five degrees for the helmet mounted one.\nThe range accuracy was around 5 percent for both of the systems.\nThe problem with this and similar centralized systems is the need of the one or handful of microphone arrays to be in line-of-sight of the shooter.\nA sensor networked based solution has the advantage of widely distributed sensing for better coverage, multipath effect compensation and multiple simultaneous shot resolution [8].\nThis is especially important for operation in acoustically reverberant urban areas.\nNote that BBN``s current vehicle-mounted system called BOOMERANG, a modified version of Bullet Ears, is currently used in Iraq [1].\nThe company ShotSpotter specializes in law enforcement systems that report the location of gunfire to police within seconds.\nThe goal of the system is significantly different than that of military systems.\nShotspotter reports 25 m typical accuracy which is more than enough for police to 125 respond.\nThey are also manufacturing experimental soldier wearable and UAV mounted systems for military use [16], but no specifications or evaluation results are publicly available.\n9.\nCONCLUSIONS The main contribution of this work is twofold.\nFirst, the performance of the overall distributed networked system is excellent.\nMost noteworthy are the trajectory accuracy of one degree, the correct caliber estimation rate of well over 90% and the close to 100% weapon classification rate for 4 of the 6 weapons tested.\nThe system proved to be very robust when increasing the node location and orientation errors and decreasing the number of available sensors all the way down to a couple.\nThe key factor behind this is the sensor fusion algorithm``s ability to reject erroneous measurements.\nIt is also worth mentioning that the results presented here correspond to the first and only test of the system beyond 100 m and with six different weapons.\nWe believe that with the lessons learned in the test, a consecutive field experiment could have showed significantly improved results especially in range estimation beyond 100 m and weapon classification for the remaining two weapons that were mistaken for each other the majority of the times during the test.\nSecond, the performance of the system when used in standalone mode, that is, when single sensors alone provided localization, was also very good.\nWhile the overall localization rate of 42% per sensor for shots up to 130 m could be improved, the bearing accuracy of less than a degree and the average 5% range error are remarkable using the handmade prototypes of the low-cost nodes.\nNote that 87% of the shots were successfully localized by at least one of the ten sensors utilized in standalone mode.\nWe believe that the technology is mature enough that a next revision of the system could be a commercial one.\nHowever, important aspects of the system would still need to be worked on.\nWe have not addresses power management yet.\nA current node runs on 4 AA batteries for about 12 hours of continuous operation.\nA deployable version of the sensor node would need to be asleep during normal operation and only wake up when an interesting event occurs.\nAn analog trigger circuit could solve this problem, however, the system would miss the first shot.\nInstead, the acoustic channels would need to be sampled and stored in a circular buffer.\nThe rest of the board could be turned off.\nWhen a trigger wakes up the board, the acoustic data would be immediately available.\nExperiments with a previous generation sensor board indicated that this could provide a 10x increase in battery life.\nOther outstanding issues include weatherproof packaging and ruggedization, as well as integration with current military infrastructure.\n10.\nREFERENCES [1] BBN technologies website.\nhttp://www.bbn.com.\n[2] E. Danicki.\nAcoustic sniper localization.\nArchives of Acoustics, 30(2):233-245, 2005.\n[3] G. L. Duckworth et al..\nFixed and wearable acoustic counter-sniper systems for law enforcement.\nIn E. M. Carapezza and D. B. Law, editors, Proc.\nSPIE Vol.\n3577, p. 210-230, pages 210-230, Jan. 1999.\n[4] K. Fansler.\nDescription of muzzle blast by modified scaling models.\nShock and Vibration, 5(1):1-12, 1998.\n[5] D. Gay, P. Levis, R. von Behren, M. Welsh, E. Brewer, and D. Culler.\nThe nesC language: a holistic approach to networked embedded systems.\nProceedings of Programming Language Design and Implementation (PLDI), June 2003.\n[6] J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. Culler, and K. Pister.\nSystem architecture directions for networked sensors.\nin Proc.\nof ASPLOS 2000, Nov. 2000.\n[7] B. Kus´y, G. Balogh, P. V¨olgyesi, J. Sallai, A. N´adas, A. L´edeczi, M. Mar´oti, and L. Meertens.\nNode-density independent localization.\nInformation Processing in Sensor Networks (IPSN 06) SPOTS Track, Apr. 2006.\n[8] A. L´edeczi, A. N´adas, P. V¨olgyesi, G. Balogh, B. Kus´y, J. Sallai, G. Pap, S. D´ora, K. Moln´ar, M. Mar´oti, and G. Simon.\nCountersniper system for urban warfare.\nACM Transactions on Sensor Networks, 1(1):153-177, Nov. 2005.\n[9] M. Mar´oti.\nDirected flood-routing framework for wireless sensor networks.\nIn Proceedings of the 5th ACM/IFIP/USENIX International Conference on Middleware, pages 99-114, New York, NY, USA, 2004.\nSpringer-Verlag New York, Inc. [10] B. Mays.\nShockwave and muzzle blast classification via joint time frequency and wavelet analysis.\nTechnical report, Army Research Lab Adelphi MD 20783-1197, Sept. 2001.\n[11] TinyOS Hardware Platforms.\nhttp://tinyos.net/scoop/special/hardware.\n[12] Crossbow MICAz (MPR2400) Radio Module.\nhttp://www.xbow.com/Products/productsdetails.\naspx?sid=101.\n[13] PicoBlaze User Resources.\nhttp://www.xilinx.com/ipcenter/processor_ central/picoblaze/picoblaze_user_resources.htm.\n[14] B. M. Sadler, T. Pham, and L. C. Sadler.\nOptimal and wavelet-based shock wave detection and estimation.\nAcoustical Society of America Journal, 104:955-963, Aug. 1998.\n[15] J. Sallai, B. Kus´y, A. L´edeczi, and P. Dutta.\nOn the scalability of routing-integrated time synchronization.\n3rd European Workshop on Wireless Sensor Networks (EWSN 2006), Feb. 2006.\n[16] ShotSpotter website.\nhttp: //www.shotspotter.com/products/military.html.\n[17] G. Simon, M. Mar´oti, A. L´edeczi, G. Balogh, B. Kus´y, A. N´adas, G. Pap, J. Sallai, and K. Frampton.\nSensor network-based countersniper system.\nIn SenSys ``04: Proceedings of the 2nd international conference on Embedded networked sensor systems, pages 1-12, New York, NY, USA, 2004.\nACM Press.\n[18] R. Stoughton.\nMeasurements of small-caliber ballistic shock waves in air.\nAcoustical Society of America Journal, 102:781-787, Aug. 1997.\n[19] B. A. Weiss, C. Schlenoff, M. Shneier, and A. Virts.\nTechnology evaluations and performance metrics for soldier-worn sensors for assist.\nIn Performance Metrics for Intelligent Systems Workshop, Aug. 2006.\n[20] G. Whitham.\nFlow pattern of a supersonic projectile.\nCommunications on pure and applied mathematics, 5(3):301, 1952.\n126"},"lvl-2":{"kind":"string","value":"Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors\nABSTRACT\nThe paper presents a wireless sensor network-based mobile countersniper system.\nA sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implements the acoustic detection and Time of Arrival (ToA) estimation algorithms on an FPGA.\nA 3-axis compass provides self orientation and Bluetooth is used for communication with the soldier's PDA running the data fusion and the user interface.\nThe heterogeneous sensor fusion algorithm can work with data from a single sensor or it can fuse ToA or Angle of Arrival (AoA) observations of muzzle blasts and ballistic shockwaves from multiple sensors.\nThe system estimates the trajectory, the range, the caliber and the weapon type.\nThe paper presents the system design and the results from an independent evaluation at the US Army Aberdeen Test Center.\nThe system performance is characterized by 1degree trajectory precision and over 95% caliber estimation accuracy for all shots, and close to 100% weapon estimation accuracy for 4 out of 6 guns tested.\n1.\nINTRODUCTION\nThe importance of countersniper systems is underscored by the constant stream of news reports coming from the Middle East.\nIn October 2006 CNN reported on a new tactic employed by insurgents.\nA mobile sniper team moves around busy city streets in a car, positions itself at a good standoff distance from dismounted US military personnel, takes a single well-aimed shot and immediately melts in the city traffic.\nBy the time the soldiers can react, they are gone.\nA countersniper system that provides almost immediate shooter location to every soldier in the vicinity would provide clear benefits to the warfigthers.\nOur team introduced PinPtr, the first sensor networkbased countersniper system [17, 8] in 2003.\nThe system is based on potentially hundreds of inexpensive sensor nodes deployed in the area of interest forming an ad hoc multihop network.\nThe acoustic sensors measure the Time of Arrival (ToA) of muzzle blasts and ballistic shockwaves, pressure waves induced by the supersonic projectile, send the data to a base station where a sensor fusion algorithm determines the origin of the shot.\nPinPtr is characterized by high precision: 1m average 3D accuracy for shots originating within or near the sensor network and 1 degree bearing precision for both azimuth and elevation and 10% accuracy in range estimation for longer range shots.\nThe truly unique characteristic of the system is that it works in such reverberant environments as cluttered urban terrain and that it can resolve multiple simultaneous shots at the same time.\nThis capability is due to the widely distributed sensing and the unique sensor fusion approach [8].\nThe system has been tested several times in US Army MOUT (Military Operations in Urban Terrain) facilities.\nThe obvious disadvantage of such a system is its static nature.\nOnce the sensors are distributed, they cover a certain area.\nDepending on the operation, the deployment may be needed for an hour or a month, but eventually the area looses its importance.\nIt is not practical to gather and reuse the sensors, especially under combat conditions.\nEven if the sensors are cheap, it is still a waste and a logistical problem to provide a continuous stream of sensors as the operations move from place to place.\nAs it is primarily the soldiers that the system protects, a natural extension is to mount the sensors on the soldiers themselves.\nWhile there are vehiclemounted countersniper systems [1] available commercially, we are not aware of a deployed system that protects dismounted soldiers.\nA helmet-mounted system was developed in the mid 90s by BBN [3], but it was not continued beyond the Darpa program that funded it.\nTo move from a static sensor network-based solution to a highly mobile one presents significant challenges.\nThe sensor positions and orientation need to be constantly monitored.\nAs soldiers may work in groups of as little as four people, the number of sensors measuring the acoustic phenomena may be an order of magnitude smaller than before.\nMoreover, the system should be useful to even a single soldier.\nFinally, additional requirements called for caliber estimation and weapon classification in addition to source localization.\nThe paper presents the design and evaluation of our soldierwearable mobile countersniper system.\nIt describes the hardware and software architecture including the custom sensor board equipped with a small microphone array and connected to a COTS MICAz mote [12].\nSpecial emphasis is paid to the sensor fusion technique that estimates the trajectory, range, caliber and weapon type simultaneously.\nThe results and analysis of an independent evaluation of the system at the US Army Aberdeen Test Center are also presented.\n2.\nAPPROACH\nThe firing of a typical military rifle, such as the AK47 or M16, produces two distinct acoustic phenomena.\nThe muzzle blast is generated at the muzzle of the gun and travels at the speed sound.\nThe supersonic projectile generates an acoustic shockwave, a kind of sonic boom.\nThe wavefront has a conical shape, the angle of which depends on the Mach number, the speed of the bullet relative to the speed of sound.\nThe shockwave has a characteristic shape resembling a capital N.\nThe rise time at both the start and end of the signal is very fast, under 1 µsec.\nThe length is determined by the caliber and the miss distance, the distance between the trajectory and the sensor.\nIt is typically a few hundred µsec.\nOnce a trajectory estimate is available, the shockwave length can be used for caliber estimation.\nOur system is based on four microphones connected to a sensorboard.\nThe board detects shockwaves and muzzle blasts and measures their ToA.\nIf at least three acoustic channels detect the same event, its AoA is also computed.\nIf both the shockwave and muzzle blast AoA are available, a simple analytical solution gives the shooter location as shown in Section 6.\nAs the microphones are close to each other, typically 2-4\", we cannot expect very high precision.\nAlso, this method does not estimate a trajectory.\nIn fact, an infinite number of trajectory-bullet speed pairs satisfy the observations.\nHowever, the sensorboards are also connected to COTS MICAz motes and they share their AoA and ToA measurements, as well as their own location and orientation, with each other using a multihop routing service [9].\nA hybrid sensor fusion algorithm then estimates the trajectory, the range, the caliber and the weapon type based on all available observations.\nThe sensorboard is also Bluetooth capable for communication with the soldier's PDA or laptop computer.\nA wired USB connection is also available.\nThe sensorfusion algorithm and the user interface get their data through one of these channels.\nThe orientation of the microphone array at the time of detection is provided by a 3-axis digital compass.\nCurrently the system assumes that the soldier's PDA is GPS-capable and it does not provide self localization service itself.\nHowever, the accuracy of GPS is a few meters degrading the\nFigure 1: Acoustic sensorboard/mote assembly.\noverall accuracy of the system.\nRefer to Section 7 for an analysis.\nThe latest generation sensorboard features a Texas Instruments CC-1000 radio enabling the high-precision radio interferometric self localization approach we have developed separately [7].\nHowever, we leave the integration of the two technologies for future work.\n3.\nHARDWARE\nSince the first static version of our system in 2003, the sensor nodes have been built upon the UC Berkeley/Crossbow MICA product line [11].\nAlthough rudimentary acoustic signal processing can be done on these microcontroller-based boards, they do not provide the required computational performance for shockwave detection and angle of arrival measurements, where multiple signals from different microphones need to be processed in parallel at a high sampling rate.\nOur 3rd generation sensorboard is designed to be used with MICAz motes--in fact it has almost the same size as the mote itself (see Figure 1).\nThe board utilizes a powerful Xilinx XC3S1000 FPGA chip with various standard peripheral IP cores, multiple soft processor cores and custom logic for the acoustic detectors (Figure 2).\nThe onboard Flash (4MB) and PSRAM (8MB) modules allow storing raw samples of several acoustic events, which can be used to build libraries of various acoustic signatures and for refining the detection cores off-line.\nAlso, the external memory blocks can store program code and data used by the soft processor cores on the FPGA.\nThe board supports four independent analog channels sampled at up to 1 MS/s (million samples per seconds).\nThese channels, featuring an electret microphone (Panasonic WM64PNT), amplifiers with controllable gain (30-60 dB) and a 12-bit serial ADC (Analog Devices AD7476), reside on separate tiny boards which are connected to the main sensorboard with ribbon cables.\nThis partitioning enables the use of truly different audio channels (eg.: slower sampling frequency, different gain or dynamic range) and also results in less noisy measurements by avoiding long analog signal paths.\nThe sensor platform offers a rich set of interfaces and can be integrated with existing systems in diverse ways.\nAn RS232 port and a Bluetooth (BlueGiga WT12) wireless link with virtual UART emulation are directly available on the board and provide simple means to connect the sensor to PCs and PDAs.\nThe mote interface consists of an I2C bus along with an interrupt and GPIO line (the latter one is used\nFigure 2: Block diagram of the sensorboard.\nfor precise time synchronization between the board and the mote).\nThe motes are equipped with IEEE 802.15.4 compliant radio transceivers and support ad-hoc wireless networking among the nodes and to/from the base station.\nThe sensorboard also supports full-speed USB transfers (with custom USB dongles) for uploading recorded audio samples to the PC.\nThe on-board JTAG chain--directly accessible through a dedicated connector--contains the FPGA part and configuration memory and provides in-system programming and debugging facilities.\nThe integrated Honeywell HMR3300 digital compass module provides heading, pitch and roll information with 1 ◦ accuracy, which is essential for calculating and combining directional estimates of the detected events.\nDue to the complex voltage requirements of the FPGA, the power supply circuitry is implemented on the sensorboard and provides power both locally and to the mote.\nWe used a quad pack of rechargeable AA batteries as the power source (although any other configuration is viable that meets the voltage requirements).\nThe FPGA core (1.2 V) and I/O (3.3 V) voltages are generated by a highly efficient buck switching regulator.\nThe FPGA configuration (2.5 V) and a separate 3.3 V power net are fed by low current LDOs, the latter one is used to provide independent power to the mote and to the Bluetooth radio.\nThe regulators--except the last one--can be turned on/off from the mote or through the Bluetooth radio (via GPIO lines) to save power.\nThe first prototype of our system employed 10 sensor nodes.\nSome of these nodes were mounted on military kevlar helmets with the microphones directly attached to the surface at about 20 cm separation as shown in Figure 3 (a).\nThe rest of the nodes were mounted in plastic enclosures (Figure 3 (b)) with the microphones placed near the corners of the boxes to form approximately 5 cm × 10 cm rectangles.\n4.\nSOFTWARE ARCHITECTURE\nThe sensor application relies on three subsystems exploiting three different computing paradigms as they are shown in Figure 4.\nAlthough each of these execution models suit their domain specific tasks extremely well, this diversity\nFigure 3: Sensor prototypes mounted on a kevlar helmet (a) and in a plastic box on a tripod (b).\npresents a challenge for software development and system integration.\nThe sensor fusion and user interface subsystem is running on PDAs and were implemented in Java.\nThe sensing and signal processing tasks are executed by an FPGA, which also acts as a bridge between various wired and wireless communication channels.\nThe ad-hoc internode communication, time synchronization and data sharing are the responsibilities of a microcontroller based radio module.\nSimilarly, the application employs a wide variety of communication protocols such as Bluetooth ® and IEEE 802.14.5 wireless links, as well as optional UARTs, I2C and/or USB buses.\nFigure 4: Software architecture diagram.\nThe sensor fusion module receives and unpacks raw measurements (time stamps and feature vectors) from the sensorboard through the Bluetooth ® link.\nAlso, it fine tunes the execution of the signal processing cores by setting parameters through the same link.\nNote that measurements from other nodes along with their location and orientation information also arrive from the sensorboard which acts as a gateway between the PDA and the sensor network.\nThe handheld device obtains its own GPS location data and di\nrectly receives orientation information through the sensorboard.\nThe results of the sensor fusion are displayed on the PDA screen with low latency.\nSince, the application is implemented in pure Java, it is portable across different PDA platforms.\nThe border between software and hardware is considerably blurred on the sensor board.\nThe IP cores--implemented in hardware description languages (HDL) on the reconfigurable FPGA fabric--closely resemble hardware building blocks.\nHowever, some of them--most notably the soft processor cores--execute true software programs.\nThe primary tasks of the sensor board software are 1) acquiring data samples from the analog channels, 2) processing acoustic data (detection), and 3) providing access to the results and run-time parameters through different interfaces.\nAs it is shown in Figure 4, a centralized virtual register file contains the address decoding logic, the registers for storing parameter values and results and the point to point data buses to and from the peripherals.\nThus, it effectively integrates the building blocks within the sensorboard and decouples the various communication interfaces.\nThis architecture enabled us to deploy the same set of sensors in a centralized scenario, where the ad-hoc mote network (using the I2C interface) collected and forwarded the results to a base station or to build a decentralized system where the local PDAs execute the sensor fusion on the data obtained through the Bluetooth ® interface (and optionally from other sensors through the mote interface).\nThe same set of registers are also accessible through a UART link with a terminal emulation program.\nAlso, because the low-level interfaces are hidden by the register file, one can easily add/replace these with new ones (eg.: the first generation of motes supported a standard µP interface bus on the sensor connector, which was dropped in later designs).\nThe most important results are the time stamps of the detected events.\nThese time stamps and all other timing information (parameters, acoustic event features) are based on a 1 MHz clock and an internal timer on the FPGA.\nThe time conversion and synchronization between the sensor network and the board is done by the mote by periodically requesting the capture of the current timer value through a dedicated GPIO line and reading the captured value from the register file through the I2C interface.\nBased on the the current and previous readings and the corresponding mote local time stamps, the mote can calculate and maintain the scaling factor and offset between the two time domains.\nThe mote interface is implemented by the I2C slave IP core and a thin adaptation layer which provides a data and address bus abstraction on top of it.\nThe maximum effective bandwidth is 100 Kbps through this interface.\nThe FPGA contains several UART cores as well: for communicating with the on-board Bluetooth ® module, for controlling the digital compass and for providing a wired RS232 link through a dedicated connector.\nThe control, status and data registers of the UART modules are available through the register file.\nThe higher level protocols on these lines are implemented by Xilinx PicoBlaze microcontroller cores [13] and corresponding software programs.\nOne of them provides a command line interface for test and debug purposes, while the other is responsible for parsing compass readings.\nBy default, they are connected to the RS232 port and to the on-board digital compass line respectively, however, they can be rewired to any communication interface by changing the register file base address in the programs (e.g. the command line interface can be provided through the Bluetooth ® channel).\nTwo of the external interfaces are not accessible through the register file: a high speed USB link and the SRAM interface are tied to the recorder block.\nThe USB module implements a simple FIFO with parallel data lines connected to an external FT245R USB device controller.\nThe RAM driver implements data read/write cycles with correct timing and is connected to the on-board pseudo SRAM.\nThese interfaces provide 1 MB/s effective bandwidth for downloading recorded audio samples, for example.\nThe data acquisition and signal processing paths exhibit clear symmetry: the same set of IP cores are instantiated four times (i.e. the number of acoustic channels) and run independently.\nThe signal paths\" meet\" only just before the register file.\nEach of the analog channels is driven by a serial A/D core for providing a 20 MHz serial clock and shifting in 8-bit data samples at 1 MS/s and a digital potentiometer driver for setting the required gain.\nEach channel has its own shockwave and muzzle blast detector, which are described in Section 5.\nThe detectors fetch run-time parameter values from the register file and store their results there as well.\nThe coordinator core constantly monitors the detection results and generates a mote interrupt promptly upon\" full detection\" or after a reasonable timeout after\" partial detection\".\nThe recorder component is not used in the final deployment, however, it is essential for development purposes for refining parameter values for new types of weapons or for other acoustic sources.\nThis component receives the samples from all channels and stores them in circular buffers in the PSRAM device.\nIf the signal amplitude on one of the channels crosses a predefined threshold, the recorder component suspends the sample collection with a predefined delay and dumps the contents of the buffers through the USB link.\nThe length of these buffers and delays, the sampling rate, the threshold level and the set of recorded channels can be (re) configured run-time through the register file.\nNote that the core operates independently from the other signal processing modules, therefore, it can be used to validate the detection results off-line.\nThe FPGA cores are implemented in VHDL, the PicoBlaze programs are written in assembly.\nThe complete configuration occupies 40% of the resources (slices) of the FPGA and the maximum clock speed is 30 MHz, which is safely higher than the speed used with the actual device (20MHz).\nThe MICAz motes are responsible for distributing measurement data across the network, which drastically improves the localization and classification results at each node.\nBesides a robust radio (MAC) layer, the motes require two essential middleware services to achieve this goal.\nThe messages need to be propagated in the ad-hoc multihop network using a routing service.\nWe successfully integrated the Directed Flood-Routing Framework (DFRF) [9] in our application.\nApart from automatic message aggregation and efficient buffer management, the most unique feature of DFRF is its plug-in architecture, which accepts custom routing policies.\nRouting policies are state machines that govern how received messages are stored, resent or discarded.\nExample policies include spanning tree routing, broadcast, geographic routing, etc. .\nDifferent policies can be used for different messages concurrently, and the application is able to\nchange the underlying policies at run-time (eg.: because of the changing RF environment or power budget).\nIn fact, we switched several times between a simple but lavish broadcast policy and a more efficient gradient routing on the field.\nCorrelating ToA measurements requires a common time base and precise time synchronization in the sensor network.\nThe Routing Integrated Time Synchronization (RITS) [15] protocol relies on very accurate MAC-layer time-stamping to embed the cumulative delay that a data message accrued since the time of the detection in the message itself.\nThat is, at every node it measures the time the message spent there and adds this to the number in the time delay slot of the message, right before it leaves the current node.\nEvery receiving node can subtract the delay from its current time to obtain the detection time in its local time reference.\nThe service provides very accurate time conversion (few µs per hop error), which is more than adequate for this application.\nNote, that the motes also need to convert the sensorboard time stamps to mote time as it is described earlier.\nThe mote application is implemented in nesC [5] and is running on top of TinyOS [6].\nWith its 3 KB RAM and 28 KB program space (ROM) requirement, it easily fits on the MICAz motes.\n5.\nDETECTION ALGORITHM\nThere are several characteristics of acoustic shockwaves and muzzle blasts which distinguish their detection and signal processing algorithms from regular audio applications.\nBoth events are transient by their nature and present very intense stimuli to the microphones.\nThis is increasingly problematic with low cost electret microphones--designed for picking up regular speech or music.\nAlthough mechanical damping of the microphone membranes can mitigate the problem, this approach is not without side effects.\nThe detection algorithms have to be robust enough to handle severe nonlinear distortion and transitory oscillations.\nSince the muzzle blast signature closely follows the shockwave signal and because of potential automatic weapon bursts, it is extremely important to settle the audio channels and the detection logic as soon as possible after an event.\nAlso, precise angle of arrival estimation necessitates high sampling frequency (in the MHz range) and accurate event detection.\nMoreover, the detection logic needs to process multiple channels in parallel (4 channels on our existing hardware).\nThese requirements dictated simple and robust algorithms both for muzzle blast and shockwave detections.\nInstead of using mundane energy detectors--which might not be able to distinguish the two different events--the applied detectors strive to find the most important characteristics of the two signals in the time-domain using simple state machine logic.\nThe detectors are implemented as independent IP cores within the FPGA--one pair for each channel.\nThe cores are run-time configurable and provide detection event signals with high precision time stamps and event specific feature vectors.\nAlthough the cores are running independently and in parallel, a crude local fusion module integrates them by shutting down those cores which missed their events after a reasonable timeout and by generating a single detection message towards the mote.\nAt this point, the mote can read and forward the detection times and features and is responsible to restart the cores afterwards.\nThe most conspicuous characteristics of an acoustic shockwave (see Figure 5 (a)) are the steep rising edges at the be\nFigure 5: Shockwave signal generated by a 5.56 × 45 mm NATO projectile (a) and the state machine of the detection algorithm (b).\nginning and end of the signal.\nAlso, the length of the N-wave is fairly predictable--as it is described in Section 6.5--and is relatively short (200-300 µs).\nThe shockwave detection core is continuously looking for two rising edges within a given interval.\nThe state machine of the algorithm is shown in Figure 5 (b).\nThe input parameters are the minimum steepness of the edges (D, E), and the bounds on the length of the wave (Lmin, Lmax).\nThe only feature calculated by the core is the length of the observed shockwave signal.\nIn contrast to shockwaves, the muzzle blast signatures are characterized by a long initial period (1-5 ms) where the first half period is significantly shorter than the second half [4].\nDue to the physical limitations of the analog circuitry described at the beginning of this section, irregular oscillations and glitches might show up within this longer time window as they can be clearly seen in Figure 6 (a).\nTherefore, the real challenge for the matching detection core is to identify the first and second half periods properly.\nThe state machine (Figure 6 (b)) does not work on the raw samples directly but is fed by a zero crossing (ZC) encoder.\nAfter the initial triggering, the detector attempts to collect those ZC segments which belong to the first period (positive amplitude) while discarding too short (in our terminology: garbage) segments--effectively implementing a rudimentary low-pass filter in the ZC domain.\nAfter it encounters a sufficiently long negative segment, it runs the same collection logic for the second half period.\nIf too much garbage is discarded in the collection phases, the core resets itself to prevent the (false) detection of the halves from completely different periods separated by rapid oscillation or noise.\nFinally, if the constraints on the total length and on the length ratio hold, the core generates a detection event along with the actual length, amplitude and energy of the period calculated concurrently.\nThe initial triggering mechanism is based on two amplitude thresholds: one static (but configurable) amplitude level and a dynamically computed one.\nThe latter one is essential to adapt the sensor to different ambient noise environments and to temporarily suspend the muzzle blast detector after a shock wave event (oscillations in the analog section or reverberations in the sensor enclosure might otherwise trigger false muzzle blast detections).\nThe dynamic noise level is estimated by a single pole recursive low-pass filter (cutoff @ 0.5 kHz) on the FPGA.\nFigure 6: Muzzle blast signature (a) produced by an M16 assault rifle and the corresponding detection logic (b).\nThe detection cores were originally implemented in Java and evaluated on pre-recorded signals because of much faster test runs and more convenient debugging facilities.\nLater on, they were ported to VHDL and synthesized using the Xilinx ISE tool suite.\nThe functional equivalence between the two implementations were tested by VHDL test benches and Python scripts which provided an automated way to exercise the detection cores on the same set of pre-recorded signals and to compare the results.\n6.\nSENSOR FUSION\nThe sensor fusion algorithm receives detection messages from the sensor network and estimates the bullet trajectory, the shooter position, the caliber of the projectile and the type of the weapon.\nThe algorithm consists of well separated computational tasks outlined below:\n1.\nCompute muzzle blast and shockwave directions of arrivals for each individual sensor (see 6.1).\n2.\nCompute range estimates.\nThis algorithm can analytically fuse a pair of shockwave and muzzle blast AoA estimates.\n(see 6.2).\n3.\nCompute a single trajectory from all shockwave measurements (see 6.3).\n4.\nIf trajectory available compute range (see 6.4).\nelse compute shooter position first and then trajectory based on it.\n(see 6.4) 5.\nIf trajectory available compute caliber (see 6.5).\n6.\nIf caliber available compute weapon type (see 6.6).\nWe describe each step in the following sections in detail.\n6.1 Direction of arrival\nThe first step of the sensor fusion is to calculate the muzzle blast and shockwave AoA-s for each sensorboard.\nEach sensorboard has four microphones that measure the ToA-s.\nSince the microphone spacing is orders of magnitude smaller than the distance to the sound source, we can approximate the approaching sound wave front with a plane (far field assumption).\nLet us formalize the problem for 3 microphones first.\nLet P1, P2 and P3 be the position of the microphones ordered by time of arrival t1 <t2 <t3.\nFirst we apply a simple geometry validation step.\nThe measured time difference between two microphones cannot be larger than the sound propagation time between the two microphones:\nWhere c is the speed of sound and ε is the maximum measurement error.\nIf this condition does not hold, the corresponding detections are discarded.\nLet v (x, y, z) be the normal vector of the unknown direction of arrival.\nWe also use r1 (x1, y1, z1), the vector from P1 to P2 and r2 (x2, y2, z2), the vector from P1 to P3.\nLet's consider the projection of the direction of the motion of the wave front (v) to r1 divided by the speed of sound (c).\nThis gives us how long it takes the wave front to propagate form P1 to P2:\nMoving from vectors to coordinates using the dot product definition leads to a quadratic system:\nWe omit the solution steps here, as they are straightforward, but long.\nThere are two solutions (if the source is on the P1P2P3 plane the two solutions coincide).\nWe use the fourth microphone's measurement--if there is one--to eliminate one of them.\nOtherwise, both solutions are considered for further processing.\n6.2 Muzzle-shock fusion\nFigure 7: Section plane of a shot (at P) and two sensors (at P1 and at P2).\nOne sensor detects the muzzle blast's, the other the shockwave's time and direction of arrivals.\nConsider the situation in Figure 7.\nA shot was fired from P at time t. Both P and t are unknown.\nWe have one muzzle blast and one shockwave detections by two different sensors\nwith AoA and hence, ToA information available.\nThe muzzle blast detection is at position P1 with time t1 and AoA u.\nThe shockwave detection is at P2 with time t2 and AoA v. u and v are normal vectors.\nIt is shown below that these measurements are sufficient to compute the position of the shooter (P).\nLet P2 ~ be the point on the extended shockwave cone surface where PP2 ~ is perpendicular to the surface.\nNote that PP2 ~ is parallel with v.\nSince P2 ~ is on the cone surface which hits P2, a sensor at P2 ~ would detect the same shockwave time of arrival (t2).\nThe cone surface travels at the speed of sound (c), so we can express P using P ~ 2:\ncontaining only one unknown t.\nOne obtains:\nFrom here we can calculate the shoter position P. Let's consider the special single sensor case where P1 = P2 (one sensor detects both shockwave and muzzle blast AoA).\nIn this case:\nSince u and v are not used separately only uv, the absolute orientation of the sensor can be arbitrary, we still get t which gives us the range.\nHere we assumed that the shockwave is a cone which is only true for constant projectile speeds.\nIn reality, the angle of the cone slowly grows; the surface resembles one half of an American football.\nThe decelerating bullet results in a smaller time difference between the shockwave and the muzzle blast detections because the shockwave generation slows down with the bullet.\nA smaller time difference results in a smaller range, so the above formula underestimates the true range.\nHowever, it can still be used with a proper deceleration correction function.\nWe leave this for future work.\n6.3 Trajectory estimation\nDanicki showed that the bullet trajectory and speed can be computed analytically from two independent shockwave measurements where both ToA and AoA are measured [2].\nThe method gets more sensitive to measurement errors as the two shockwave directions get closer to each other.\nIn the special case when both directions are the same, the trajectory cannot be computed.\nIn a real world application, the sensors are typically deployed on a plane approximately.\nIn this case, all sensors located on one\" side\" of the trajectory measure almost the same shockwave AoA.\nTo avoid this error sensitivity problem, we consider shockwave measurement pairs only if the direction of arrival difference is larger than a certain threshold.\nWe have multiple sensors and one sensor can report two different directions (when only three microphones detect the shockwave).\nHence, we typically have several trajectory candidates, i.e. one for each AoA pair over the threshold.\nWe applied an outlier filtering and averaging method to fuse together the shockwave direction and time information and come up with a single trajectory.\nAssume that we have N individual shockwave AoA measurements.\nLet's take all possible unordered pairs where the direction difference is above the mentioned threshold and compute the trajectory for each.\nThis gives us at most N (N-1)\n2 trajectories.\nA tra\njectory is represented by one point pi and the normal vector vi (where i is the trajectory index).\nWe define the distance of two trajectories as the dot product of their normal vectors:\nwhere R is a radius parameter.\nThe largest neighbor set is considered to be the core set C, all other trajectories are outliers.\nThe core set can be found in O (N2) time.\nThe trajectories in the core set are then averaged to get the final trajectory.\nIt can happen that we cannot form any sensor pairs because of the direction difference threshold.\nIt means all sensors are on the same side of the trajectory.\nIn this case, we first compute the shooter position (described in the next section) that fixes p making v the only unknown.\nTo find v in this case, we use a simple high resolution grid search and minimize an error function based on the shockwave directions.\nWe have made experiments to utilize the measured shockwave length in the trajectory estimation.\nThere are some promising results, but it needs further research.\n6.4 Shooter position estimation\nThe shooter position estimation algorithm aggregates the following heterogenous information generated by earlier computational steps:\n1.\ntrajectory, 2.\nmuzzle blast ToA at a sensor, 3.\nmuzzle blast AoA at a sensor, which is effectively a bearing estimate to the shooter, and 4.\nrange estimate at a sensor (when both shockwave and muzzle blast AoA are available).\nSome sensors report only ToA, some has bearing estimate (s) also and some has range estimate (s) as well, depend\ning on the number of successful muzzle blast and shockwave detections by the sensor.\nFor an example, refer to Figure 8.\nNote that a sensor may have two different bearing and range estimates.\n3 detections gives two possible AoA-s for muzzle blast (i.e. bearing) and/or shockwave.\nFurthermore, the combination of two different muzzle blast and shockwave AoA-s may result in two different ranges.\nFigure 8: Example of heterogenous input data for the shooter position estimation algorithm.\nAll sen\nsors have ToA measurements (t1, t2, t3, t4, t5), one sensor has a single bearing estimate (v2), one sensor has two possible bearings (v3, v ~ 3) and one sensor has two bearing and two range estimates (v1, v ~ 1, r1, r ~ 1) In a multipath environment, these detections will not only contain gaussian noise, but also possibly large errors due to echoes.\nIt has been showed in our earlier work that a similar problem can be solved efficiently with an interval arithmetic based bisection search algorithm [8].\nThe basic idea is to define a discrete consistency function over the area of interest and subdivide the space into 3D boxes.\nFor any given 3D box, this function gives the number of measurements supporting the hypothesis that the shooter was within that box.\nThe search starts with a box large enough to contain the whole area of interest, then zooms in by dividing and evaluating boxes.\nThe box with the maximum consistency is divided until the desired precision is reached.\nBacktracking is possible to avoid getting stuck in a local maximum.\nThis approach has been shown to be fast enough for online processing.\nNote, however, that when the trajectory has already been calculated in previous steps, the search needs to be done only on the trajectory making it orders of magnitude faster.\nNext let us describe how the consistency function is calculated in detail.\nConsider B, a three dimensional box, we would like to compute the consistency value of.\nFirst we consider only the ToA information.\nIf one sensor has multiple ToA detections, we use the average of those times, so one sensor supplies at most one ToA estimate.\nFor each ToA, we can calculate the corresponding time of the shot, since the origin is assumed to be in box B.\nSince it is a box and not a single point, this gives us an interval for the shot time.\nThe maximum number of overlapping time intervals gives us the value of the consistency function for B. For a detailed description of the consistency function and search algorithm, refer to [8].\nHere we extend the approach the following way.\nWe modify the consistency function based on the bearing and range data from individual sensors.\nA bearing estimate supports B if the line segment starting from the sensor with the measured direction intersects the B box.\nA range supports B, if the sphere with the radius of the range and origin of the sensor intersects B. Instead of simply checking whether the position specified by the corresponding bearing-range pairs falls within B, this eliminates the sensor's possible orientation error.\nThe value of the consistency function is incremented by one for each bearing and range estimate that is consistent with B.\n6.5 Caliber estimation\nThe shockwave signal characteristics has been studied before by Whitham [20].\nHe showed that the shockwave period T is related to the projectile diameter d, the length l, the perpendicular miss distance b from the bullet trajectory to the sensor, the Mach number M and the speed of sound c.\nFigure 9: Shockwave length and miss distance relationship.\nEach data point represents one sensorboard after an aggregation of the individual measurements of the four acoustic channels.\nThree different caliber projectiles have been tested (196 shots, 10 sensors).\nTo illustrate the relationship between miss distance and shockwave length, here we use all 196 shots with three different caliber projectiles fired during the evaluation.\n(During the evaluation we used data obtained previously using a few practice shots per weapon.)\n10 sensors (4 microphones by sensor) measured the shockwave length.\nFor each sensor, we considered the shockwave length estimation valid if at least three out of four microphones agreed on a value with at most 5 microsecond variance.\nThis filtering leads to a 86% report rate per sensor and gets rid of large measurement errors.\nThe experimental data is shown in Figure 9.\nWhitham's formula suggests that the shockwave length for a given caliber can be approximated with a power function of the miss distance (with a 1/4 exponent).\nBest fit functions on our data are:\nTo evaluate a shot, we take the caliber whose approximation function results in the smallest RMS error of the filtered sensor readings.\nThis method has less than 1% caliber estimation error when an accurate trajectory estimate is available.\nIn other words, caliber estimation only works if enough shockwave detections are made by the system to compute a trajectory.\n6.6 Weapon estimation\nWe analyzed all measured signal characteristics to find weapon specific information.\nUnfortunately, we concluded that the observed muzzle blast signature is not characteristic enough of the weapon for classification purposes.\nThe reflections of the high energy muzzle blast from the environment have much higher impact on the muzzle blast signal shape than the weapon itself.\nShooting the same weapon from different places caused larger differences on the recorded signal than shooting different weapons from the same place.\nFigure 10: AK47 and M240 bullet deceleration measurements.\nBoth weapons have the same caliber.\nData is approximated using simple linear regression.\nFigure 11: M16, M249 and M4 bullet deceleration measurements.\nAll weapons have the same caliber.\nData is approximated using simple linear regression.\nHowever, the measured speed of the projectile and its caliber showed good correlation with the weapon type.\nThis is because for a given weapon type and ammunition pair, the muzzle velocity is nearly constant.\nIn Figures 10 and 11 we can see the relationship between the range and the measured bullet speed for different calibers and weapons.\nIn the supersonic speed range, the bullet deceleration can be approximated with a linear function.\nIn case of the 7.62 mm caliber, the two tested weapons (AK47, M240) can be clearly separated (Figure 10).\nUnfortunately, this is not necessarily true for the 5.56 mm caliber.\nThe M16 with its higher muzzle speed can still be well classified, but the M4 and M249 weapons seem practically undistinguishable (Figure 11).\nHowever, this may be partially due to the limited number of practice shots we were able to take before the actual testing began.\nMore training data may reveal better separation between the two weapons since their published muzzle velocities do differ somewhat.\nThe system carries out weapon classification in the following manner.\nOnce the trajectory is known, the speed can be calculated for each sensor based on the shockwave geometry.\nTo evaluate a shot, we choose the weapon type whose deceleration function results in the smallest RMS error of the estimated range-speed pairs for the estimated caliber class.\n7.\nRESULTS\nAn independent evaluation of the system was carried out by a team from NIST at the US Army Aberdeen Test Center in April 2006 [19].\nThe experiment was setup on a shooting range with mock-up wooden buildings and walls for supporting elevated shooter positions and generating multipath effects.\nFigure 12 shows the user interface with an aerial photograph of the site.\n10 sensor nodes were deployed on surveyed points in an approximately 30 × 30 m area.\nThere were five fixed targets behind the sensor network.\nSeveral firing positions were located at each of the firing lines at 50, 100, 200 and 300 meters.\nThese positions were known to the evaluators, but not to the operators of the system.\nSix different weapons were utilized: AK47 and M240 firing 7.62 mm projectiles, M16, M4 and M249 with 5.56 mm ammunition and the .50 caliber M107.\nNote that the sensors remained static during the test.\nThe primary reason for this is that nobody is allowed downrange during live fire tests.\nUtilizing some kind of remote control platform would have been too involved for the limited time the range was available for the test.\nThe experiment, therefore, did not test the mobility aspect of the system.\nDuring the one day test, there were 196 shots fired.\nThe results are summarized in Table 1.\nThe system detected all shots successfully.\nSince a ballistic shockwave is a unique acoustic phenomenon, it makes the detection very robust.\nThere were no false positives for shockwaves, but there were a handful of false muzzle blast detections due to parallel tests of artillery at a nearby range.\nTable 1: Summary of results fusing all available sen\nsor observations.\nAll shots were successfully detected, so the detection rate is omitted.\nLocalization rate means the percentage of shots that the sensor fusion was able to estimate the trajectory of.\nThe caliber accuracy rate is relative to the shots localized and not all the shots because caliber estimation requires the trajectory.\nThe trajectory error is broken down to azimuth in degrees and the actual distance of the shooter from the trajectory.\nThe distance error shows the distance between the real shooter position and the estimated shooter position.\nAs such, it includes the error caused by both the trajectory and that of the range estimation.\nNote that the traditional bearing and range measures are not good ones for a distributed system such as ours because of the lack of a single reference point.\nFigure 12: The user interface of the system show\ning the experimental setup.\nThe 10 sensor nodes are labeled by their ID and marked by dark circles.\nThe targets are black squares marked T-1 through T-5.\nThe long white arrows point to the shooter position estimated by each sensor.\nWhere it is missing, the corresponding sensor did not have enough detections to measure the AoA of either the muzzle blast, the shockwave or both.\nThe thick black line and large circle indicate the estimated trajectory and the shooter position as estimated by fusing all available detections from the network.\nThis shot from the 100-meter line at target T-3 was localized almost perfectly by the sensor network.\nThe caliber and weapon were also identified correctly.\n6 out of 10 nodes were able to estimate the location alone.\nTheir bearing accuracy is within a degree, while the range is off by less than 10% in the worst case.\nThe localization rate characterizes the system's ability to successfully estimate the trajectory of shots.\nSince caliber estimation and weapon classification relies on the trajectory, non-localized shots are not classified either.\nThere were 7 shots out of 196 that were not localized.\nThe reason for missed shots is the trajectory ambiguity problem that occurs when the projectile passes on one side of all the sensors.\nIn this case, two significantly different trajectories can generate the same set of observations (see [8] and also Section 6.3).\nInstead of estimating which one is more likely or displaying both possibilities, we decided not to provide a trajectory at all.\nIt is better not to give an answer other than a shot alarm than misleading the soldier.\nLocalization accuracy is broken down to trajectory accuracy and range estimation precision.\nThe angle of the estimated trajectory was better than 1 degree except for the 300 m range.\nSince the range should not affect trajectory estimation as long as the projectile passes over the network, we suspect that the slightly worse angle precision for 300 m is due to the hurried shots we witnessed the soldiers took near the end of the day.\nThis is also indicated by another datapoint: the estimated trajectory distance from the actual targets has an average error of 1.3 m for 300 m shots, 0.75 m for 200 m shots and 0.6 m for all but 300 m shots.\nAs the distance between the targets and the sensor network was fixed, this number should not show a 2 × improvement just because the shooter is closer.\nSince the angle of the trajectory itself does not characterize the overall error--there can be a translation also--Table 1 also gives the distance of the shooter from the estimated trajectory.\nThese indicate an error which is about 1-2% of the range.\nTo put this into perspective, a trajectory estimate for a 100 m shot will very likely go through or very near the window the shooter is located at.\nAgain, we believe that the disproportionally larger errors at 300 m are due to human errors in aiming.\nAs the ground truth was obtained by knowing the precise location of the shooter and the target, any inaccuracy in the actual trajectory directly adds to the perceived error of the system.\nWe call the estimation of the shooter's position on the calculated trajectory range estimation due to the lack of a better term.\nThe range estimates are better than 5% accurate from 50 m and 10% for 100 m. However, this goes to 20% or worse for longer distances.\nWe did not have a facility to test system before the evaluation for ranges beyond 100 m.\nDuring the evaluation, we ran into the problem of mistaking shockwave echoes for muzzle blasts.\nThese echoes reached the sensors before the real muzzle blast for long range shots only, since the projectile travels 2-3 × faster than the speed of sound, so the time between the shockwave (and its possible echo from nearby objects) and the muzzle blast increases with increasing ranges.\nThis resulted in underestimating the range, since the system measured shorter times than the real ones.\nSince the evaluation we finetuned the muzzle blast detection algorithm to avoid this problem.\nTable 2: Weapon classification results.\nThe percent\nages are relative to the number of shots localized and not all shots, as the classification algorithm needs to know the trajectory and the range.\nNote that the difference is small; there were 189 shots localized out of the total 196.\nThe caliber and weapon estimation accuracy rates are based on the 189 shots that were successfully localized.\nNote that there was a single shot that was falsely classified by the caliber estimator.\nThe 73% overall weapon classification accuracy does not seem impressive.\nBut if we break it down to the six different weapons tested, the picture changes dramatically as shown in Table 2.\nFor four of the weapons (AK14, M16, M240 and M107), the classification rate is almost 100%.\nThere were only two shots out of approximately 140 that were missed.\nThe M4 and M249 proved to be too similar and they were mistaken for each other most of the time.\nOne possible explanation is that we had only a limited number of test shots taken with these weapons right before the evaluation and used the wrong deceleration approximation function.\nEither this or a similar mistake was made\nsince if we simply used the opposite of the system's answer where one of these weapons were indicated, the accuracy would have improved 3x.\nIf we consider these two weapons a single weapon class, then the classification accuracy for it becomes 93%.\nNote that the AK47 and M240 have the same caliber (7.62 mm), just as the M16, M4 and M249 do (5.56 mm).\nThat is, the system is able to differentiate between weapons of the same caliber.\nWe are not aware of any system that classifies weapons this accurately.\n7.1 Single sensor performance\nAs was shown previously, a single sensor alone is able to localize the shooter if it can determine both the muzzle blast and the shockwave AoA, that is, it needs to measure the ToA of both on at least three acoustic channels.\nWhile shockwave detection is independent of the range--unless the projectile becomes subsonic--, the likelihood of muzzle blast detection beyond 150 meters is not enough for consistently getting at least three per sensor node for AoA estimation.\nHence, we only evaluate the single sensor performance for the 104 shots that were taken from 50 and 100 m. Note that we use the same test data as in the previous section, but we evaluate individually for each sensor.\nTable 3 summarizes the results broken down by the ten sensors utilized.\nSince this is now not a distributed system, the results are given relative to the position of the given sensor, that is, a bearing and range estimate is provided.\nNote that many of the common error sources of the networked system do not play a role here.\nTime synchronization is not applicable.\nThe sensor's absolute location is irrelevant (just as the relative location of multiple sensors).\nThe sensor's orientation is still important though.\nThere are several disadvantages of the single sensor case compared to the networked system: there is no redundancy to compensate for other errors and to perform outlier rejection, the localization rate is markedly lower, and a single sensor alone is not able to estimate the caliber or classify the weapon.\nTable 3: Single sensor accuracy for 108 shots fired\nfrom 50 and 100 meters.\nLocalization rate refers to the percentage of shots the given sensor alone was able to localize.\nThe bearing and range values are average errors.\nThey characterize the accuracy of localization from the given sensor's perspective.\nThe data indicates that the performance of the sensors varied significantly especially considering the localization rate.\nOne factor has to be the location of the given sensor including how far it was from the firing lines and how obstructed its view was.\nAlso, the sensors were hand-built prototypes utilizing nowhere near production quality packaging/mounting.\nIn light of these factors, the overall average bearing error of 0.9 degrees and range error of 5 m with a microphone spacing of less than 10 cm are excellent.\nWe believe that professional manufacturing and better microphones could easily achieve better performance than the best sensor in our experiment (> 60% localization rate and 3 m range error).\nInterestingly, the largest error in range was a huge 90 m clearly due to some erroneous detection, yet the largest bearing error was less than 12 degrees which is still a good indication for the soldier where to look.\nThe overall localization rate over all single sensors was 42%, while for 50 m shots only, this jumped to 61%.\nNote that the firing range was prepared to simulate an urban area to some extent: there were a few single - and two-storey wooden structures built both in and around the sensor deployment area and the firing lines.\nHence, not all sensors had line-of-sight to all shooting positions.\nWe estimate that 10% of the sensors had obstructed view to the shooter on average.\nHence, we can claim that a given sensor had about 50% chance of localizing a shot within 130 m. (Since the sensor deployment area was 30 m deep, 100 m shots correspond to actual distances between 100 and 130 m.) Again, we emphasize that localization needs at least three muzzle blast and three shockwave detections out of a possible four for each per sensor.\nThe detection rate for single sensors--corresponding to at least one shockwave detection per sensor--was practically 100%.\nFigure 13: Histogram showing what fraction of the\n104 shots taken from 50 and 100 meters were localized by at most how many individual sensors alone.\n13% of the shots were missed by every single sensor, i.e., none of them had both muzzle blast and shockwave AoA detections.\nNote that almost all of these shots were still accurately localized by the networked system, i.e. the sensor fusion using all available observations in the sensor network.\nIt would be misleading to interpret these results as the system missing half the shots.\nAs soldiers never work alone and the sensor node is relatively cheap to afford having every soldier equipped with one, we also need to look at the overall detection rates for every shot.\nFigure 13 shows the histogram of the percentage of shots vs. the number of individual sensors that localized it.\n13% of shots were not localized by any sensor alone, but 87% was localized by at least one sensor out of ten.\n7.2 Error sources\nIn this section, we analyze the most significant sources of error that affect the performance of the networked shooter localization and weapon classification system.\nIn order to correlate the distributed observations of the acoustic events, the nodes need to have a common time and space reference.\nHence, errors in the time synchronization, node localization and node orientation all degrade the overall accuracy of the system.\npercentage of shots\nOur time synchronization approach yields errors significantly less than 100 microseconds.\nAs the sound travels about 3 cm in that time, time synchronization errors have a negligible effect on the system.\nOn the other hand, node location and orientation can have a direct effect on the overall system performance.\nNotice that to analyze this, we do not have to resort to simulation, instead we can utilize the real test data gathered at Aberdeen.\nBut instead of using the real sensor locations known very accurately and the measured and calibrated almost perfect node orientations, we can add error terms to them and run the sensor fusion.\nThis exactly replicates how the system would have performed during the test using the imprecisely known locations and orientations.\nAnother aspect of the system performance that can be evaluated this way is the effect of the number of available sensors.\nInstead of using all ten sensors in the data fusion, we can pick any subset of the nodes to see how the accuracy degrades as we decrease the number of nodes.\nThe following experiment was carried out.\nThe number of sensors were varied from 2 to 10 in increments of 2.\nEach run picked the sensors randomly using a uniform distribution.\nAt each run each node was randomly\" moved\" to a new location within a circle around its true position with a radius determined by a zero-mean Gaussian distribution.\nFinally, the node orientations were perturbed using a zeromean Gaussian distribution.\nEach combination of parameters were generated 100 times and utilized for all 196 shots.\nThe results are summarized in Figure 14.\nThere is one 3D barchart for each of the experiment sets with the given fixed number of sensors.\nThe x-axis shows the node location error, that is, the standard deviation of the corresponding Gaussian distribution that was varied between 0 and 6 meters.\nThe y-axis shows the standard deviation of the node orientation error that was varied between 0 and 6 degrees.\nThe z-axis is the resulting trajectory azimuth error.\nNote that the elevation angles showed somewhat larger errors than the azimuth.\nSince all the sensors were in approximately a horizontal plane and only a few shooter positions were out of the same plane and only by 2 m or so, the test was not sufficient to evaluate this aspect of the system.\nThere are many interesting observation one can make by analyzing these charts.\nNode location errors in this range have a small effect on accuracy.\nNode orientation errors, on the other hand, noticeably degrade the performance.\nStill the largest errors in this experiment of 3.5 degrees for 6 sensors and 5 degrees for 2 sensors are still very good.\nNote that as the location and orientation errors increase and the number of sensors decrease, the most significantly affected performance metric is the localization rate.\nSee Table 4 for a summary.\nSuccessful localization goes down from almost 100% to 50% when we go from 10 sensors to 2 even without additional errors.\nThis is primarily caused by geometry: for a successful localization, the bullet needs to pass over the sensor network, that is, at least one sensor should be on the side of the trajectory other than the rest of the nodes.\n(This is a simplification for illustrative purposes.\nIf all the sensors and the trajectory are not coplanar, localization may be successful even if the projectile passes on one side of the network.\nSee Section 6.3.)\nAs the numbers of sensors decreased in the experiment by randomly selecting a subset, the probability of trajectories abiding by this rule decreased.\nThis also means that even if there are\nFigure 14: The effect of node localization and orientation errors on azimuth accuracy with 2, 4, 6 and 8 nodes.\nNote that the chart for 10 nodes is almost identical for the 8-node case, hence, it is omitted.\nmany sensors (i.e. soldiers), but all of them are right next to each other, the localization rate will suffer.\nHowever, when the sensor fusion does provide a result, it is still accurate even with few available sensors and relatively large individual errors.\nA very few consistent observation lead to good accuracy as the inconsistent ones are discarded by the algorithm.\nThis is also supported by the observation that for the cases with the higher number of sensors (8 or 10), the localization rate is hardly affected by even large errors.\nTable 4: Localization rate as a function of the number of sensors used, the sensor node location and orientation errors.\nOne of the most significant observations on Figure 14 and Table 4 is that there is hardly any difference in the data for 6, 8 and 10 sensors.\nThis means that there is little advantage of adding more nodes beyond 6 sensors as far as the accuracy is concerned.\nThe speed of sound depends on the ambient temperature.\nThe current prototype considers it constant that is typically set before a test.\nIt would be straightforward to employ a temperature sensor to update the value of the speed of sound periodically during operation.\nNote also that wind may adversely affect the accuracy of the system.\nThe sensor fusion, however, could incorporate wind speed into its calculations.\nIt would be more complicated than temperature compensation, but could be done.\nOther practical issues also need to be looked at before a real world deployment.\nSilencers reduce the muzzle blast energy and hence, the effective range the system can detect it at.\nHowever, silencers do not effect the shockwave and the system would still detect the trajectory and caliber accurately.\nThe range and weapon type could not be estimated without muzzle blast detections.\nSubsonic weapons do not produce a shockwave.\nHowever, this is not of great significance, since they have shorter range, lower accuracy and much less lethality.\nHence, their use is not widespread and they pose less danger in any case.\nAnother issue is the type of ammunition used.\nIrregular armies may use substandard, even hand manufactured bullets.\nThis effects the muzzle velocity of the weapon.\nFor weapon classification to work accurately, the system would need to be calibrated with the typical ammunition used by the given adversary.\n8.\nRELATED WORK\nAcoustic detection and recognition has been under research since the early fifties.\nThe area has a close relevance to the topic of supersonic flow mechanics [20].\nFansler analyzed the complex near-field pressure waves that occur within a foot of the muzzle blast.\nFansler's work gives a good idea of the ideal muzzle blast pressure wave without contamination from echoes or propagation effects [4].\nExperiments with greater distances from the muzzle were conducted by Stoughton [18].\nThe measurements of the ballistic shockwaves using calibrated pressure transducers at known locations, measured bullet speeds, and miss distances of 3 55 meters for 5.56 mm and 7.62 mm projectiles were made.\nResults indicate that ground interaction becomes a problem for miss distances of 30 meters or larger.\nAnother area of research is the signal processing of gunfire acoustics.\nThe focus is on the robust detection and length estimation of small caliber acoustic shockwaves and muzzle blasts.\nPossible techniques for classifying signals as either shockwaves or muzzle blasts includes short-time Fourier Transform (STFT), the Smoothed Pseudo Wigner-Ville distribution (SPWVD), and a discrete wavelet transformation (DWT).\nJoint time-frequency (JTF) spectrograms are used to analyze the typical separation of the shockwave and muzzle blast transients in both time and frequency.\nMays concludes that the DWT is the best method for classifying signals as either shockwaves or muzzle blasts because it works well and is less expensive to compute than the SPWVD [10].\nThe edges of the shockwave are typically well defined and the shockwave length is directly related to the bullet characteristics.\nA paper by Sadler [14] compares two shockwave edge detection methods: a simple gradient-based detector, and a multi-scale wavelet detector.\nIt also demonstrates how the length of the shockwave, as determined by the edge detectors, can be used along with Whithams equations [20] to estimate the caliber of a projectile.\nNote that the available computational performance on the sensor nodes, the limited wireless bandwidth and real-time requirements render these approaches infeasible on our platform.\nA related topic is the research and development of experimental and prototype shooter location systems.\nResearchers at BBN have developed the Bullet Ears system [3] which has the capability to be installed in a fixed position or worn by soldiers.\nThe fixed system has tetrahedron shaped microphone arrays with 1.5 meter spacing.\nThe overall system consists of two to three of these arrays spaced 20 to 100 meters from each other.\nThe soldier-worn system has 12 microphones as well as a GPS antenna and orientation sensors mounted on a helmet.\nThere is a low speed RF connection from the helmet to the processing body.\nAn extensive test has been conducted to measure the performance of both type of systems.\nThe fixed systems performance was one order of magnitude better in the angle calculations while their range performance where matched.\nThe angle accuracy of the fixed system was dominantly less than one degree while it was around five degrees for the helmet mounted one.\nThe range accuracy was around 5 percent for both of the systems.\nThe problem with this and similar centralized systems is the need of the one or handful of microphone arrays to be in line-of-sight of the shooter.\nA sensor networked based solution has the advantage of widely distributed sensing for better coverage, multipath effect compensation and multiple simultaneous shot resolution [8].\nThis is especially important for operation in acoustically reverberant urban areas.\nNote that BBN's current vehicle-mounted system called BOOMERANG, a modified version of Bullet Ears, is currently used in Iraq [1].\nThe company ShotSpotter specializes in law enforcement systems that report the location of gunfire to police within seconds.\nThe goal of the system is significantly different than that of military systems.\nShotspotter reports 25 m typical accuracy which is more than enough for police to\nrespond.\nThey are also manufacturing experimental soldier wearable and UAV mounted systems for military use [16], but no specifications or evaluation results are publicly available.\n9.\nCONCLUSIONS\nThe main contribution of this work is twofold.\nFirst, the performance of the overall distributed networked system is excellent.\nMost noteworthy are the trajectory accuracy of one degree, the correct caliber estimation rate of well over 90% and the close to 100% weapon classification rate for 4 of the 6 weapons tested.\nThe system proved to be very robust when increasing the node location and orientation errors and decreasing the number of available sensors all the way down to a couple.\nThe key factor behind this is the sensor fusion algorithm's ability to reject erroneous measurements.\nIt is also worth mentioning that the results presented here correspond to the first and only test of the system beyond 100 m and with six different weapons.\nWe believe that with the lessons learned in the test, a consecutive field experiment could have showed significantly improved results especially in range estimation beyond 100 m and weapon classification for the remaining two weapons that were mistaken for each other the majority of the times during the test.\nSecond, the performance of the system when used in standalone mode, that is, when single sensors alone provided localization, was also very good.\nWhile the overall localization rate of 42% per sensor for shots up to 130 m could be improved, the bearing accuracy of less than a degree and the average 5% range error are remarkable using the handmade prototypes of the low-cost nodes.\nNote that 87% of the shots were successfully localized by at least one of the ten sensors utilized in standalone mode.\nWe believe that the technology is mature enough that a next revision of the system could be a commercial one.\nHowever, important aspects of the system would still need to be worked on.\nWe have not addresses power management yet.\nA current node runs on 4 AA batteries for about 12 hours of continuous operation.\nA deployable version of the sensor node would need to be asleep during normal operation and only wake up when an interesting event occurs.\nAn analog trigger circuit could solve this problem, however, the system would miss the first shot.\nInstead, the acoustic channels would need to be sampled and stored in a circular buffer.\nThe rest of the board could be turned off.\nWhen a trigger wakes up the board, the acoustic data would be immediately available.\nExperiments with a previous generation sensor board indicated that this could provide a 10x increase in battery life.\nOther outstanding issues include weatherproof packaging and ruggedization, as well as integration with current military infrastructure."},"lvl-3":{"kind":"string","value":"Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors\nABSTRACT\nThe paper presents a wireless sensor network-based mobile countersniper system.\nA sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implements the acoustic detection and Time of Arrival (ToA) estimation algorithms on an FPGA.\nA 3-axis compass provides self orientation and Bluetooth is used for communication with the soldier's PDA running the data fusion and the user interface.\nThe heterogeneous sensor fusion algorithm can work with data from a single sensor or it can fuse ToA or Angle of Arrival (AoA) observations of muzzle blasts and ballistic shockwaves from multiple sensors.\nThe system estimates the trajectory, the range, the caliber and the weapon type.\nThe paper presents the system design and the results from an independent evaluation at the US Army Aberdeen Test Center.\nThe system performance is characterized by 1degree trajectory precision and over 95% caliber estimation accuracy for all shots, and close to 100% weapon estimation accuracy for 4 out of 6 guns tested.\n1.\nINTRODUCTION\nThe importance of countersniper systems is underscored by the constant stream of news reports coming from the Middle East.\nIn October 2006 CNN reported on a new tactic employed by insurgents.\nA mobile sniper team moves around busy city streets in a car, positions itself at a good standoff distance from dismounted US military personnel, takes a single well-aimed shot and immediately melts in the city traffic.\nBy the time the soldiers can react, they are gone.\nA countersniper system that provides almost immediate shooter location to every soldier in the vicinity would provide clear benefits to the warfigthers.\nOur team introduced PinPtr, the first sensor networkbased countersniper system [17, 8] in 2003.\nThe system is based on potentially hundreds of inexpensive sensor nodes deployed in the area of interest forming an ad hoc multihop network.\nThe acoustic sensors measure the Time of Arrival (ToA) of muzzle blasts and ballistic shockwaves, pressure waves induced by the supersonic projectile, send the data to a base station where a sensor fusion algorithm determines the origin of the shot.\nPinPtr is characterized by high precision: 1m average 3D accuracy for shots originating within or near the sensor network and 1 degree bearing precision for both azimuth and elevation and 10% accuracy in range estimation for longer range shots.\nThe truly unique characteristic of the system is that it works in such reverberant environments as cluttered urban terrain and that it can resolve multiple simultaneous shots at the same time.\nThis capability is due to the widely distributed sensing and the unique sensor fusion approach [8].\nThe system has been tested several times in US Army MOUT (Military Operations in Urban Terrain) facilities.\nThe obvious disadvantage of such a system is its static nature.\nOnce the sensors are distributed, they cover a certain area.\nDepending on the operation, the deployment may be needed for an hour or a month, but eventually the area looses its importance.\nIt is not practical to gather and reuse the sensors, especially under combat conditions.\nEven if the sensors are cheap, it is still a waste and a logistical problem to provide a continuous stream of sensors as the operations move from place to place.\nAs it is primarily the soldiers that the system protects, a natural extension is to mount the sensors on the soldiers themselves.\nWhile there are vehiclemounted countersniper systems [1] available commercially, we are not aware of a deployed system that protects dismounted soldiers.\nA helmet-mounted system was developed in the mid 90s by BBN [3], but it was not continued beyond the Darpa program that funded it.\nTo move from a static sensor network-based solution to a highly mobile one presents significant challenges.\nThe sensor positions and orientation need to be constantly monitored.\nAs soldiers may work in groups of as little as four people, the number of sensors measuring the acoustic phenomena may be an order of magnitude smaller than before.\nMoreover, the system should be useful to even a single soldier.\nFinally, additional requirements called for caliber estimation and weapon classification in addition to source localization.\nThe paper presents the design and evaluation of our soldierwearable mobile countersniper system.\nIt describes the hardware and software architecture including the custom sensor board equipped with a small microphone array and connected to a COTS MICAz mote [12].\nSpecial emphasis is paid to the sensor fusion technique that estimates the trajectory, range, caliber and weapon type simultaneously.\nThe results and analysis of an independent evaluation of the system at the US Army Aberdeen Test Center are also presented.\n2.\nAPPROACH\n3.\nHARDWARE\n4.\nSOFTWARE ARCHITECTURE\n5.\nDETECTION ALGORITHM\n6.\nSENSOR FUSION\n6.1 Direction of arrival\n6.2 Muzzle-shock fusion\n6.3 Trajectory estimation\n6.4 Shooter position estimation\n6.5 Caliber estimation\n6.6 Weapon estimation\n7.\nRESULTS\n7.1 Single sensor performance\n7.2 Error sources\n8.\nRELATED WORK\nAcoustic detection and recognition has been under research since the early fifties.\nThe area has a close relevance to the topic of supersonic flow mechanics [20].\nFansler analyzed the complex near-field pressure waves that occur within a foot of the muzzle blast.\nFansler's work gives a good idea of the ideal muzzle blast pressure wave without contamination from echoes or propagation effects [4].\nExperiments with greater distances from the muzzle were conducted by Stoughton [18].\nThe measurements of the ballistic shockwaves using calibrated pressure transducers at known locations, measured bullet speeds, and miss distances of 3 55 meters for 5.56 mm and 7.62 mm projectiles were made.\nResults indicate that ground interaction becomes a problem for miss distances of 30 meters or larger.\nAnother area of research is the signal processing of gunfire acoustics.\nThe focus is on the robust detection and length estimation of small caliber acoustic shockwaves and muzzle blasts.\nPossible techniques for classifying signals as either shockwaves or muzzle blasts includes short-time Fourier Transform (STFT), the Smoothed Pseudo Wigner-Ville distribution (SPWVD), and a discrete wavelet transformation (DWT).\nJoint time-frequency (JTF) spectrograms are used to analyze the typical separation of the shockwave and muzzle blast transients in both time and frequency.\nMays concludes that the DWT is the best method for classifying signals as either shockwaves or muzzle blasts because it works well and is less expensive to compute than the SPWVD [10].\nThe edges of the shockwave are typically well defined and the shockwave length is directly related to the bullet characteristics.\nA paper by Sadler [14] compares two shockwave edge detection methods: a simple gradient-based detector, and a multi-scale wavelet detector.\nIt also demonstrates how the length of the shockwave, as determined by the edge detectors, can be used along with Whithams equations [20] to estimate the caliber of a projectile.\nNote that the available computational performance on the sensor nodes, the limited wireless bandwidth and real-time requirements render these approaches infeasible on our platform.\nA related topic is the research and development of experimental and prototype shooter location systems.\nResearchers at BBN have developed the Bullet Ears system [3] which has the capability to be installed in a fixed position or worn by soldiers.\nThe fixed system has tetrahedron shaped microphone arrays with 1.5 meter spacing.\nThe overall system consists of two to three of these arrays spaced 20 to 100 meters from each other.\nThe soldier-worn system has 12 microphones as well as a GPS antenna and orientation sensors mounted on a helmet.\nThere is a low speed RF connection from the helmet to the processing body.\nAn extensive test has been conducted to measure the performance of both type of systems.\nThe fixed systems performance was one order of magnitude better in the angle calculations while their range performance where matched.\nThe angle accuracy of the fixed system was dominantly less than one degree while it was around five degrees for the helmet mounted one.\nThe range accuracy was around 5 percent for both of the systems.\nThe problem with this and similar centralized systems is the need of the one or handful of microphone arrays to be in line-of-sight of the shooter.\nA sensor networked based solution has the advantage of widely distributed sensing for better coverage, multipath effect compensation and multiple simultaneous shot resolution [8].\nThis is especially important for operation in acoustically reverberant urban areas.\nNote that BBN's current vehicle-mounted system called BOOMERANG, a modified version of Bullet Ears, is currently used in Iraq [1].\nThe company ShotSpotter specializes in law enforcement systems that report the location of gunfire to police within seconds.\nThe goal of the system is significantly different than that of military systems.\nShotspotter reports 25 m typical accuracy which is more than enough for police to\nrespond.\nThey are also manufacturing experimental soldier wearable and UAV mounted systems for military use [16], but no specifications or evaluation results are publicly available.\n9.\nCONCLUSIONS\nThe main contribution of this work is twofold.\nFirst, the performance of the overall distributed networked system is excellent.\nMost noteworthy are the trajectory accuracy of one degree, the correct caliber estimation rate of well over 90% and the close to 100% weapon classification rate for 4 of the 6 weapons tested.\nThe system proved to be very robust when increasing the node location and orientation errors and decreasing the number of available sensors all the way down to a couple.\nThe key factor behind this is the sensor fusion algorithm's ability to reject erroneous measurements.\nIt is also worth mentioning that the results presented here correspond to the first and only test of the system beyond 100 m and with six different weapons.\nWe believe that with the lessons learned in the test, a consecutive field experiment could have showed significantly improved results especially in range estimation beyond 100 m and weapon classification for the remaining two weapons that were mistaken for each other the majority of the times during the test.\nSecond, the performance of the system when used in standalone mode, that is, when single sensors alone provided localization, was also very good.\nWhile the overall localization rate of 42% per sensor for shots up to 130 m could be improved, the bearing accuracy of less than a degree and the average 5% range error are remarkable using the handmade prototypes of the low-cost nodes.\nNote that 87% of the shots were successfully localized by at least one of the ten sensors utilized in standalone mode.\nWe believe that the technology is mature enough that a next revision of the system could be a commercial one.\nHowever, important aspects of the system would still need to be worked on.\nWe have not addresses power management yet.\nA current node runs on 4 AA batteries for about 12 hours of continuous operation.\nA deployable version of the sensor node would need to be asleep during normal operation and only wake up when an interesting event occurs.\nAn analog trigger circuit could solve this problem, however, the system would miss the first shot.\nInstead, the acoustic channels would need to be sampled and stored in a circular buffer.\nThe rest of the board could be turned off.\nWhen a trigger wakes up the board, the acoustic data would be immediately available.\nExperiments with a previous generation sensor board indicated that this could provide a 10x increase in battery life.\nOther outstanding issues include weatherproof packaging and ruggedization, as well as integration with current military infrastructure."},"lvl-4":{"kind":"string","value":"Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors\nABSTRACT\nThe paper presents a wireless sensor network-based mobile countersniper system.\nA sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implements the acoustic detection and Time of Arrival (ToA) estimation algorithms on an FPGA.\nA 3-axis compass provides self orientation and Bluetooth is used for communication with the soldier's PDA running the data fusion and the user interface.\nThe heterogeneous sensor fusion algorithm can work with data from a single sensor or it can fuse ToA or Angle of Arrival (AoA) observations of muzzle blasts and ballistic shockwaves from multiple sensors.\nThe system estimates the trajectory, the range, the caliber and the weapon type.\nThe paper presents the system design and the results from an independent evaluation at the US Army Aberdeen Test Center.\nThe system performance is characterized by 1degree trajectory precision and over 95% caliber estimation accuracy for all shots, and close to 100% weapon estimation accuracy for 4 out of 6 guns tested.\n1.\nINTRODUCTION\nThe importance of countersniper systems is underscored by the constant stream of news reports coming from the Middle East.\nBy the time the soldiers can react, they are gone.\nA countersniper system that provides almost immediate shooter location to every soldier in the vicinity would provide clear benefits to the warfigthers.\nOur team introduced PinPtr, the first sensor networkbased countersniper system [17, 8] in 2003.\nThe system is based on potentially hundreds of inexpensive sensor nodes deployed in the area of interest forming an ad hoc multihop network.\nThe acoustic sensors measure the Time of Arrival (ToA) of muzzle blasts and ballistic shockwaves, pressure waves induced by the supersonic projectile, send the data to a base station where a sensor fusion algorithm determines the origin of the shot.\nThe truly unique characteristic of the system is that it works in such reverberant environments as cluttered urban terrain and that it can resolve multiple simultaneous shots at the same time.\nThis capability is due to the widely distributed sensing and the unique sensor fusion approach [8].\nThe system has been tested several times in US Army MOUT (Military Operations in Urban Terrain) facilities.\nThe obvious disadvantage of such a system is its static nature.\nOnce the sensors are distributed, they cover a certain area.\nIt is not practical to gather and reuse the sensors, especially under combat conditions.\nEven if the sensors are cheap, it is still a waste and a logistical problem to provide a continuous stream of sensors as the operations move from place to place.\nAs it is primarily the soldiers that the system protects, a natural extension is to mount the sensors on the soldiers themselves.\nWhile there are vehiclemounted countersniper systems [1] available commercially, we are not aware of a deployed system that protects dismounted soldiers.\nA helmet-mounted system was developed in the mid 90s by BBN [3], but it was not continued beyond the Darpa program that funded it.\nTo move from a static sensor network-based solution to a highly mobile one presents significant challenges.\nThe sensor positions and orientation need to be constantly monitored.\nAs soldiers may work in groups of as little as four people, the number of sensors measuring the acoustic phenomena may be an order of magnitude smaller than before.\nMoreover, the system should be useful to even a single soldier.\nFinally, additional requirements called for caliber estimation and weapon classification in addition to source localization.\nThe paper presents the design and evaluation of our soldierwearable mobile countersniper system.\nIt describes the hardware and software architecture including the custom sensor board equipped with a small microphone array and connected to a COTS MICAz mote [12].\nSpecial emphasis is paid to the sensor fusion technique that estimates the trajectory, range, caliber and weapon type simultaneously.\nThe results and analysis of an independent evaluation of the system at the US Army Aberdeen Test Center are also presented.\n8.\nRELATED WORK\nAcoustic detection and recognition has been under research since the early fifties.\nFansler analyzed the complex near-field pressure waves that occur within a foot of the muzzle blast.\nFansler's work gives a good idea of the ideal muzzle blast pressure wave without contamination from echoes or propagation effects [4].\nExperiments with greater distances from the muzzle were conducted by Stoughton [18].\nResults indicate that ground interaction becomes a problem for miss distances of 30 meters or larger.\nAnother area of research is the signal processing of gunfire acoustics.\nThe focus is on the robust detection and length estimation of small caliber acoustic shockwaves and muzzle blasts.\nJoint time-frequency (JTF) spectrograms are used to analyze the typical separation of the shockwave and muzzle blast transients in both time and frequency.\nMays concludes that the DWT is the best method for classifying signals as either shockwaves or muzzle blasts because it works well and is less expensive to compute than the SPWVD [10].\nThe edges of the shockwave are typically well defined and the shockwave length is directly related to the bullet characteristics.\nNote that the available computational performance on the sensor nodes, the limited wireless bandwidth and real-time requirements render these approaches infeasible on our platform.\nA related topic is the research and development of experimental and prototype shooter location systems.\nResearchers at BBN have developed the Bullet Ears system [3] which has the capability to be installed in a fixed position or worn by soldiers.\nThe fixed system has tetrahedron shaped microphone arrays with 1.5 meter spacing.\nThe overall system consists of two to three of these arrays spaced 20 to 100 meters from each other.\nThe soldier-worn system has 12 microphones as well as a GPS antenna and orientation sensors mounted on a helmet.\nAn extensive test has been conducted to measure the performance of both type of systems.\nThe fixed systems performance was one order of magnitude better in the angle calculations while their range performance where matched.\nThe angle accuracy of the fixed system was dominantly less than one degree while it was around five degrees for the helmet mounted one.\nThe range accuracy was around 5 percent for both of the systems.\nThe problem with this and similar centralized systems is the need of the one or handful of microphone arrays to be in line-of-sight of the shooter.\nA sensor networked based solution has the advantage of widely distributed sensing for better coverage, multipath effect compensation and multiple simultaneous shot resolution [8].\nThis is especially important for operation in acoustically reverberant urban areas.\nNote that BBN's current vehicle-mounted system called BOOMERANG, a modified version of Bullet Ears, is currently used in Iraq [1].\nThe company ShotSpotter specializes in law enforcement systems that report the location of gunfire to police within seconds.\nThe goal of the system is significantly different than that of military systems.\nShotspotter reports 25 m typical accuracy which is more than enough for police to\nrespond.\nThey are also manufacturing experimental soldier wearable and UAV mounted systems for military use [16], but no specifications or evaluation results are publicly available.\n9.\nCONCLUSIONS\nThe main contribution of this work is twofold.\nFirst, the performance of the overall distributed networked system is excellent.\nMost noteworthy are the trajectory accuracy of one degree, the correct caliber estimation rate of well over 90% and the close to 100% weapon classification rate for 4 of the 6 weapons tested.\nThe system proved to be very robust when increasing the node location and orientation errors and decreasing the number of available sensors all the way down to a couple.\nThe key factor behind this is the sensor fusion algorithm's ability to reject erroneous measurements.\nIt is also worth mentioning that the results presented here correspond to the first and only test of the system beyond 100 m and with six different weapons.\nSecond, the performance of the system when used in standalone mode, that is, when single sensors alone provided localization, was also very good.\nWhile the overall localization rate of 42% per sensor for shots up to 130 m could be improved, the bearing accuracy of less than a degree and the average 5% range error are remarkable using the handmade prototypes of the low-cost nodes.\nNote that 87% of the shots were successfully localized by at least one of the ten sensors utilized in standalone mode.\nWe believe that the technology is mature enough that a next revision of the system could be a commercial one.\nHowever, important aspects of the system would still need to be worked on.\nA current node runs on 4 AA batteries for about 12 hours of continuous operation.\nA deployable version of the sensor node would need to be asleep during normal operation and only wake up when an interesting event occurs.\nAn analog trigger circuit could solve this problem, however, the system would miss the first shot.\nInstead, the acoustic channels would need to be sampled and stored in a circular buffer.\nThe rest of the board could be turned off.\nWhen a trigger wakes up the board, the acoustic data would be immediately available.\nExperiments with a previous generation sensor board indicated that this could provide a 10x increase in battery life."}}},{"rowIdx":95,"cells":{"id":{"kind":"string","value":"J-41"},"title":{"kind":"string","value":"An Analysis of Alternative Slot Auction Designs for Sponsored Search"},"abstract":{"kind":"string","value":"Billions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for placement alongside web search results. Slots for ad listings are allocated via an auction-style mechanism where the higher a merchant bids, the more likely his ad is to appear above other ads on the page. In this paper we analyze the incentive, efficiency, and revenue properties of two slot auction designs: rank by bid (RBB) and rank by revenue (RBR), which correspond to stylized versions of the mechanisms currently used by Yahoo! and Google, respectively. We also consider first- and second-price payment rules together with each of these allocation rules, as both have been used historically. We consider both the short-run incomplete information setting and the long-run complete information setting. With incomplete information, neither RBB nor RBR are truthful with either first or second pricing. We find that the informational requirements of RBB are much weaker than those of RBR, but that RBR is efficient whereas RBB is not. We also show that no revenue ranking of RBB and RBR is possible given an arbitrary distribution over bidder values and relevance. With complete information, we find that no equilibrium exists with first pricing using either RBB or RBR. We show that there typically exists a multitude of equilibria with second pricing, and we bound the divergence of (economic) value in such equilibria from the value obtained assuming all merchants bid truthfully."},"keyphrases":{"kind":"list like","value":["altern slot auction design","sponsor search","sponsor search","ad list","rank by bid","rank by revenu","incomplet inform","second price","auction-style mechan","second-price payment rule","equilibrium multitud","diverg of econom valu","diverg of valu","combin market capit","resurg onlin advertis industri","web search engin","pai per click","search engin","slot alloc","auction theori"],"string":"[\n \"altern slot auction design\",\n \"sponsor search\",\n \"sponsor search\",\n \"ad list\",\n \"rank by bid\",\n \"rank by revenu\",\n \"incomplet inform\",\n \"second price\",\n \"auction-style mechan\",\n \"second-price payment rule\",\n \"equilibrium multitud\",\n \"diverg of econom valu\",\n \"diverg of valu\",\n \"combin market capit\",\n \"resurg onlin advertis industri\",\n \"web search engin\",\n \"pai per click\",\n \"search engin\",\n \"slot alloc\",\n \"auction theori\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","P","P","P","P","P","M","M","R","R","R","U","M","M","U","M","R","M"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"M\",\n \"M\",\n \"R\",\n \"R\",\n \"R\",\n \"U\",\n \"M\",\n \"M\",\n \"U\",\n \"M\",\n \"R\",\n \"M\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"An Analysis of Alternative Slot Auction Designs for Sponsored Search S´ebastien Lahaie ∗ Division of Engineering and Applied Sciences Harvard University, Cambridge, MA 02138 slahaie@eecs.harvard.edu ABSTRACT Billions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for placement alongside web search results.\nSlots for ad listings are allocated via an auction-style mechanism where the higher a merchant bids, the more likely his ad is to appear above other ads on the page.\nIn this paper we analyze the incentive, efficiency, and revenue properties of two slot auction designs: rank by bid (RBB) and rank by revenue (RBR), which correspond to stylized versions of the mechanisms currently used by Yahoo! and Google, respectively.\nWe also consider first- and second-price payment rules together with each of these allocation rules, as both have been used historically.\nWe consider both the short-run incomplete information setting and the long-run complete information setting.\nWith incomplete information, neither RBB nor RBR are truthful with either first or second pricing.\nWe find that the informational requirements of RBB are much weaker than those of RBR, but that RBR is efficient whereas RBB is not.\nWe also show that no revenue ranking of RBB and RBR is possible given an arbitrary distribution over bidder values and relevance.\nWith complete information, we find that no equilibrium exists with first pricing using either RBB or RBR.\nWe show that there typically exists a multitude of equilibria with second pricing, and we bound the divergence of (economic) value in such equilibria from the value obtained assuming all merchants bid truthfully.\nCategories and Subject Descriptors J.4 [Computer Applications]: Social and Behavioral Sciences-Economics General Terms Economics, Theory 1.\nINTRODUCTION Today, Internet giants Google and Yahoo! boast a combined market capitalization of over $150 billion, largely on the strength of sponsored search, the fastest growing component of a resurgent online advertising industry.\nPricewaterhouseCoopers estimates that 2004 industry-wide sponsored search revenues were $3.9 billion, or 40% of total Internet advertising revenues.1 Industry watchers expect 2005 revenues to reach or exceed $7 billion.2 Roughly 80% of Google``s estimated $4 billion in 2005 revenue and roughly 45% of Yahoo!``s estimated $3.7 billion in 2005 revenue will likely be attributable to sponsored search.3 A number of other companies-including LookSmart, FindWhat, InterActiveCorp (Ask Jeeves), and eBay (Shopping.com)-earn hundreds of millions of dollars of sponsored search revenue annually.\nSponsored search is a form of advertising where merchants pay to appear alongside web search results.\nFor example, when a user searches for used honda accord san diego in a web search engine, a variety of commercial entities (San Diego car dealers, Honda Corp, automobile information portals, classified ad aggregators, eBay, etc..\n.)\nmay bid to to have their listings featured alongside the standard algorithmic search listings.\nAdvertisers bid for placement on the page in an auction-style format where the higher they bid the more likely their listing will appear above other ads on the page.\nBy convention, sponsored search advertisers generally pay per click, meaning that they pay only when a user clicks on their ad, and do not pay if their ad is displayed but not clicked.\nThough many people claim to systematically ignore sponsored search ads, Majestic Research reports that 1 www.iab.net/resources/adrevenue/pdf/IAB PwC 2004full.pdf 2 battellemedia.com/archives/002032.php 3 These are rough back of the envelope estimates.\nGoogle and Yahoo! 2005 revenue estimates were obtained from Yahoo! Finance.\nWe assumed $7 billion in 2005 industry-wide sponsored search revenues.\nWe used Nielsen/NetRatings estimates of search engine market share in the US, the most monetized market: wired-vig.\nwired.com/news/technology/0,1282,69291,00.html Using comScore``s international search engine market share estimates would yield different estimates: www.comscore.com/press/release.asp?press=622 218 as many as 17% of Google searches result in a paid click, and that Google earns roughly nine cents on average for every search query they process.4 Usually, sponsored search results appear in a separate section of the page designated as sponsored above or to the right of the algorithmic results.\nSponsored search results are displayed in a format similar to algorithmic results: as a list of items each containing a title, a text description, and a hyperlink to a corresponding web page.\nWe call each position in the list a slot.\nGenerally, advertisements that appear in a higher ranked slot (higher on the page) garner more attention and more clicks from users.\nThus, all else being equal, merchants generally prefer higher ranked slots to lower ranked slots.\nMerchants bid for placement next to particular search queries; for example, Orbitz and Travelocity may bid for las vegas hotel while Dell and HP bid for laptop computer.\nAs mentioned, bids are expressed as a maximum willingness to pay per click.\nFor example, a forty-cent bid by HostRocket for web hosting means HostRocket is willing to pay up to forty cents every time a user clicks on their ad.5 The auctioneer (the search engine6 ) evaluates the bids and allocates slots to advertisers.\nIn principle, the allocation decision can be altered with each new incoming search query, so in effect new auctions clear continuously over time as search queries arrive.\nMany allocation rules are plausible.\nIn this paper, we investigate two allocation rules, roughly corresponding to the two allocation rules used by Yahoo! and Google.\nThe rank by bid (RBB) allocation assigns slots in order of bids, with higher ranked slots going to higher bidders.\nThe rank by revenue (RBR) allocation assigns slots in order of the product of bid times expected relevance, where relevance is the proportion of users that click on the merchant``s ad after viewing it.\nIn our model, we assume that an ad``s expected relevance is known to the auctioneer and the advertiser (but not necessarily to other advertisers), and that clickthrough rate decays monotonically with lower ranked slots.\nIn practice, the expected clickthrough rate depends on a number of factors, including the position on the page, the ad text (which in turn depends on the identity of the bidder), the nature and intent of the user, and the context of other ads and algorithmic results on the page, and must be learned over time by both the auctioneer and the bidder [13].\nAs of this writing, to a rough first-order approximation, Yahoo! employs a RBB allocation and Google employs a RBR allocation, though numerous caveats apply in both cases when it comes to the vagaries of real-world implementations.7 Even when examining a one-shot version of a slot auction, the mechanism differs from a standard multi-item auc4 battellemedia.com/archives/001102.php 5 Usually advertisers also set daily or monthly budget caps; in this paper we do not model budget constraints.\n6 In the sponsored search industry, the auctioneer and search engine are not always the same entity.\nFor example Google runs the sponsored search ads for AOL web search, with revenue being shared.\nSimilarly, Yahoo! currently runs the sponsored search ads for MSN web search, though Microsoft will begin independent operations soon.\n7 Here are two among many exceptions to the Yahoo! = RBB and Google = RBR assertion: (1) Yahoo! excludes ads deemed insufficiently relevant either by a human editor or due to poor historical click rate; (2) Google sets differing reserve prices depending on Google``s estimate of ad quality.\ntion in subtle ways.\nFirst, a single bid per merchant is used to allocate multiple non-identical slots.\nSecond, the bid is communicated not as a direct preference over slots, but as a preference for clicks that depend stochastically on slot allocation.\nWe investigate a number of economic properties of RBB and RBR slot auctions.\nWe consider the short-run incomplete information case in Section 3, adapting and extending standard analyses of single-item auctions.\nIn Section 4 we turn to the long-run complete information case; our characterization results here draw on techniques from linear programming.\nThroughout, important observations are highlighted as claims supported by examples.\nOur contributions are as follows: • We show that with multiple slots, bidders do not reveal their true values with either RBB or RBR, and with either first- or second-pricing.\n• With incomplete information, we find that the informational requirements of playing the equilibrium bid are much weaker for RBB than for RBR, because bidders need not know any information about each others'' relevance (or even their own) with RBB.\n• With incomplete information, we prove that RBR is efficient but that RBB is not.\n• We show via a simple example that no general revenue ranking of RBB and RBR is possible.\n• We prove that in a complete-information setting, firstprice slot auctions have no pure strategy Nash equilibrium, but that there always exists a pure-strategy equilibrium with second pricing.\n• We provide a constant-factor bound on the deviation from efficiency that can occur in the equilibrium of a second-price slot auction.\nIn Section 2 we specify our model of bidders and the various slot auction formats.\nIn Section 3.1 we study the incentive properties of each format, asking in which cases agents would bid truthfully.\nThere is possible confusion here because the second-price design for slot auctions is reminiscent of the Vickrey auction for a single item; we note that for slot auctions the Vickrey mechanism is in fact very different from the second-price mechanism, and so they have different incentive properties.8 In Section 3.2 we derive the Bayes-Nash equilibrium bids for the various auction formats.\nThis is useful for the efficiency and revenue results in later sections.\nIt should become clear in this section that slot auctions in our model are a straightforward generalization of single-item auctions.\nSections 3.3 and 3.4 address questions of efficiency and revenue under incomplete information, respectively.\nIn Section 4.1 we determine whether pure-strategy equilibria exist for the various auction formats, under complete information.\nIn Section 4.2 we derive bounds on the deviation from efficiency in the pure-strategy equilibria of secondprice slot auctions.\nOur approach is positive rather than normative.\nWe aim to clarify the incentive, efficiency, and revenue properties of two slot auction designs currently in use, under settings of 8 Other authors have also made this observation [5, 6].\n219 incomplete and complete information.\nWe do not attempt to derive the optimal mechanism for a slot auction.\nRelated work.\nFeng et al. [7] compare the revenue performance of various ranking mechanisms for slot auctions in a model with incomplete information, much as we do in Section 3.4, but they obtain their results via simulations whereas we perform an equilibrium analysis.\nLiu and Chen [12] study properties of slot auctions under incomplete information.\nTheir setting is essentially the same as ours, except they restrict their attention to a model with a single slot and a binary type for bidder relevance (high or low).\nThey find that RBR is efficient, but that no general revenue ranking of RBB and RBR is possible, which agrees with our results.\nThey also take a design approach and show how the auctioneer should assign relevance scores to optimize its revenue.\nEdelman et al. [6] model the slot auction problem both as a static game of complete information and a dynamic game of incomplete information.\nThey study the locally envyfree equilibria of the static game of complete information; this is a solution concept motivated by certain bidding behaviors that arise due to the presence of budget constraints.\nThey do not view slot auctions as static games of incomplete information as we do, but do study them as dynamic games of incomplete information and derive results on the uniqueness and revenue properties of the resulting equilibria.\nThey also provide a nice description of the evolution of the market for sponsored search.\nVarian [18] also studies slot auctions under a setting of complete information.\nHe focuses on symmetric equilibria, which are a refinement of Nash equilibria appropriate for slot auctions.\nHe provides bounds on the revenue obtained in equilibrium.\nHe also gives bounds that can be used to infer bidder values given their bids, and performs some empirical analysis using these results.\nIn contrast, we focus instead on efficiency and provide bounds on the deviation from efficiency in complete-information equilibria.\n2.\nPRELIMINARIES We focus on a slot auction for a single keyword.\nIn a setting of incomplete information, a bidder knows only distributions over others'' private information (value per click and relevance).\nWith complete information, a bidder knows others'' private information, and so does not need to rely on distributions to strategize.\nWe first describe the model for the case with incomplete information, and drop the distributional information from the model when we come to the complete-information case in Section 4.\n2.1 The Model There is a fixed number K of slots to be allocated among N bidders.\nWe assume without loss of generality that K ≤ N, since superfluous slots can remain blank.\nBidder i assigns a value of Xi to each click received on its advertisement, regardless of this advertisement``s rank.9 The probability that i``s advertisement will be clicked if viewed is Ai ∈ [0, 1].\nWe refer to Ai as bidder i``s relevance.\nWe refer to Ri = AiXi as bidder i``s revenue.\nThe Xi, Ai, and Ri are random 9 Indeed Kitts et al. [10] find that in their sample of actual click data, the correlation between rank and conversion rate is not statistically significant.\nHowever, for the purposes of our model it is also important that bidders believe that conversion rate does not vary with rank.\nvariables and we denote their realizations by xi, αi, and ri respectively.\nThe probability that an advertisement will be viewed if placed in slot j is γj ∈ [0, 1].\nWe assume γ1 > γ2 > ... > γK.\nHence bidder i``s advertisement will have a clickthrough rate of γjαi if placed in slot j. Of course, an advertisement does not receive any clicks if it is not allocated a slot.\nEach bidder``s value and relevance pair (Xi, Ai) is independently and identically distributed on [0, ¯x] × [0, 1] according to a continuous density function f that has full support on its domain.\nThe density f and slot probabilities γ1, ... , γK are common knowledge.\nOnly bidder i knows the realization xi of its value per click Xi.\nBoth bidder i and the seller know the realization αi of Ai, but this realization remains unobservable to the other bidders.\nWe assume that bidders have quasi-linear utility functions.\nThat is, the expected utility to bidder i of obtaining the slot of rank j at a price of b per click is ui(j, b) = γjαi(xi − b) If the advertising firms bidding in the slot auction are riskneutral and have ample liquidity, quasi-linearity is a reasonable assumption.\nThe assumptions of independence, symmetry, and riskneutrality made above are all quite standard in single-item auction theory [11, 19].\nThe assumption that clickthrough rate decays monotonically with lower slots-by the same factors for each agent-is unique to the slot auction problem.\nWe view it as a main contribution of our work to show that this assumption allows for tractable analysis of the slot auction problem using standard tools from singleitem auction theory.\nIt also allows for interesting results in the complete information case.\nA common model of decaying clickthrough rate is the exponential decay model, where γk = 1 δk−1 with decay δ > 1.\nFeng et al. [7] state that their actual clickthrough data is fitted extremely well by an exponential decay model with δ = 1.428.\nOur model lacks budget constraints, which are an important feature of real slot auctions.\nWith budget constraints keyword auctions cannot be considered independently of one another, because the budget must be allocated across multiple keywords-a single advertiser typically bids on multiple keywords relevant to his business.\nIntroducing this element into the model is an important next step for future work.10 2.2 Auction Formats In a slot auction a bidder provides to the seller a declared value per click ˜xi(xi, αi) which depends on his true value and relevance.\nWe often denote this declared value (bid) by ˜xi for short.\nSince a bidder``s relevance αi is observable to the seller, the bidder cannot misrepresent it.\nWe denote the kth highest of the N declared values by ˜x(k) , and the kth highest of the N declared revenues by ˜r(k) , where the declared revenue of bidder i is ˜ri = αi ˜xi.\nWe consider two types of allocation rules, rank by bid (RBB) and rank by revenue (RBR): 10 Models with budget constraints have begun to appear in this research area.\nAbrams [1] and Borgs et al. [3] design multi-unit auctions for budget-constrained bidders, which can be interpreted as slot auctions, with a focus on revenue optimization and truthfulness.\nMehta et al. [14] address the problem of matching user queries to budget-constrained advertisers so as to maximize revenue.\n220 RBB.\nSlot k goes to bidder i if and only if ˜xi = ˜x(k) .\nRBR.\nSlot k goes to bidder i if and only if ˜ri = ˜r(k) .\nWe will commonly represent an allocation by a one-to-one function σ : [K] → [N], where [n] is the set of integers {1, 2, ... , n}.\nHence slot k goes to bidder σ(k).\nWe also consider two different types of payment rules.\nNote that no matter what the payment rule, a bidder that is not allocated a slot will pay 0 since his listing cannot receive any clicks.\nFirst-price.\nThe bidder allocated slot k, namely σ(k), pays ˜xσ(k) per click under both the RBB and RBR allocation rules.\nSecond-price.\nIf k < N, bidder σ(k) pays ˜xσ(k+1) per click under the RBB rule, and pays ˜rσ(k+1)/ασ(k) per click under the RBR rule.\nIf k = N, bidder σ(k) pays 0 per click.11 Intuitively, a second-price payment rule sets a bidder``s payment to the lowest bid it could have declared while maintaining the same ranking, given the allocation rule used.\nOverture introduced the first slot auction design in 1997, using a first-price RBB scheme.\nGoogle then followed in 2000 with a second-price RBR scheme.\nIn 2002, Overture (at this point acquired by Yahoo!) then switched to second pricing but still allocates using RBB.\nOne possible reason for the switch is given in Section 4.\nWe assume that ties are broken as follows in the event that two agents make the exact same bid or declare the same revenue.\nThere is a permutation of the agents κ : [N] → [N] that is fixed beforehand.\nIf the bids of agents i and j are tied, then agent i obtains a higher slot if and only if κ(i) < κ(j).\nThis is consistent with the practice in real slot auctions where ties are broken by the bidders'' order of arrival.\n3.\nINCOMPLETE INFORMATION 3.1 Incentives It should be clear that with a first-price payment rule, truthful bidding is neither a dominant strategy nor an ex post Nash equilibrium using either RBB or RBR, because this guarantees a payoff of 0.\nThere is always an incentive to shade true values with first pricing.\nThe second-price payment rule is reminiscent of the secondprice (Vickrey) auction used for selling a single item, and in a Vickrey auction it is a dominant strategy for a bidder to reveal his true value for the item [19].\nHowever, using a second-price rule in a slot auction together with either allocation rule above does not yield an incentive-compatible mechanism, either in dominant strategies or ex post Nash equilibrium.12 With a second-price rule there is no incentive for a bidder to bid higher than his true value per click using either RBB or RBR: this either leads to no change 11 We are effectively assuming a reserve price of zero, but in practice search engines charge a non-zero reserve price per click.\n12 Unless of course there is only a single slot available, since this is the single-item case.\nWith a single slot both RBB and RBR with a second-price payment rule are dominantstrategy incentive-compatible.\nin the outcome, or a situation in which he will have to pay more than his value per click for each click received, resulting in a negative payoff.13 However, with either allocation rule there may be an incentive to shade true values with second pricing.\nClaim 1.\nWith second pricing and K ≥ 2, truthful bidding is not a dominant strategy nor an ex post Nash equilibrium for either RBB or RBR.\nExample.\nThere are two agents and two slots.\nThe agents have relevance α1 = α2 = 1, whereas γ1 = 1 and γ2 = 1/2.\nAgent 1 has a value of x1 = 6 per click, and agent 2 has a value of x2 = 4 per click.\nLet us first consider the RBB rule.\nSuppose agent 2 bids truthfully.\nIf agent 1 also bids truthfully, he wins the first slot and obtains a payoff of 2.\nHowever, if he shades his bid down below 4, he obtains the second slot at a cost of 0 per click yielding a payoff of 3.\nSince the agents have equal relevance, the exact same situation holds with the RBR rule.\nHence truthful bidding is not a dominant strategy in either format, and neither is it an ex post Nash equilibrium.\nTo find payments that make RBB and RBR dominantstrategy incentive-compatible, we can apply Holmstrom``s lemma [9] (see also chapter 3 in Milgrom [15]).\nUnder the restriction that a bidder with value 0 per click does not pay anything (even if he obtains a slot, which can occur if there are as many slots as bidders), this lemma implies that there is a unique payment rule that achieves dominant-strategy incentive compatibility for either allocation rule.\nFor RBB, the bidder allocated slot k is charged per click KX i=k+1 (γi−1 − γi)˜x(i) + γK ˜x(K+1) (1) Note that if K = N, ˜x(K+1) = 0 since there is no K + 1th bidder.\nFor RBR, the bidder allocated slot k is charged per click 1 ασ(k) KX i=k+1 (γi−1 − γi)˜r(i) + γK ˜r(K+1) !\n(2) Using payment rule (2) and RBR, the auctioneer is aware of the true revenues of the bidders (since they reveal their values truthfully), and hence ranks them according to their true revenues.\nWe show in Section 3.3 that this allocation is in fact efficient.\nSince the VCG mechanism is the unique mechanism that is efficient, truthful, and ensures bidders with value 0 pay nothing (by the Green-Laffont theorem [8]), the RBR rule and payment scheme (2) constitute exactly the VCG mechanism.\nIn the VCG mechanism an agent pays the externality he imposes on others.\nTo understand payment (2) in this sense, note that the first term is the added utility (due to an increased clickthrough rate) agents in slots k + 1 to K would receive if they were all to move up a slot; the last term is the utility that the agent with the K +1st revenue would receive by obtaining the last slot as opposed to nothing.\nThe leading coefficient simply reduces the agent``s expected payment to a payment per click.\n13 In a dynamic setting with second pricing, there may be an incentive to bid higher than one``s true value in order to exhaust competitors'' budgets.\nThis phenomenon is commonly called bid jamming or antisocial bidding [4].\n221 3.2 Equilibrium Analysis To understand the efficiency and revenue properties of the various auction formats, we must first understand which rankings of the bidders occur in equilibrium with different allocation and payment rule combinations.\nThe following lemma essentially follows from the Monotonic Selection Theorem by Milgrom and Shannon [16].\nLemma 1.\nIn a RBB (RBR) auction with either a firstor second-price payment rule, the symmetric Bayes-Nash equilibrium bid is strictly increasing with value ( revenue).\nAs a consequence of this lemma, we find that RBB and RBR auctions allocate the slots greedily by the true values and revenues of the agents, respectively (whether using firstor second-price payment rules).\nThis will be relevant in Section 3.3 below.\nFor a first-price payment rule, we can explicitly derive the symmetric Bayes-Nash equilibrium bid functions for RBB and RBR auctions.\nThe purpose of this exercise is to lend qualitative insights into the parameters that influence an agent``s bidding, and to derive formulae for the expected revenue in RBB and RBR auctions in order to make a revenue ranking of these two allocation rules (in Section 3.4).\nLet G(y) be the expected resulting clickthrough rate, in a symmetric equilibrium of the RBB auction (with either payment rule), to a bidder with value y and relevance α = 1.\nLet H(y) be the analogous quantity for a bidder with revenue y and relevance 1 in a RBR auction.\nBy Lemma 1, a bidder with value y will obtain slot k in a RBB auction if y is the kth highest of the true realized values.\nThe same applies in a RBR auction when y is the kth highest of the true realized revenues.\nLet FX (y) be the distribution function for value, and let FR(y) be the distribution function for revenue.\nThe probability that y is the kth highest out of N values is N − 1 k − 1 !\n(1 − FX (y))k−1 FX (y)N−k whereas the probability that y is the kth highest out of N revenues is the same formula with FR replacing FX .\nHence we have G(y) = KX k=1 γk N − 1 k − 1 !\n(1 − FX (y))k−1 FX (y)N−k The H function is analogous to G with FR replacing FX .\nIn the two propositions that follow, g and h are the derivatives of G and H respectively.\nWe omit the proof of the next proposition, because it is almost identical to the derivation of the equilibrium bid in the single-item case (see Krishna [11], Proposition 2.2).\nProposition 1.\nThe symmetric Bayes-Nash equilibrium strategies in a first-price RBB auction are given by ˜xB (x, α) = 1 G(x) Z x 0 y g(y)dy The first-price equilibrium above closely parallels the firstprice equilibrium in the single-item model.\nWith a single item g is the density of the second highest value among all N agent values, whereas in a slot auction it is a weighted combination of the densities for the second, third, etc..\nhighest values.\nNote that the symmetric Bayes-Nash equilibrium bid in a first-price RBB auction does not depend on a bidder``s relevance α.\nTo see clearly why, note that a bidder chooses a bid b so as to maximize the objective αG(˜x−1 (b))(x − b) and here α is just a leading constant factor.\nSo dropping it does not change the set of optimal solutions.\nHence the equilibrium bid depends only on the value x and function G, and G in turn depends only on the marginal cumulative distribution of value FX .\nSo really only the latter needs to be common knowledge to the bidders.\nOn the other hand, we will now see that information about relevance is needed for bidders to play the equilibrium in the first-price RBR auction.\nSo the informational requirements for a first-price RBB auction are much weaker than for a first-price RBR auction: in the RBB auction a bidder need not know his own relevance, and need not know any distributional information over others'' relevance in order to play the equilibrium.\nAgain we omit the next proposition``s proof since it is so similar to the one above.\nProposition 2.\nThe symmetric Bayes-Nash equilibrium strategies in a first-price RBR auction are given by ˜xR (x, α) = 1 αH(αx) Z αx 0 y h(y) dy Here it can be seen that the equilibrium bid is increasing with x, but not necessarily with α.\nThis should not be much of a concern to the auctioneer, however, because in any case the declared revenue in equilibrium is always increasing in the true revenue.\nIt would be interesting to obtain the equilibrium bids when using a second-price payment rule, but it appears that the resulting differential equations for this case do not have a neat analytical solution.\nNonetheless, the same conclusions about the informational requirements of the RBB and RBR rules still hold, as can be seen simply by inspecting the objective function associated with an agent``s bidding problem for the second-price case.\n3.3 Efficiency A slot auction is efficient if in equilibrium the sum of the bidders'' revenues from their allocated slots is maximized.\nUsing symmetry as our equilibrium selection criterion, we find that the RBB auction is not efficient with either payment rule.\nClaim 2.\nThe RBB auction is not efficient with either first or second pricing.\nExample.\nThere are two agents and one slot, with γ1 = 1.\nAgent 1 has a value of x1 = 6 per click and relevance α1 = 1/2.\nAgent 2 has a value of x2 = 4 per click and relevance α2 = 1.\nBy Lemma 1, agents are ranked greedily by value.\nHence agent 1 obtains the lone slot, for a total revenue of 3 to the agents.\nHowever, it is most efficient to allocate the slot to agent 2, for a total revenue of 4.\nExamples with more agents or more slots are simple to construct along the same lines.\nOn the other hand, under our assumptions on how clickthrough rate decreases with lower rank, the RBR auction is efficient with either payment rule.\n222 Theorem 1.\nThe RBR auction is efficient with either first- or second-price payments rules.\nProof.\nSince by Lemma 1 the agents'' equilibrium bids are increasing functions of their revenues in the RBR auction, slots are allocated greedily according to true revenues.\nLet σ be a non-greedy allocation.\nThen there are slots s, t with s < t and rσ(s) < rσ(t).\nWe can switch the agents in slots s and t to obtain a new allocation, and the difference between the total revenue in this new allocation and the original allocation``s total revenue is ` γtrσ(s) + γsrσ(t) ´ − ` γsrσ(s) + γtrσ(t) ´ = (γs − γt) ` rσ(t) − rσ(s) ´ Both parenthesized terms above are positive.\nHence the switch has increased the total revenue to the bidders.\nIf we continue to perform such switches, we will eventually reach a greedy allocation of greater revenue than the initial allocation.\nSince the initial allocation was arbitrary, it follows that a greedy allocation is always efficient, and hence the RBR auction``s allocation is efficient.\nNote that the assumption that clickthrough rate decays montonically by the same factors γ1, ... , γK for all agents is crucial to this result.\nA greedy allocation scheme does not necessarily find an efficient solution if the clickthrough rates are monotonically decreasing in an independent fashion for each agent.\n3.4 Revenue To obtain possible revenue rankings for the different auction formats, we first note that when the allocation rule is fixed to RBB, then using either a first-price, second-price, or truthful payment rule leads to the same expected revenue in a symmetric, increasing Bayes-Nash equilibrium.\nBecause a RBB auction ranks agents by their true values in equilibrium for any of these payment rules (by Lemma 1), it follows that expected revenue is the same for all these payment rules, following arguments that are virtually identical to those used to establish revenue equivalence in the singleitem case (see e.g. Proposition 3.1 in Krishna [11]).\nThe same holds for RBR auctions; however, the revenue ranking of the RBB and RBR allocation rules is still unclear.\nBecause of this revenue equivalence principle, we can choose whichever payment rule is most convenient for the purpose of making revenue comparisons.\nUsing Propositions 1 and 2, it is a simple matter to derive formulae for the expected revenue under both allocation rules.\nThe payment of an agent in a RBB auction is mB (x, α) = αG(x)˜xV (x, α) The expected revenue is then N · E ˆ mV (X, A) ˜ , where the expectation is taken with respect to the joint density of value and relevance.\nThe expected revenue formula for RBR auctions is entirely analogous using ˜xR (x, α) and the H function.\nWith these in hand we can obtain revenue rankings for specific numbers of bidders and slots, and specific distributions over values and relevance.\nClaim 3.\nFor fixed K, N, and fixed γ1, ... , γK, no revenue ranking of RBB and RBR is possible for an arbitrary density f. Example.\nAssume there are 2 bidders, 2 slots, and that γ1 = 1, γ2 = 1/2.\nAssume that value-relevance pairs are uniformly distributed over [0, 1]× [0, 1].\nFor such a distribution with a closed-form formula, it is most convenient to use the revenue formulae just derived.\nRBB dominates RBR in terms of revenue for these parameters.\nThe formula for the expected revenue in a RBB auction yields 1/12, whereas for RBR auctions we have 7/108.\nAssume instead that with probability 1/2 an agent``s valuerelevance pair is (1, 1/2), and that with probability 1/2 it is (1/2, 1).\nIn this scenario it is more convenient to appeal to formulae (1) and (2).\nIn a truthful auction the second agent will always pay 0.\nAccording to (1), in a truthful RBB auction the first agent makes an expected payment of E ˆ (γ1 − γ2)Aσ(1)Xσ(2) ˜ = 1 2 E ˆ Aσ(1) ˜ E ˆ Xσ(2) ˜ where we have used the fact that value and relevance are independently distributed for different agents.\nThe expected relevance of the agent with the highest value is E ˆ Aσ(1) ˜ = 5/8.\nThe expected second highest value is also E ˆ Xσ(2) ˜ = 5/8.\nThe expected revenue for a RBB auction here is then 25/128.\nAccording to (2), in a truthful RBR auction the first agent makes an expected payment of E ˆ (γ1 − γ2)Rσ(2) ˜ = 1 2 E ˆ Rσ(2) ˜ In expectation the second highest revenue is E ˆ Rσ(2) ˜ = 1/2, so the expected revenue for a RBR auction is 1/4.\nHence in this case the RBR auction yields higher expected revenue.1415 This example suggests the following conjecture: when value and relevance are either uncorrelated or positively correlated, RBB dominates RBR in terms of revenue.\nWhen value and relevance are negatively correlated, RBR dominates.\n4.\nCOMPLETE INFORMATION In typical slot auctions such as those run by Yahoo! and Google, bidders can adjust their bids up or down at any time.\nAs B¨orgers et al. [2] and Edelman et al. [6] have noted, this can be viewed as a continuous-time process in which bidders learn each other``s bids.\nIf the process stabilizes the result can then be modeled as a Nash equilibrium in pure strategies of the static one-shot game of complete information, since each bidder will be playing a best-response to the others'' bids.16 This argument seems especially appropriate for Yahoo!``s slot auction design where all bids are 14 To be entirely rigorous and consistent with our initial assumptions, we should have constructed a continuous probability density with full support over an appropriate domain.\nTaking the domain to be e.g. [0, 1] × [0, 1] and a continuous density with full support that is sufficiently concentrated around (1, 1/2) and (1/2, 1), with roughly equal mass around both, would yield the same conclusion.\n15 Claim 3 should serve as a word of caution, because Feng et al. [7] find through their simulations that with a bivariate normal distribution over value-relevance pairs, and with 5 slots, 15 bidders, and δ = 2, RBR dominates RBB in terms of revenue for any level of correlation between value and relevance.\nHowever, they assume that bidding behavior in a second-price slot auction can be well approximated by truthful bidding.\n16 We do not claim that bidders will actually learn each others'' private information (value and relevance), just that for a stable set of bids there is a corresponding equilibrium of the complete information game.\n223 made public.\nGoogle keeps bids private, but experimentation can allow one to discover other bids, especially since second pricing automatically reveals to an agent the bid of the agent ranked directly below him.\n4.1 Equilibrium Analysis In this section we ask whether a pure-strategy Nash equilibrium exists in a RBB or RBR slot auction, with either first or second pricing.\nBefore dealing with the first-price case there is a technical issue involving ties.\nIn our model we allow bids to be nonnegative real numbers for mathematical convenience, but this can become problematic because there is then no bid that is just higher than another.\nWe brush over such issues by assuming that an agent can bid infinitesimally higher than another.\nThis is imprecise but allows us to focus on the intuition behind the result that follows.\nSee Reny [17] for a full treatment of such issues.\nFor the remainder of the paper, we assume that there are as many slots as bidders.\nThe following result shows that there can be no pure-strategy Nash equilibrium with first pricing.17 Note that the argument holds for both RBB and RBR allocation rules.\nFor RBB, bids should be interpreted as declared values, and for RBR as declared revenues.\nTheorem 2.\nThere exists no complete information Nash equilibrium in pure strategies in the first-price slot auction, for any possible values of the agents, whether using a RBB or RBR allocation rule.\nProof.\nLet σ : [K] → [N] be the allocation of slots to the agents resulting from their bids.\nLet ri and bi be the revenue and bid of the agent ranked ith , respectively.\nNote that we cannot have bi > bi+1, or else the agent in slot i can make a profitable deviation by instead bidding bi − > bi+1 for small enough > 0.\nThis does not change its allocation, but increases its profit.\nHence we must have bi = bi+1 (i.e. with one bidder bidding infinitesimally higher than the other).\nSince this holds for any two consecutive bidders, it follows that in a Nash equilibrium all bidders must be bidding 0 (since the bidder ranked last matches the bid directly below him, which is 0 by default because there is no such bid).\nBut this is impossible: consider the bidder ranked last.\nThe identity of this bidder is always clear given the deterministic tie-breaking rule.\nThis bidder can obtain the top spot and increase his revenue by (γ1 −γK)rK > 0 by bidding some > 0, and for small enough this is necessarily a profitable deviation.\nHence there is no Nash equilibrium in pure strategies.\nOn the other hand, we find that in a second-price slot auction there can be a multitude of pure strategy Nash equilibria.\nThe next two lemmas give conditions that characterize the allocations that can occur as a result of an equilibrium profile of bids, given fixed agent values and revenues.\nThen if we can exhibit an allocation that satisfies these conditions, there must exist at least one equilibrium.\nWe first consider the RBR case.\n17 B¨orgers et al. [2] have proven this result in a model with three bidders and three slots, and we generalize their argument.\nEdelman et al. [6] also point out this non-existence phenomenon.\nThey only illustrate the fact with an example because the result is quite immediate.\nLemma 2.\nGiven an allocation σ, there exists a Nash equilibrium profile of bids b leading to σ in a second-price RBR slot auction if and only if „ 1 − γi γj+1 `` rσ(i) ≤ rσ(j) for 1 ≤ j ≤ N − 2 and i ≥ j + 2.\nProof.\nThere exists a desired vector b which constitutes a Nash equilibrium if and only if the following set of inequalities can be satisfied (the variables are the πi and bj): πi ≥ γj(rσ(i) − bj) ∀i, ∀j < i (3) πi ≥ γj(rσ(i) − bj+1) ∀i, ∀j > i (4) πi = γi(rσ(i) − bi+1) ∀i (5) bi ≥ bi+1 1 ≤ i ≤ N − 1 (6) πi ≥ 0, bi ≥ 0 ∀i Here rσ(i) is the revenue of the agent allocated slot i, and πi and bi may be interpreted as this agent``s surplus and declared revenue, respectively.\nWe first argue that constraints (6) can be removed, because the inequalities above can be satisfied if and only if the inequalities without (6) can be satisfied.\nThe necessary direction is immediate.\nAssume we have a vector (π, b) which satisfies all inequalities above except (6).\nThen there is some i for which bi < bi+1.\nConstruct a new vector (π, b ) identical to the original except with bi+1 = bi.\nWe now have bi = bi+1.\nAn agent in slot k < i sees the price of slot i decrease from bi+1 to bi+1 = bi, but this does not make i more preferred than k to this agent because we have πk ≥ γi−1(rσ(k) − bi) ≥ γi(rσ(k) − bi) = γi(rσ(k) −bi+1) (i.e. because the agent in slot k did not originally prefer slot i − 1 at price bi, he will not prefer slot i at price bi).\nA similar argument applies for agents in slots k > i + 1.\nThe agent in slot i sees the price of this slot go down, which only makes it more preferred.\nFinally, the agent in slot i + 1 sees no change in the price of any slot, so his slot remains most preferred.\nHence inequalities (3)-(5) remain valid at (π, b ).\nWe first make this change to the bi+1 where bi < bi+1 and index i is smallest.\nWe then recursively apply the change until we eventually obtain a vector that satisfies all inequalities.\nWe safely ignore inequalities (6) from now on.\nBy the Farkas lemma, the remaining inequalities can be satisfied if and only if there is no vector z such that X i,j (γj rσ(i)) zσ(i)j > 0 X i>j γjzσ(i)j + X i<j γj−1zσ(i)j−1 ≤ 0 ∀j (7) X j zσ(i)j ≤ 0 ∀i (8) zσ(i)j ≥ 0 ∀i, ∀j = i zσ(i)i free ∀i Note that a variable of the form zσ(i)i appears at most once in a constraint of type (8), so such a variable can never be positive.\nAlso, zσ(i)1 = 0 for all i = 1 by constraint (7), since such variables never appear with another of the form zσ(i)i.\nNow if we wish to raise zσ(i)j above 0 by one unit for j = i, we must lower zσ(i)i by one unit because of the constraint of type (8).\nBecause γjrσ(i) ≤ γirσ(i) for i < j, raising 224 zσ(i)j with i < j while adjusting other variables to maintain feasibility cannot make the objective P i,j(γjrσ(i))zσ(i)j positive.\nIf this objective is positive, then this is due to some component zσ(i)j with i > j being positive.\nNow for the constraints of type (7), if i > j then zσ(i)j appears with zσ(j−1)j−1 (for 1 < j < N).\nSo to raise the former variable γ−1 j units and maintain feasibility, we must (I) lower zσ(i)i by γ−1 j units, and (II) lower zσ(j−1)j−1 by γ−1 j−1 units.\nHence if the following inequalities hold: rσ(i) ≤ „ γi γj `` rσ(i) + rσ(j−1) (9) for 2 ≤ j ≤ N − 1 and i > j, raising some zσ(i)j with i > j cannot make the objective positive, and there is no z that satisfies all inequalities above.\nConversely, if some inequality (9) does not hold, the objective can be made positive by raising the corresponding zσ(i)j and adjusting other variables so that feasibility is just maintained.\nBy a slight reindexing, inequalities (9) yield the statement of the theorem.\nThe RBB case is entirely analogous.\nLemma 3.\nGiven an allocation σ, there exists a Nash equilibrium profile of bids b leading to σ in a second-price RBB slot auction if and only if „ 1 − γi γj+1 `` xσ(i) ≤ xσ(j) for 1 ≤ j ≤ N − 2 and i ≥ j + 2.\nProof Sketch.\nThe proof technique is the same as in the previous lemma.\nThe desired Nash equilibrium exists if and only if a related set of inequalities can be satisfied; by the Farkas lemma, this occurs if and only if an alternate set of inequalities cannot be satisfied.\nThe conditions that determine whether the latter holds are given in the statement of the lemma.\nThe two lemmas above immediately lead to the following result.\nTheorem 3.\nThere always exists a complete information Nash equilibrium in pure strategies in the second-price RBB slot auction.\nThere always exists an efficient complete information Nash equilibrium in pure strategies in the secondprice RBR slot auction.\nProof.\nFirst consider RBB.\nSuppose agents are ranked according to their true values.\nSince xσ(i) ≤ xσ(j) for i > j, the system of inequalities in Lemma 3 is satisfied, and the allocation is the result of some Nash equilibrium bid profile.\nBy the same type of argument but appealing to Lemma 2 for RBR, there exists a Nash equilibrium bid profile such that bidders are ranked according to their true revenues.\nBy Theorem 1, this latter allocation is efficient.\nThis theorem establishes existence but not uniqueness.\nIndeed we expect that in many cases there will be multiple allocations (and hence equilibria) which satisfy the conditions of Lemmas 2 and 3.\nIn particular, not all equilibria of a second-price RBR auction will be efficient.\nFor instance, according to Lemma 2, with two agents and two slots any allocation can arise in a RBR equilibrium because no constraints apply.\nTheorems 2 and 3 taken together provide a possible explanation for Yahoo!``s switch from first to second pricing.\nWe saw in Section 3.1 that this does not induce truthfulness from bidders.\nWith first pricing, there will always be some bidder that feels compelled to adjust his bid.\nSecond pricing is more convenient because an equilibrium can be reached, and this reduces the cost of bid management.\n4.2 Efficiency For a given allocation rule, we call the allocation that would result if the bidders reported their values truthfully the standard allocation.\nHence in the standard RBB allocation bidders are ranked by true values, and in the standard RBR allocation they are ranked by true revenues.\nAccording to Lemmas 2 and 3, a ranking that results from a Nash equilibrium profile can only deviate from the standard allocation by having agents with relatively similar values or revenues switch places.\nThat is, if ri > rj then with RBR agent j can be ranked higher than i only if the ratio rj/ri is sufficiently large; similarly for RBB.\nThis suggests that the value of an equilibrium allocation cannot differ too much from the value obtained in the standard allocation, and the following theorems confirms this.\nFor an allocation σ of slots to agents, we denote its total value by f(σ) = PN i=1 γirσ(i).\nWe denote by g(σ) = PN i=1 γixσ(i) allocation σ``s value when assuming all agents have identical relevance, normalized to 1.\nLet L = min i=1,...,N−1 min γi+1 γi , 1 − γi+2 γi+1 ff (where by default γN+1 = 0).\nLet ηx and ηr be the standard allocations when using RBB and RBR, respectively.\nTheorem 4.\nFor an allocation σ that results from a purestrategy Nash equilibrium of a second-price RBR slot auction, we have f(σ) ≥ Lf(ηr).\nProof.\nWe number the agents so that agent i has the ith highest revenue, so r1 ≥ r2 ≥ ... ≥ rN .\nHence the standard allocation has value f(ηr) = PN i=1 γiri.\nTo prove the theorem, we will make repeated use of the fact thatP k akP k bk ≥ mink ak bk when the ak and bk are positive.\nNote that according to Lemma 2, if agent i lies at least two slots below slot j, then rσ(j) ≥ ri 1 − γj+2 γj+1 .\nIt may be the case that for some slot i, we have σ(i) > i and for slots k > i + 1 we have σ(k) > i.\nWe then say that slot i is inverted.\nLet S be the set of agents with indices at least i + 1; there are N − i of these.\nIf slot i is inverted, it is occupied by some agent from S. Also all slots strictly lower than i + 1 must be occupied by the remaining agents from S, since σ(k) > i for k ≥ i + 2.\nThe agent in slot i + 1 must then have an index σ(i + 1) ≤ i (note this means slot i + 1 cannot be inverted).\nNow there are two cases.\nIn the first case we have σ(i) = i + 1.\nThen γirσ(i) + γi+1rσ(i+1) γiri + γi+1ri+1 ≥ γi+1ri + γiri+1 γiri + γi+1ri+1 ≥ min γi+1 γi , γi γi+1 ff = γi+1 γi In the second case we have σ(i) > i+1.\nThen since all agents in S except the one in slot i lie strictly below slot i + 1, and 225 the agent in slot i is not agent i + 1, it must be that agent i+1 is in a slot strictly below slot i+1.\nThis means that it is at least two slots below the agent that actually occupies slot i, and by Lemma 2 we then have rσ(i) ≥ ri+1 1 − γi+2 γi+1 .\nThus, γirσ(i) + γi+1rσ(i+1) γiri + γi+1ri+1 ≥ γi+1ri + γirσ(i) γiri + γi+1ri+1 ≥ min γi+1 γi , 1 − γi+2 γi+1 ff If slot i is not inverted, then on one hand we may have σ(i) ≤ i, in which case rσ(i)/ri ≥ 1.\nOn the other hand we may have σ(i) > i but there is some agent with index j ≤ i that lies at least two slots below slot i.\nThen by Lemma 2, rσ(i) ≥ rj 1 − γi+2 γi+1 ≥ ri 1 − γi+2 γi+1 .\nWe write i ∈ I if slot i is inverted, and i ∈ I if neither i nor i − 1 are inverted.\nBy our arguments above two consecutive slots cannot be inverted, so we can write f(σ) f(γr) = P i∈I ` γirσ(i) + γi+1rσ(i+1) ´ + P i∈I γirσ(i) P i∈I (γiri + γi+1ri+1) + P i∈I γiri ≥ min min i∈I γirσ(i) + γi+1rσ(i+1) γiri + γi+1ri+1 ff , min i∈I γirσ(i) γiri ffff ≥ L and this completes the proof.\nNote that for RBR, the standard value is also the efficient value by Theorem 1.\nAlso note that for an exponential decay model, L = min ˘1 δ , 1 − 1 δ ¯ .\nWith δ = 1.428 (see Section 2.1), the factor is L ≈ 1/3.34, so the total value in a pure-strategy Nash equilibrium of a second-price RBR slot auction is always within a factor of 3.34 of the efficient value with such a discount.\nAgain for RBB we have an analogous result.\nTheorem 5.\nFor an allocation σ that results from a purestrategy Nash equilibrium of a second-price RBB slot auction, we have g(σ) ≥ Lg(ηx).\nProof Sketch.\nSimply substitute bidder values for bidder revenues in the proof of Theorem 4, and appeal to Lemma 3.\n5.\nCONCLUSIONS This paper analyzed stylized versions of the slot auction designs currently used by Yahoo! and Google, namely rank by bid (RBB) and rank by revenue (RBR), respectively.\nWe also considered first and second pricing rules together with each of these allocation rules, since both have been used historically.\nWe first studied the short-run setting with incomplete information, corresponding to the case where agents have just approached the mechanism.\nOur equilibrium analysis revealed that RBB has much weaker informational requirements than RBR, because bidders need not know any information about relevance (even their own) to play the Bayes-Nash equilibrium.\nHowever, RBR leads to an efficient allocation in equilibrium, whereas RBB does not.\nWe showed that for an arbitrary distribution over value and relevance, no revenue ranking of RBB and RBR is possible.\nWe hope that the tools we used to establish these results (revenue equivalence, the form of first-price equilibria, the truthful payments rules) will help others wanting to pursue further analyses of slot auctions.\nWe also studied the long-run case where agents have experimented with their bids and each settled on one they find optimal.\nWe argued that a stable set of bids in this setting can be modeled as a pure-strategy Nash equilibrium of the static game of complete information.\nWe showed that no pure-strategy equilibrium exists with either RBB or RBR using first pricing, but that with second pricing there always exists such an equilibrium (in the case of RBR, an efficient equilibrium).\nIn general second pricing allows for multiple pure-strategy equilibria, but we showed that the value of such equilibria diverges by only a constant factor from the value obtained if all agents bid truthfully (which in the case of RBR is the efficient value).\n6.\nFUTURE WORK Introducing budget constraints into the model is a natural next step for future work.\nThe complication here lies in the fact that budgets are often set for entire campaigns rather than single keywords.\nAssuming that the optimal choice of budget can be made independent of the choice of bid for a specific keyword, it can be shown that it is a dominant-strategy to report this optimal budget with one``s bid.\nThe problem is then to ascertain that bids and budgets can indeed be optimized separately, or to find a plausible model where deriving equilibrium bids and budgets together is tractable.\nIdentifying a condition on the distribution over value and relevance that actually does yield a revenue ranking of RBB and RBR (such as correlation between value and relevance, perhaps) would yield a more satisfactory characterization of their relative revenue properties.\nPlacing bounds on the revenue obtained in a complete information equilibrium is also a relevant question.\nBecause the incomplete information case is such a close generalization of the most basic single-item auction model, it would be interesting to see which standard results from single-item auction theory (e.g. results with risk-averse bidders, an endogenous number of bidders, asymmetries, etc..\n.)\nautomatically generalize and which do not, to fully understand the structural differences between single-item and slot auctions.\nAcknowledgements David Pennock provided valuable guidance throughout this project.\nI would also like to thank David Parkes for helpful comments.\n7.\nREFERENCES [1] Z. Abrams.\nRevenue maximization when bidders have budgets.\nIn Proc.\nthe ACM-SIAM Symposium on Discrete Algorithms, 2006.\n[2] T. B¨orgers, I. Cox, and M. Pesendorfer.\nPersonal Communication.\n[3] C. Borgs, J. Chayes, N. Immorlica, M. Mahdian, and A. Saberi.\nMulti-unit auctions with budget-constrained bidders.\nIn Proc.\nthe Sixth ACM Conference on Electronic Commerce, Vancouver, BC, 2005.\n[4] F. Brandt and G. Weiß.\nAntisocial agents and Vickrey auctions.\nIn J.-J.\nC. Meyer and M. Tambe, editors, 226 Intelligent Agents VIII, volume 2333 of Lecture Notes in Artificial Intelligence.\nSpringer Verlag, 2001.\n[5] B. Edelman and M. Ostrovsky.\nStrategic bidder behavior in sponsored search auctions.\nIn Workshop on Sponsored Search Auctions, ACM Electronic Commerce, 2005.\n[6] B. Edelman, M. Ostrovsky, and M. Schwarz.\nInternet advertising and the generalized second price auction: Selling billions of dollars worth of keywords.\nNBER working paper 11765, November 2005.\n[7] J. Feng, H. K. Bhargava, and D. M. Pennock.\nImplementing sponsored search in web search engines: Computational evaluation of alternative mechanisms.\nINFORMS Journal on Computing, 2005.\nForthcoming.\n[8] J. Green and J.-J.\nLaffont.\nCharacterization of satisfactory mechanisms for the revelation of preferences for public goods.\nEconometrica, 45:427-438, 1977.\n[9] B. Holmstrom.\nGroves schemes on restricted domains.\nEconometrica, 47(5):1137-1144, 1979.\n[10] B. Kitts, P. Laxminarayan, B. LeBlanc, and R. Meech.\nA formal analysis of search auctions including predictions on click fraud and bidding tactics.\nIn Workshop on Sponsored Search Auctions, ACM Electronic Commerce, 2005.\n[11] V. Krishna.\nAuction Theory.\nAcademic Press, 2002.\n[12] D. Liu and J. Chen.\nDesigning online auctions with past performance information.\nDecision Support Systems, 2005.\nForthcoming.\n[13] C. Meek, D. M. Chickering, and D. B. Wilson.\nStochastic and contingent payment auctions.\nIn Workshop on Sponsored Search Auctions, ACM Electronic Commerce, 2005.\n[14] A. Mehta, A. Saberi, U. Vazirani, and V. Vazirani.\nAdwords and generalized on-line matching.\nIn Proc.\n46th IEEE Symposium on Foundations of Computer Science, 2005.\n[15] P. Milgrom.\nPutting Auction Theory to Work.\nCambridge University Press, 2004.\n[16] P. Milgrom and C. Shannon.\nMonotone comparative statics.\nEconometrica, 62(1):157-180, 1994.\n[17] P. J. Reny.\nOn the existence of pure and mixed strategy Nash equilibria in discontinuous games.\nEconometrica, 67(5):1029-1056, 1999.\n[18] H. R. Varian.\nPosition auctions.\nWorking Paper, February 2006.\n[19] W. Vickrey.\nCounterspeculation, auctions and competitive sealed tenders.\nJournal of Finance, 16:8-37, 1961.\n227"},"lvl-2":{"kind":"string","value":"An Analysis of Alternative Slot Auction Designs for Sponsored Search\nABSTRACT\nBillions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for placement alongside web search results.\nSlots for ad listings are allocated via an auction-style mechanism where the higher a merchant bids, the more likely his ad is to appear above other ads on the page.\nIn this paper we analyze the incentive, efficiency, and revenue properties of two slot auction designs: \"rank by bid\" (RBB) and \"rank by revenue\" (RBR), which correspond to stylized versions of the mechanisms currently used by Yahoo! and Google, respectively.\nWe also consider first - and second-price payment rules together with each of these allocation rules, as both have been used historically.\nWe consider both the \"short-run\" incomplete information setting and the \"long-run\" complete information setting.\nWith incomplete information, neither RBB nor RBR are truthful with either first or second pricing.\nWe find that the informational requirements of RBB are much weaker than those of RBR, but that RBR is efficient whereas RBB is not.\nWe also show that no revenue ranking of RBB and RBR is possible given an arbitrary distribution over bidder values and relevance.\nWith complete information, we find that no equilibrium exists with first pricing using either RBB or RBR.\nWe show that there typically exists a multitude of equilibria with second pricing, and we bound the divergence of (economic) value in such equilibria from the value obtained assuming all merchants bid truthfully.\n1.\nINTRODUCTION\nToday, Internet giants Google and Yahoo! boast a combined market capitalization of over $150 billion, largely on the strength of sponsored search, the fastest growing component of a resurgent online advertising industry.\nPricewaterhouseCoopers estimates that 2004 industry-wide sponsored search revenues were $3.9 billion, or 40% of total Internet advertising revenues . '\nIndustry watchers expect 2005 revenues to reach or exceed $7 billion .2 Roughly 80% of Google's estimated $4 billion in 2005 revenue and roughly 45% of Yahoo!'s estimated $3.7 billion in 2005 revenue will likely be attributable to sponsored search .3 A number of other companies--including LookSmart, FindWhat, InterActiveCorp (Ask Jeeves), and eBay (Shopping.com)--earn hundreds of millions of dollars of sponsored search revenue annually.\nSponsored search is a form of advertising where merchants pay to appear alongside web search results.\nFor example, when a user searches for \"used honda accord san diego\" in a web search engine, a variety of commercial entities (San Diego car dealers, Honda Corp, automobile information portals, classified ad aggregators, eBay, etc. .\n.)\nmay bid to to have their listings featured alongside the standard \"algorithmic\" search listings.\nAdvertisers bid for placement on the page in an auction-style format where the higher they bid the more likely their listing will appear above other ads on the page.\nBy convention, sponsored search advertisers generally pay per click, meaning that they pay only when a user clicks on their ad, and do not pay if their ad is displayed but not clicked.\nThough many people claim to systematically ignore sponsored search ads, Majestic Research reports that\nas many as 17% of Google searches result in a paid click, and that Google earns roughly nine cents on average for every search query they process .4 Usually, sponsored search results appear in a separate section of the page designated as \"sponsored\" above or to the right of the algorithmic results.\nSponsored search results are displayed in a format similar to algorithmic results: as a list of items each containing a title, a text description, and a hyperlink to a corresponding web page.\nWe call each position in the list a slot.\nGenerally, advertisements that appear in a higher ranked slot (higher on the page) garner more attention and more clicks from users.\nThus, all else being equal, merchants generally prefer higher ranked slots to lower ranked slots.\nMerchants bid for placement next to particular search queries; for example, Orbitz and Travelocity may bid for \"las vegas hotel\" while Dell and HP bid for \"laptop computer\".\nAs mentioned, bids are expressed as a maximum willingness to pay per click.\nFor example, a forty-cent bid by HostRocket for \"web hosting\" means HostRocket is willing to pay up to forty cents every time a user clicks on their ad .5 The auctioneer (the search engine6) evaluates the bids and allocates slots to advertisers.\nIn principle, the allocation decision can be altered with each new incoming search query, so in effect new auctions clear continuously over time as search queries arrive.\nMany allocation rules are plausible.\nIn this paper, we investigate two allocation rules, roughly corresponding to the two allocation rules used by Yahoo! and Google.\nThe \"rank by bid\" (RBB) allocation assigns slots in order of bids, with higher ranked slots going to higher bidders.\nThe \"rank by revenue\" (RBR) allocation assigns slots in order of the product of bid times expected relevance, where relevance is the proportion of users that click on the merchant's ad after viewing it.\nIn our model, we assume that an ad's expected relevance is known to the auctioneer and the advertiser (but not necessarily to other advertisers), and that clickthrough rate decays monotonically with lower ranked slots.\nIn practice, the expected clickthrough rate depends on a number of factors, including the position on the page, the ad text (which in turn depends on the identity of the bidder), the nature and intent of the user, and the context of other ads and algorithmic results on the page, and must be learned over time by both the auctioneer and the bidder [13].\nAs of this writing, to a rough first-order approximation, Yahoo! employs a RBB allocation and Google employs a RBR allocation, though numerous caveats apply in both cases when it comes to the vagaries of real-world implementations .7 Even when examining a one-shot version of a slot auction, the mechanism differs from a standard multi-item auc\nengine are not always the same entity.\nFor example Google runs the sponsored search ads for AOL web search, with revenue being shared.\nSimilarly, Yahoo! currently runs the sponsored search ads for MSN web search, though Microsoft will begin independent operations soon.\n7Here are two among many exceptions to the Yahoo! = RBB and Google = RBR assertion: (1) Yahoo! excludes ads deemed insufficiently relevant either by a human editor or due to poor historical click rate; (2) Google sets differing reserve prices depending on Google's estimate of ad quality.\ntion in subtle ways.\nFirst, a single bid per merchant is used to allocate multiple non-identical slots.\nSecond, the bid is communicated not as a direct preference over slots, but as a preference for clicks that depend stochastically on slot allocation.\nWe investigate a number of economic properties of RBB and RBR slot auctions.\nWe consider the \"short-run\" incomplete information case in Section 3, adapting and extending standard analyses of single-item auctions.\nIn Section 4 we turn to the \"long-run\" complete information case; our characterization results here draw on techniques from linear programming.\nThroughout, important observations are highlighted as claims supported by examples.\nOur contributions are as follows:\n• We show that with multiple slots, bidders do not reveal their true values with either RBB or RBR, and with either first - or second-pricing.\n• With incomplete information, we find that the informational requirements of playing the equilibrium bid are much weaker for RBB than for RBR, because bidders need not know any information about each others' relevance (or even their own) with RBB.\n• With incomplete information, we prove that RBR is efficient but that RBB is not.\n• We show via a simple example that no general revenue ranking of RBB and RBR is possible.\n• We prove that in a complete-information setting, firstprice slot auctions have no pure strategy Nash equilibrium, but that there always exists a pure-strategy equilibrium with second pricing.\n• We provide a constant-factor bound on the deviation from efficiency that can occur in the equilibrium of a second-price slot auction.\nIn Section 2 we specify our model of bidders and the various slot auction formats.\nIn Section 3.1 we study the incentive properties of each format, asking in which cases agents would bid truthfully.\nThere is possible confusion here because the \"second-price\" design for slot auctions is reminiscent of the Vickrey auction for a single item; we note that for slot auctions the Vickrey mechanism is in fact very different from the second-price mechanism, and so they have different incentive properties .8 In Section 3.2 we derive the Bayes-Nash equilibrium bids for the various auction formats.\nThis is useful for the efficiency and revenue results in later sections.\nIt should become clear in this section that slot auctions in our model are a straightforward generalization of single-item auctions.\nSections 3.3 and 3.4 address questions of efficiency and revenue under incomplete information, respectively.\nIn Section 4.1 we determine whether pure-strategy equilibria exist for the various auction formats, under complete information.\nIn Section 4.2 we derive bounds on the deviation from efficiency in the pure-strategy equilibria of secondprice slot auctions.\nOur approach is positive rather than normative.\nWe aim to clarify the incentive, efficiency, and revenue properties of two slot auction designs currently in use, under settings of\nincomplete and complete information.\nWe do not attempt to derive the \"optimal\" mechanism for a slot auction.\nRelated work.\nFeng et al. [7] compare the revenue performance of various ranking mechanisms for slot auctions in a model with incomplete information, much as we do in Section 3.4, but they obtain their results via simulations whereas we perform an equilibrium analysis.\nLiu and Chen [12] study properties of slot auctions under incomplete information.\nTheir setting is essentially the same as ours, except they restrict their attention to a model with a single slot and a binary type for bidder relevance (high or low).\nThey find that RBR is efficient, but that no general revenue ranking of RBB and RBR is possible, which agrees with our results.\nThey also take a design approach and show how the auctioneer should assign relevance scores to optimize its revenue.\nEdelman et al. [6] model the slot auction problem both as a static game of complete information and a dynamic game of incomplete information.\nThey study the \"locally envyfree equilibria\" of the static game of complete information; this is a solution concept motivated by certain bidding behaviors that arise due to the presence of budget constraints.\nThey do not view slot auctions as static games of incomplete information as we do, but do study them as dynamic games of incomplete information and derive results on the uniqueness and revenue properties of the resulting equilibria.\nThey also provide a nice description of the evolution of the market for sponsored search.\nVarian [18] also studies slot auctions under a setting of complete information.\nHe focuses on \"symmetric\" equilibria, which are a refinement of Nash equilibria appropriate for slot auctions.\nHe provides bounds on the revenue obtained in equilibrium.\nHe also gives bounds that can be used to infer bidder values given their bids, and performs some empirical analysis using these results.\nIn contrast, we focus instead on efficiency and provide bounds on the deviation from efficiency in complete-information equilibria.\n2.\nPRELIMINARIES\nWe focus on a slot auction for a single keyword.\nIn a setting of incomplete information, a bidder knows only distributions over others' private information (value per click and relevance).\nWith complete information, a bidder knows others' private information, and so does not need to rely on distributions to strategize.\nWe first describe the model for the case with incomplete information, and drop the distributional information from the model when we come to the complete-information case in Section 4.\n2.1 The Model\nThere is a fixed number K of slots to be allocated among N bidders.\nWe assume without loss of generality that K ≤ N, since superfluous slots can remain blank.\nBidder i assigns a value of Xi to each click received on its advertisement, regardless of this advertisement's rank .9 The probability that i's advertisement will be clicked if viewed is Ai ∈ [0, 1].\nWe refer to Ai as bidder i's relevance.\nWe refer to Ri = AiXi as bidder i's revenue.\nThe Xi, Ai, and Ri are random 9Indeed Kitts et al. [10] find that in their sample of actual click data, the correlation between rank and conversion rate is not statistically significant.\nHowever, for the purposes of our model it is also important that bidders believe that conversion rate does not vary with rank.\nvariables and we denote their realizations by xi, αi, and ri respectively.\nThe probability that an advertisement will be viewed if placed in slot j is ryj ∈ [0, 1].\nWe assume ry1> rye>...> ryK.\nHence bidder i's advertisement will have a clickthrough rate of ryjαi if placed in slot j. Of course, an advertisement does not receive any clicks if it is not allocated a slot.\nEach bidder's value and relevance pair (Xi, Ai) is independently and identically distributed on [0, ¯ x] × [0, 1] according to a continuous density function f that has full support on its domain.\nThe density f and slot probabilities ry1,..., ryK are common knowledge.\nOnly bidder i knows the realization xi of its value per click Xi.\nBoth bidder i and the seller know the realization αi of Ai, but this realization remains unobservable to the other bidders.\nWe assume that bidders have quasi-linear utility functions.\nThat is, the expected utility to bidder i of obtaining the slot of rank j at a price of b per click is ui (j, b) = ryj αi (xi − b) If the advertising firms bidding in the slot auction are riskneutral and have ample liquidity, quasi-linearity is a reasonable assumption.\nThe assumptions of independence, symmetry, and riskneutrality made above are all quite standard in single-item auction theory [11, 19].\nThe assumption that clickthrough rate decays monotonically with lower slots--by the same factors for each agent--is unique to the slot auction problem.\nWe view it as a main contribution of our work to show that this assumption allows for tractable analysis of the slot auction problem using standard tools from singleitem auction theory.\nIt also allows for interesting results in the complete information case.\nA common model of decaying clickthrough rate is the exponential decay model, where ryk = δk − 1 with decay δ> 1.\nFeng et al. [7] state that\ntheir actual clickthrough data is fitted extremely well by an exponential decay model with δ = 1.428.\nOur model lacks budget constraints, which are an important feature of real slot auctions.\nWith budget constraints keyword auctions cannot be considered independently of one another, because the budget must be allocated across multiple keywords--a single advertiser typically bids on multiple keywords relevant to his business.\nIntroducing this element into the model is an important next step for future work .10\n2.2 Auction Formats\nIn a slot auction a bidder provides to the seller a declared value per click ˜xi (xi, αi) which depends on his true value and relevance.\nWe often denote this declared value (bid) by ˜xi for short.\nSince a bidder's relevance αi is observable to the seller, the bidder cannot misrepresent it.\nWe denote the kth highest of the N declared values by ˜x (k), and the kth highest of the N declared revenues by ˜r (k), where the declared revenue of bidder i is ˜ri = αi˜xi.\nWe consider two types of allocation rules, \"rank by bid\" (RBB) and \"rank by revenue\" (RBR): 10Models with budget constraints have begun to appear in this research area.\nAbrams [1] and Borgs et al. [3] design multi-unit auctions for budget-constrained bidders, which can be interpreted as slot auctions, with a focus on revenue optimization and truthfulness.\nMehta et al. [14] address the problem of matching user queries to budget-constrained advertisers so as to maximize revenue.\nRBB.\nSlot k goes to bidder i if and only if ˜xi = ˜x (k).\nRBR.\nSlot k goes to bidder i if and only if ˜ri = ˜r (k).\nWe will commonly represent an allocation by a one-to-one function σ: [K]--+ [N], where [n] is the set of integers {1, 2,..., n}.\nHence slot k goes to bidder σ (k).\nWe also consider two different types of payment rules.\nNote that no matter what the payment rule, a bidder that is not allocated a slot will pay 0 since his listing cannot receive any clicks.\nFirst-price.\nThe bidder allocated slot k, namely σ (k), pays ˜xσ (k) per click under both the RBB and RBR allocation rules.\nSecond-price.\nIf k <N, bidder σ (k) pays ˜xσ (k +1) per click under the RBB rule, and pays ˜rσ (k +1) / ασ (k) per click under the RBR rule.\nIf k = N, bidder σ (k) pays 0 per click .11 Intuitively, a second-price payment rule sets a bidder's payment to the lowest bid it could have declared while maintaining the same ranking, given the allocation rule used.\nOverture introduced the first slot auction design in 1997, using a first-price RBB scheme.\nGoogle then followed in 2000 with a second-price RBR scheme.\nIn 2002, Overture (at this point acquired by Yahoo!) then switched to second pricing but still allocates using RBB.\nOne possible reason for the switch is given in Section 4.\nWe assume that ties are broken as follows in the event that two agents make the exact same bid or declare the same revenue.\nThere is a permutation of the agents κ: [N]--+ [N] that is fixed beforehand.\nIf the bids of agents i and j are tied, then agent i obtains a higher slot if and only if κ (i) <κ (j).\nThis is consistent with the practice in real slot auctions where ties are broken by the bidders' order of arrival.\n3.\nINCOMPLETE INFORMATION\n3.1 Incentives\nIt should be clear that with a first-price payment rule, truthful bidding is neither a dominant strategy nor an ex post Nash equilibrium using either RBB or RBR, because this guarantees a payoff of 0.\nThere is always an incentive to shade true values with first pricing.\nThe second-price payment rule is reminiscent of the secondprice (Vickrey) auction used for selling a single item, and in a Vickrey auction it is a dominant strategy for a bidder to reveal his true value for the item [19].\nHowever, using a second-price rule in a slot auction together with either allocation rule above does not yield an incentive-compatible mechanism, either in dominant strategies or ex post Nash equilibrium .12 With a second-price rule there is no incentive for a bidder to bid higher than his true value per click using either RBB or RBR: this either leads to no change 11We are effectively assuming a reserve price of zero, but in practice search engines charge a non-zero reserve price per click.\n12Unless of course there is only a single slot available, since this is the single-item case.\nWith a single slot both RBB and RBR with a second-price payment rule are dominantstrategy incentive-compatible.\nin the outcome, or a situation in which he will have to pay more than his value per click for each click received, resulting in a negative payoff .13 However, with either allocation rule there may be an incentive to shade true values with second pricing.\nEXAMPLE.\nThere are two agents and two slots.\nThe agents have relevance α1 = α2 = 1, whereas γ1 = 1 and γ2 = 1/2.\nAgent 1 has a value of x1 = 6 per click, and agent 2 has a value of x2 = 4 per click.\nLet us first consider the RBB rule.\nSuppose agent 2 bids truthfully.\nIf agent 1 also bids truthfully, he wins the first slot and obtains a payoff of\n2.\nHowever, if he shades his bid down below 4, he obtains the second slot at a cost of 0 per click yielding a payoff of 3.\nSince the agents have equal relevance, the exact same\nsituation holds with the RBR rule.\nHence truthful bidding is not a dominant strategy in either format, and neither is it an ex post Nash equilibrium.\nTo find payments that make RBB and RBR dominantstrategy incentive-compatible, we can apply Holmstrom's lemma [9] (see also chapter 3 in Milgrom [15]).\nUnder the restriction that a bidder with value 0 per click does not pay anything (even if he obtains a slot, which can occur if there are as many slots as bidders), this lemma implies that there is a unique payment rule that achieves dominant-strategy incentive compatibility for either allocation rule.\nFor RBB, the bidder allocated slot k is charged per click\nNote that if K = N, ˜x (K +1) = 0 since there is no K + 1th bidder.\nFor RBR, the bidder allocated slot k is charged per click\nUsing payment rule (2) and RBR, the auctioneer is aware of the true revenues of the bidders (since they reveal their values truthfully), and hence ranks them according to their true revenues.\nWe show in Section 3.3 that this allocation is in fact efficient.\nSince the VCG mechanism is the unique mechanism that is efficient, truthful, and ensures bidders with value 0 pay nothing (by the Green-Laffont theorem [8]), the RBR rule and payment scheme (2) constitute exactly the VCG mechanism.\nIn the VCG mechanism an agent pays the externality he imposes on others.\nTo understand payment (2) in this sense, note that the first term is the added utility (due to an increased clickthrough rate) agents in slots k + 1 to K would receive if they were all to move up a slot; the last term is the utility that the agent with the K +1 st revenue would receive by obtaining the last slot as opposed to nothing.\nThe leading coefficient simply reduces the agent's expected payment to a payment per click.\n13In a dynamic setting with second pricing, there may be an incentive to bid higher than one's true value in order to exhaust competitors' budgets.\nThis phenomenon is commonly called \"bid jamming\" or \"antisocial bidding\" [4].\n3.2 Equilibrium Analysis\nTo understand the efficiency and revenue properties of the various auction formats, we must first understand which rankings of the bidders occur in equilibrium with different allocation and payment rule combinations.\nThe following lemma essentially follows from the Monotonic Selection Theorem by Milgrom and Shannon [16].\nAs a consequence of this lemma, we find that RBB and RBR auctions allocate the slots greedily by the true values and revenues of the agents, respectively (whether using firstor second-price payment rules).\nThis will be relevant in Section 3.3 below.\nFor a first-price payment rule, we can explicitly derive the symmetric Bayes-Nash equilibrium bid functions for RBB and RBR auctions.\nThe purpose of this exercise is to lend qualitative insights into the parameters that influence an agent's bidding, and to derive formulae for the expected revenue in RBB and RBR auctions in order to make a revenue ranking of these two allocation rules (in Section 3.4).\nLet G (y) be the expected resulting clickthrough rate, in a symmetric equilibrium of the RBB auction (with either payment rule), to a bidder with value y and relevance α = 1.\nLet H (y) be the analogous quantity for a bidder with revenue y and relevance 1 in a RBR auction.\nBy Lemma 1, a bidder with value y will obtain slot k in a RBB auction if y is the kth highest of the true realized values.\nThe same applies in a RBR auction when y is the kth highest of the true realized revenues.\nLet FX (y) be the distribution function for value, and let FR (y) be the distribution function for revenue.\nThe probability that y is the kth highest out of N values is!\n(1 - FX (y)) k − 1FX (y) N − k whereas the probability that y is the kth highest out of N revenues is the same formula with FR replacing FX.\nHence we have!\n(1 - FX (y)) k − 1FX (y) N − k The H function is analogous to G with FR replacing FX.\nIn the two propositions that follow, g and h are the derivatives of G and H respectively.\nWe omit the proof of the next proposition, because it is almost identical to the derivation of the equilibrium bid in the single-item case (see Krishna [11], Proposition 2.2).\nPROPOSITION 1.\nThe symmetric Bayes-Nash equilibrium\nstrategies in a first-price RBB auction are given by\nThe first-price equilibrium above closely parallels the firstprice equilibrium in the single-item model.\nWith a single item g is the density of the second highest value among all N agent values, whereas in a slot auction it is a weighted combination of the densities for the second, third, etc. highest values.\nNote that the symmetric Bayes-Nash equilibrium bid in a first-price RBB auction does not depend on a bidder's relevance α.\nTo see clearly why, note that a bidder chooses a bid b so as to maximize the objective\nand here α is just a leading constant factor.\nSo dropping it does not change the set of optimal solutions.\nHence the equilibrium bid depends only on the value x and function G, and G in turn depends only on the marginal cumulative distribution of value FX.\nSo really only the latter needs to be common knowledge to the bidders.\nOn the other hand, we will now see that information about relevance is needed for bidders to play the equilibrium in the first-price RBR auction.\nSo the informational requirements for a first-price RBB auction are much weaker than for a first-price RBR auction: in the RBB auction a bidder need not know his own relevance, and need not know any distributional information over others' relevance in order to play the equilibrium.\nAgain we omit the next proposition's proof since it is so similar to the one above.\nPROPOSITION 2.\nThe symmetric Bayes-Nash equilibrium strategies in a first-price RBR auction are given by\nHere it can be seen that the equilibrium bid is increasing with x, but not necessarily with α.\nThis should not be much of a concern to the auctioneer, however, because in any case the declared revenue in equilibrium is always increasing in the true revenue.\nIt would be interesting to obtain the equilibrium bids when using a second-price payment rule, but it appears that the resulting differential equations for this case do not have a neat analytical solution.\nNonetheless, the same conclusions about the informational requirements of the RBB and RBR rules still hold, as can be seen simply by inspecting the objective function associated with an agent's bidding problem for the second-price case.\n3.3 Efficiency\nA slot auction is efficient if in equilibrium the sum of the bidders' revenues from their allocated slots is maximized.\nUsing symmetry as our equilibrium selection criterion, we find that the RBB auction is not efficient with either payment rule.\nCLAIM 2.\nThe RBB auction is not efficient with either first or second pricing.\nEXAMPLE.\nThere are two agents and one slot, with γ1 = 1.\nAgent 1 has a value of x1 = 6 per click and relevance α1 = 1/2.\nAgent 2 has a value of x2 = 4 per click and relevance α2 = 1.\nBy Lemma 1, agents are ranked greedily by value.\nHence agent 1 obtains the lone slot, for a total revenue of 3 to the agents.\nHowever, it is most efficient to allocate the slot to agent 2, for a total revenue of 4.\nExamples with more agents or more slots are simple to construct along the same lines.\nOn the other hand, under our assumptions on how clickthrough rate decreases with lower rank, the RBR auction is efficient with either payment rule.\nPROOF.\nSince by Lemma 1 the agents' equilibrium bids are increasing functions of their revenues in the RBR auction, slots are allocated greedily according to true revenues.\nLet σ be a non-greedy allocation.\nThen there are slots s, t with s <t and rσ (s) <rσ (t).\nWe can switch the agents in slots s and t to obtain a new allocation, and the difference between the total revenue in this new allocation and the original allocation's total revenue is\nBoth parenthesized terms above are positive.\nHence the switch has increased the total revenue to the bidders.\nIf we continue to perform such switches, we will eventually reach a greedy allocation of greater revenue than the initial allocation.\nSince the initial allocation was arbitrary, it follows that a greedy allocation is always efficient, and hence the RBR auction's allocation is efficient.\nNote that the assumption that clickthrough rate decays montonically by the same factors 71,..., 7K for all agents is crucial to this result.\nA greedy allocation scheme does not necessarily find an efficient solution if the clickthrough rates are monotonically decreasing in an independent fashion for each agent.\n3.4 Revenue\nTo obtain possible revenue rankings for the different auction formats, we first note that when the allocation rule is fixed to RBB, then using either a first-price, second-price, or truthful payment rule leads to the same expected revenue in a symmetric, increasing Bayes-Nash equilibrium.\nBecause a RBB auction ranks agents by their true values in equilibrium for any of these payment rules (by Lemma 1), it follows that expected revenue is the same for all these payment rules, following arguments that are virtually identical to those used to establish revenue equivalence in the singleitem case (see e.g. Proposition 3.1 in Krishna [11]).\nThe same holds for RBR auctions; however, the revenue ranking of the RBB and RBR allocation rules is still unclear.\nBecause of this revenue equivalence principle, we can choose whichever payment rule is most convenient for the purpose of making revenue comparisons.\nUsing Propositions 1 and 2, it is a simple matter to derive formulae for the expected revenue under both allocation rules.\nThe payment of an agent in a RBB auction is\nThe expected revenue is then N • E ˆmV (X, A) ˜, where the expectation is taken with respect to the joint density of value and relevance.\nThe expected revenue formula for RBR auctions is entirely analogous using ˜xR (x, α) and the H function.\nWith these in hand we can obtain revenue rankings for specific numbers of bidders and slots, and specific distributions over values and relevance.\nEXAMPLE.\nAssume there are 2 bidders, 2 slots, and that 71 = 1, 72 = 1/2.\nAssume that value-relevance pairs are uniformly distributed over [0, 1] x [0, 1].\nFor such a distribution with a closed-form formula, it is most convenient to use the revenue formulae just derived.\nRBB dominates RBR in terms of revenue for these parameters.\nThe formula for the expected revenue in a RBB auction yields 1/12, whereas for RBR auctions we have 7/108.\nAssume instead that with probability 1/2 an agent's valuerelevance pair is (1, 1/2), and that with probability 1/2 it is (1/2, 1).\nIn this scenario it is more convenient to appeal to formulae (1) and (2).\nIn a truthful auction the second agent will always pay 0.\nAccording to (1), in a truthful RBB auction the first agent makes an expected payment of\nwhere we have used the fact that value and relevance are independently distributed for different agents.\nThe expected relevance of the agent with the highest value is E ˆAσ (1) ˜ = 5/8.\nThe expected second highest value is also E ˆXσ (2) ˜ = 5/8.\nThe expected revenue for a RBB auction here is then 25/128.\nAccording to (2), in a truthful RBR auction the first agent makes an expected payment of\nIn expectation the second highest revenue is E ˆRσ (2) ˜ = 1/2, so the expected revenue for a RBR auction is 1/4.\nHence in this case the RBR auction yields higher expected revenue .1415 This example suggests the following conjecture: when value and relevance are either uncorrelated or positively correlated, RBB dominates RBR in terms of revenue.\nWhen value and relevance are negatively correlated, RBR dominates.\n4.\nCOMPLETE INFORMATION\nIn typical slot auctions such as those run by Yahoo! and Google, bidders can adjust their bids up or down at any time.\nAs B ¨ orgers et al. [2] and Edelman et al. [6] have noted, this can be viewed as a continuous-time process in which bidders learn each other's bids.\nIf the process stabilizes the result can then be modeled as a Nash equilibrium in pure strategies of the static one-shot game of complete information, since each bidder will be playing a best-response to the others' bids .16 This argument seems especially appropriate for Yahoo!'s slot auction design where all bids are 14To be entirely rigorous and consistent with our initial assumptions, we should have constructed a continuous probability density with full support over an appropriate domain.\nTaking the domain to be e.g. [0, 1] x [0, 1] and a continuous density with full support that is sufficiently concentrated around (1, 1/2) and (1/2, 1), with roughly equal mass around both, would yield the same conclusion.\n15Claim 3 should serve as a word of caution, because Feng et al. [7] find through their simulations that with a bivariate normal distribution over value-relevance pairs, and with 5 slots, 15 bidders, and δ = 2, RBR dominates RBB in terms of revenue for any level of correlation between value and relevance.\nHowever, they assume that bidding behavior in a second-price slot auction can be well approximated by truthful bidding.\n16We do not claim that bidders will actually learn each others' private information (value and relevance), just that for a stable set of bids there is a corresponding equilibrium of the complete information game.\nmade public.\nGoogle keeps bids private, but experimentation can allow one to discover other bids, especially since second pricing automatically reveals to an agent the bid of the agent ranked directly below him.\n4.1 Equilibrium Analysis\nIn this section we ask whether a pure-strategy Nash equilibrium exists in a RBB or RBR slot auction, with either first or second pricing.\nBefore dealing with the first-price case there is a technical issue involving ties.\nIn our model we allow bids to be nonnegative real numbers for mathematical convenience, but this can become problematic because there is then no bid that is \"just higher\" than another.\nWe brush over such issues by assuming that an agent can bid \"infinitesimally higher\" than another.\nThis is imprecise but allows us to focus on the intuition behind the result that follows.\nSee Reny [17] for a full treatment of such issues.\nFor the remainder of the paper, we assume that there are as many slots as bidders.\nThe following result shows that there can be no pure-strategy Nash equilibrium with first pricing .17 Note that the argument holds for both RBB and RBR allocation rules.\nFor RBB, bids should be interpreted as declared values, and for RBR as declared revenues.\nPROOF.\nLet σ: [K] → [N] be the allocation of slots to the agents resulting from their bids.\nLet ri and bi be the revenue and bid of the agent ranked ith, respectively.\nNote that we cannot have bi> bi +1, or else the agent in slot i can make a profitable deviation by instead bidding bi − $> bi +1 for small enough $> 0.\nThis does not change its allocation, but increases its profit.\nHence we must have bi = bi +1 (i.e. with one bidder bidding infinitesimally higher than the other).\nSince this holds for any two consecutive bidders, it follows that in a Nash equilibrium all bidders must be bidding 0 (since the bidder ranked last matches the bid directly below him, which is 0 by default because there is no such bid).\nBut this is impossible: consider the bidder ranked last.\nThe identity of this bidder is always clear given the deterministic tie-breaking rule.\nThis bidder can obtain the top spot and increase his revenue by (γ1 − γK) rK> 0 by bidding some $> 0, and for small enough $this is necessarily a profitable deviation.\nHence there is no Nash equilibrium in pure strategies.\nOn the other hand, we find that in a second-price slot auction there can be a multitude of pure strategy Nash equilibria.\nThe next two lemmas give conditions that characterize the allocations that can occur as a result of an equilibrium profile of bids, given fixed agent values and revenues.\nThen if we can exhibit an allocation that satisfies these conditions, there must exist at least one equilibrium.\nWe first consider the RBR case.\n17B ¨ orgers et al. [2] have proven this result in a model with three bidders and three slots, and we generalize their argument.\nEdelman et al. [6] also point out this non-existence phenomenon.\nThey only illustrate the fact with an example because the result is quite immediate.\nPROOF.\nThere exists a desired vector b which constitutes a Nash equilibrium if and only if the following set of inequalities can be satisfied (the variables are the πi and bj):\nHere rσ (i) is the revenue of the agent allocated slot i, and πi and bi may be interpreted as this agent's surplus and declared revenue, respectively.\nWe first argue that constraints (6) can be removed, because the inequalities above can be satisfied if and only if the inequalities without (6) can be satisfied.\nThe necessary direction is immediate.\nAssume we have a vector (π, b) which satisfies all inequalities above except (6).\nThen there is some i for which bi <bi +1.\nConstruct a new vector (π, b') identical to the original except with b ` i +1 = bi.\nWe now have b' i = b ` i +1.\nAn agent in slot k <i sees the price of slot i decrease from bi +1 to b ` i +1 = bi, but this does not make i more preferred than k to this agent because we have πk ≥ γi-1 (rσ (k) − bi) ≥ γi (rσ (k) − bi) = γi (rσ (k) − b ` i +1) (i.e. because the agent in slot k did not originally prefer slot i − 1 at price bi, he will not prefer slot i at price bi).\nA similar argument applies for agents in slots k> i + 1.\nThe agent in slot i sees the price of this slot go down, which only makes it more preferred.\nFinally, the agent in slot i + 1 sees no change in the price of any slot, so his slot remains most preferred.\nHence inequalities (3)--(5) remain valid at (π, b').\nWe first make this change to the bi +1 where bi <bi +1 and index i is smallest.\nWe then recursively apply the change until we eventually obtain a vector that satisfies all inequalities.\nWe safely ignore inequalities (6) from now on.\nBy the Farkas lemma, the remaining inequalities can be satisfied if and only if there is no vector z such that\nNote that a variable of the form zσ (i) i appears at most once in a constraint of type (8), so such a variable can never be positive.\nAlso, zσ (i) 1 = 0 for all i = 1 by constraint (7), since such variables never appear with another of the form zσ (i) i.\nNow if we wish to raise zσ (i) j above 0 by one unit for j = i, we must lower zσ (i) i by one unit because of the constraint of type (8).\nBecause γjrσ (i) ≤ γirσ (i) for i <j, raising\nzσ (i) j with i <j while adjusting other variables to maintain feasibility cannot make the objective Pi, j (7jrσ (i)) zσ (i) j positive.\nIf this objective is positive, then this is due to some component zσ (i) j with i> j being positive.\nNow for the constraints of type (7), if i> j then zσ (i) j appears with zσ (j − 1) j − 1 (for 1 <j <N).\nSo to raise the former variable 7 − 1 j units and maintain feasibility, we must (I) lower zσ (i) i by 7 − 1 j units, and (II) lower zσ (j − 1) j − 1 by\nfor 2 ≤ j ≤ N − 1 and i> j, raising some zσ (i) j with i> j cannot make the objective positive, and there is no z that satisfies all inequalities above.\nConversely, if some inequality (9) does not hold, the objective can be made positive by raising the corresponding zσ (i) j and adjusting other variables so that feasibility is just maintained.\nBy a slight reindexing, inequalities (9) yield the statement of the theorem.\nThe RBB case is entirely analogous.\nPROOF SKETCH.\nThe proof technique is the same as in the previous lemma.\nThe desired Nash equilibrium exists if and only if a related set of inequalities can be satisfied; by the Farkas lemma, this occurs if and only if an alternate set of inequalities cannot be satisfied.\nThe conditions that determine whether the latter holds are given in the statement of the lemma.\nThe two lemmas above immediately lead to the following result.\nPROOF.\nFirst consider RBB.\nSuppose agents are ranked according to their true values.\nSince xσ (i) ≤ xσ (j) for i> j, the system of inequalities in Lemma 3 is satisfied, and the allocation is the result of some Nash equilibrium bid profile.\nBy the same type of argument but appealing to Lemma 2 for RBR, there exists a Nash equilibrium bid profile such that bidders are ranked according to their true revenues.\nBy Theorem 1, this latter allocation is efficient.\nThis theorem establishes existence but not uniqueness.\nIndeed we expect that in many cases there will be multiple allocations (and hence equilibria) which satisfy the conditions of Lemmas 2 and 3.\nIn particular, not all equilibria of a second-price RBR auction will be efficient.\nFor instance, according to Lemma 2, with two agents and two slots any allocation can arise in a RBR equilibrium because no constraints apply.\nTheorems 2 and 3 taken together provide a possible explanation for Yahoo!'s switch from first to second pricing.\nWe saw in Section 3.1 that this does not induce truthfulness from bidders.\nWith first pricing, there will always be some bidder that feels compelled to adjust his bid.\nSecond pricing is more convenient because an equilibrium can be reached, and this reduces the cost of bid management.\n4.2 Efficiency\nFor a given allocation rule, we call the allocation that would result if the bidders reported their values truthfully the standard allocation.\nHence in the standard RBB allocation bidders are ranked by true values, and in the standard RBR allocation they are ranked by true revenues.\nAccording to Lemmas 2 and 3, a ranking that results from a Nash equilibrium profile can only deviate from the standard allocation by having agents with relatively similar values or revenues switch places.\nThat is, if ri> rj then with RBR agent j can be ranked higher than i only if the ratio rj/ri is sufficiently large; similarly for RBB.\nThis suggests that the value of an equilibrium allocation cannot differ too much from the value obtained in the standard allocation, and the following theorems confirms this.\ntal value by f (σ) = PN For an allocation σ of slots to agents, we denote its toi = 1 7irσ (i).\nWe denote by g (σ) = PNi = 1 7ixσ (i) allocation σ's value when assuming all agents have identical relevance, normalized to 1.\nLet\n(where by default 7N +1 = 0).\nLet 77x and 77r be the standard allocations when using RBB and RBR, respectively.\nTHEOREM 4.\nFor an allocation σ that results from a purestrategy Nash equilibrium of a second-price RBR slot auction, we have f (σ) ≥ Lf (77r).\nPROOF.\nWe number the agents so that agent i has the ith highest revenue, so r1 ≥ r2 ≥...≥ rN.\nHence the standard allocation has value f (77r) = PNi = 1 7iri.\nTo prove the theorem, we will make repeated use of the fact that P ak\nPk ak\nk bk ≥ mink bk when the ak and bk are positive.\nNote that according to Lemma 2, if agent i lies at least two slots \"\" below slot j, then rσ (j) ≥ ri 1 − γ' +2.\nγ' +1 It may be the case that for some slot i, we have σ (i)> i and for slots k> i + 1 we have σ (k)> i.\nWe then say that slot i is inverted.\nLet S be the set of agents with indices at least i + 1; there are N − i of these.\nIf slot i is inverted, it is occupied by some agent from S. Also all slots strictly lower than i + 1 must be occupied by the remaining agents from S, since σ (k)> i for k ≥ i + 2.\nThe agent in slot i + 1 must then have an index σ (i + 1) ≤ i (note this means slot i + 1 cannot be inverted).\nNow there are two cases.\nIn the first case we have σ (i) = i + 1.\nThen\nIn the second case we have σ (i)> i +1.\nThen since all agents in S except the one in slot i lie strictly below slot i + 1, and\nthe agent in slot i is not agent i + 1, it must be that agent i +1 is in a slot strictly below slot i +1.\nThis means that it is at least two slots below the agent that actually occupies slot \"\" i, and by Lemma 2 we then have rσ (i)> ri +1 1--γi +2.\nIf slot i is not inverted, then on one hand we may have σ (i) <i, in which case rσ (i) / ri> 1.\nOn the other hand we may have σ (i)> i but there is some agent with index j <i that lies at least two slots below slot i.\nThen by Lemma 2,\nWe write i E I if slot i is inverted, and i E I if neither i nor\nand this completes the proof.\nNote that for RBR, the standard value is also the efficient value by Theorem 1.\nAlso note that for an exponential decay model, L = min ˘ δ1, 1--1 ¯.\nWith δ = 1.428 (see Secδ tion 2.1), the factor is L Pz 1/3 .34, so the total value in a pure-strategy Nash equilibrium of a second-price RBR slot auction is always within a factor of 3.34 of the efficient value with such a discount.\nAgain for RBB we have an analogous result.\nTHEOREM 5.\nFor an allocation σ that results from a purestrategy Nash equilibrium of a second-price RBB slot auction, we have g (σ)> Lg (ηx).\nPROOF SKETCH.\nSimply substitute bidder values for bidder revenues in the proof of Theorem 4, and appeal to Lemma 3.\n5.\nCONCLUSIONS\nThis paper analyzed stylized versions of the slot auction designs currently used by Yahoo! and Google, namely \"rank by bid\" (RBB) and \"rank by revenue\" (RBR), respectively.\nWe also considered first and second pricing rules together with each of these allocation rules, since both have been used historically.\nWe first studied the \"short-run\" setting with incomplete information, corresponding to the case where agents have just approached the mechanism.\nOur equilibrium analysis revealed that RBB has much weaker informational requirements than RBR, because bidders need not know any information about relevance (even their own) to play the Bayes-Nash equilibrium.\nHowever, RBR leads to an efficient allocation in equilibrium, whereas RBB does not.\nWe showed that for an arbitrary distribution over value and relevance, no revenue ranking of RBB and RBR is possible.\nWe hope that the tools we used to establish these results (revenue equivalence, the form of first-price equilibria, the truthful payments rules) will help others wanting to pursue further analyses of slot auctions.\nWe also studied the \"long-run\" case where agents have experimented with their bids and each settled on one they find optimal.\nWe argued that a stable set of bids in this setting can be modeled as a pure-strategy Nash equilibrium of the static game of complete information.\nWe showed that no pure-strategy equilibrium exists with either RBB or RBR using first pricing, but that with second pricing there always exists such an equilibrium (in the case of RBR, an efficient equilibrium).\nIn general second pricing allows for multiple pure-strategy equilibria, but we showed that the value of such equilibria diverges by only a constant factor from the value obtained if all agents bid truthfully (which in the case of RBR is the efficient value).\n6.\nFUTURE WORK\nIntroducing budget constraints into the model is a natural next step for future work.\nThe complication here lies in the fact that budgets are often set for entire campaigns rather than single keywords.\nAssuming that the optimal choice of budget can be made independent of the choice of bid for a specific keyword, it can be shown that it is a dominant-strategy to report this optimal budget with one's bid.\nThe problem is then to ascertain that bids and budgets can indeed be optimized separately, or to find a plausible model where deriving equilibrium bids and budgets together is tractable.\nIdentifying a condition on the distribution over value and relevance that actually does yield a revenue ranking of RBB and RBR (such as correlation between value and relevance, perhaps) would yield a more satisfactory characterization of their relative revenue properties.\nPlacing bounds on the revenue obtained in a complete information equilibrium is also a relevant question.\nBecause the incomplete information case is such a close generalization of the most basic single-item auction model, it would be interesting to see which standard results from single-item auction theory (e.g. results with risk-averse bidders, an endogenous number of bidders, asymmetries, etc. .\n.)\nautomatically generalize and which do not, to fully understand the structural differences between single-item and slot auctions."},"lvl-3":{"kind":"string","value":"An Analysis of Alternative Slot Auction Designs for Sponsored Search\nABSTRACT\nBillions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for placement alongside web search results.\nSlots for ad listings are allocated via an auction-style mechanism where the higher a merchant bids, the more likely his ad is to appear above other ads on the page.\nIn this paper we analyze the incentive, efficiency, and revenue properties of two slot auction designs: \"rank by bid\" (RBB) and \"rank by revenue\" (RBR), which correspond to stylized versions of the mechanisms currently used by Yahoo! and Google, respectively.\nWe also consider first - and second-price payment rules together with each of these allocation rules, as both have been used historically.\nWe consider both the \"short-run\" incomplete information setting and the \"long-run\" complete information setting.\nWith incomplete information, neither RBB nor RBR are truthful with either first or second pricing.\nWe find that the informational requirements of RBB are much weaker than those of RBR, but that RBR is efficient whereas RBB is not.\nWe also show that no revenue ranking of RBB and RBR is possible given an arbitrary distribution over bidder values and relevance.\nWith complete information, we find that no equilibrium exists with first pricing using either RBB or RBR.\nWe show that there typically exists a multitude of equilibria with second pricing, and we bound the divergence of (economic) value in such equilibria from the value obtained assuming all merchants bid truthfully.\n1.\nINTRODUCTION\nToday, Internet giants Google and Yahoo! boast a combined market capitalization of over $150 billion, largely on the strength of sponsored search, the fastest growing component of a resurgent online advertising industry.\nPricewaterhouseCoopers estimates that 2004 industry-wide sponsored search revenues were $3.9 billion, or 40% of total Internet advertising revenues . '\nIndustry watchers expect 2005 revenues to reach or exceed $7 billion .2 Roughly 80% of Google's estimated $4 billion in 2005 revenue and roughly 45% of Yahoo!'s estimated $3.7 billion in 2005 revenue will likely be attributable to sponsored search .3 A number of other companies--including LookSmart, FindWhat, InterActiveCorp (Ask Jeeves), and eBay (Shopping.com)--earn hundreds of millions of dollars of sponsored search revenue annually.\nSponsored search is a form of advertising where merchants pay to appear alongside web search results.\nFor example, when a user searches for \"used honda accord san diego\" in a web search engine, a variety of commercial entities (San Diego car dealers, Honda Corp, automobile information portals, classified ad aggregators, eBay, etc. .\n.)\nmay bid to to have their listings featured alongside the standard \"algorithmic\" search listings.\nAdvertisers bid for placement on the page in an auction-style format where the higher they bid the more likely their listing will appear above other ads on the page.\nBy convention, sponsored search advertisers generally pay per click, meaning that they pay only when a user clicks on their ad, and do not pay if their ad is displayed but not clicked.\nThough many people claim to systematically ignore sponsored search ads, Majestic Research reports that\nas many as 17% of Google searches result in a paid click, and that Google earns roughly nine cents on average for every search query they process .4 Usually, sponsored search results appear in a separate section of the page designated as \"sponsored\" above or to the right of the algorithmic results.\nSponsored search results are displayed in a format similar to algorithmic results: as a list of items each containing a title, a text description, and a hyperlink to a corresponding web page.\nWe call each position in the list a slot.\nGenerally, advertisements that appear in a higher ranked slot (higher on the page) garner more attention and more clicks from users.\nThus, all else being equal, merchants generally prefer higher ranked slots to lower ranked slots.\nMerchants bid for placement next to particular search queries; for example, Orbitz and Travelocity may bid for \"las vegas hotel\" while Dell and HP bid for \"laptop computer\".\nAs mentioned, bids are expressed as a maximum willingness to pay per click.\nFor example, a forty-cent bid by HostRocket for \"web hosting\" means HostRocket is willing to pay up to forty cents every time a user clicks on their ad .5 The auctioneer (the search engine6) evaluates the bids and allocates slots to advertisers.\nIn principle, the allocation decision can be altered with each new incoming search query, so in effect new auctions clear continuously over time as search queries arrive.\nMany allocation rules are plausible.\nIn this paper, we investigate two allocation rules, roughly corresponding to the two allocation rules used by Yahoo! and Google.\nThe \"rank by bid\" (RBB) allocation assigns slots in order of bids, with higher ranked slots going to higher bidders.\nThe \"rank by revenue\" (RBR) allocation assigns slots in order of the product of bid times expected relevance, where relevance is the proportion of users that click on the merchant's ad after viewing it.\nIn our model, we assume that an ad's expected relevance is known to the auctioneer and the advertiser (but not necessarily to other advertisers), and that clickthrough rate decays monotonically with lower ranked slots.\nIn practice, the expected clickthrough rate depends on a number of factors, including the position on the page, the ad text (which in turn depends on the identity of the bidder), the nature and intent of the user, and the context of other ads and algorithmic results on the page, and must be learned over time by both the auctioneer and the bidder [13].\nAs of this writing, to a rough first-order approximation, Yahoo! employs a RBB allocation and Google employs a RBR allocation, though numerous caveats apply in both cases when it comes to the vagaries of real-world implementations .7 Even when examining a one-shot version of a slot auction, the mechanism differs from a standard multi-item auc\nengine are not always the same entity.\nFor example Google runs the sponsored search ads for AOL web search, with revenue being shared.\nSimilarly, Yahoo! currently runs the sponsored search ads for MSN web search, though Microsoft will begin independent operations soon.\n7Here are two among many exceptions to the Yahoo! = RBB and Google = RBR assertion: (1) Yahoo! excludes ads deemed insufficiently relevant either by a human editor or due to poor historical click rate; (2) Google sets differing reserve prices depending on Google's estimate of ad quality.\ntion in subtle ways.\nFirst, a single bid per merchant is used to allocate multiple non-identical slots.\nSecond, the bid is communicated not as a direct preference over slots, but as a preference for clicks that depend stochastically on slot allocation.\nWe investigate a number of economic properties of RBB and RBR slot auctions.\nWe consider the \"short-run\" incomplete information case in Section 3, adapting and extending standard analyses of single-item auctions.\nIn Section 4 we turn to the \"long-run\" complete information case; our characterization results here draw on techniques from linear programming.\nThroughout, important observations are highlighted as claims supported by examples.\nOur contributions are as follows:\n• We show that with multiple slots, bidders do not reveal their true values with either RBB or RBR, and with either first - or second-pricing.\n• With incomplete information, we find that the informational requirements of playing the equilibrium bid are much weaker for RBB than for RBR, because bidders need not know any information about each others' relevance (or even their own) with RBB.\n• With incomplete information, we prove that RBR is efficient but that RBB is not.\n• We show via a simple example that no general revenue ranking of RBB and RBR is possible.\n• We prove that in a complete-information setting, firstprice slot auctions have no pure strategy Nash equilibrium, but that there always exists a pure-strategy equilibrium with second pricing.\n• We provide a constant-factor bound on the deviation from efficiency that can occur in the equilibrium of a second-price slot auction.\nIn Section 2 we specify our model of bidders and the various slot auction formats.\nIn Section 3.1 we study the incentive properties of each format, asking in which cases agents would bid truthfully.\nThere is possible confusion here because the \"second-price\" design for slot auctions is reminiscent of the Vickrey auction for a single item; we note that for slot auctions the Vickrey mechanism is in fact very different from the second-price mechanism, and so they have different incentive properties .8 In Section 3.2 we derive the Bayes-Nash equilibrium bids for the various auction formats.\nThis is useful for the efficiency and revenue results in later sections.\nIt should become clear in this section that slot auctions in our model are a straightforward generalization of single-item auctions.\nSections 3.3 and 3.4 address questions of efficiency and revenue under incomplete information, respectively.\nIn Section 4.1 we determine whether pure-strategy equilibria exist for the various auction formats, under complete information.\nIn Section 4.2 we derive bounds on the deviation from efficiency in the pure-strategy equilibria of secondprice slot auctions.\nOur approach is positive rather than normative.\nWe aim to clarify the incentive, efficiency, and revenue properties of two slot auction designs currently in use, under settings of\nincomplete and complete information.\nWe do not attempt to derive the \"optimal\" mechanism for a slot auction.\nRelated work.\nFeng et al. [7] compare the revenue performance of various ranking mechanisms for slot auctions in a model with incomplete information, much as we do in Section 3.4, but they obtain their results via simulations whereas we perform an equilibrium analysis.\nLiu and Chen [12] study properties of slot auctions under incomplete information.\nTheir setting is essentially the same as ours, except they restrict their attention to a model with a single slot and a binary type for bidder relevance (high or low).\nThey find that RBR is efficient, but that no general revenue ranking of RBB and RBR is possible, which agrees with our results.\nThey also take a design approach and show how the auctioneer should assign relevance scores to optimize its revenue.\nEdelman et al. [6] model the slot auction problem both as a static game of complete information and a dynamic game of incomplete information.\nThey study the \"locally envyfree equilibria\" of the static game of complete information; this is a solution concept motivated by certain bidding behaviors that arise due to the presence of budget constraints.\nThey do not view slot auctions as static games of incomplete information as we do, but do study them as dynamic games of incomplete information and derive results on the uniqueness and revenue properties of the resulting equilibria.\nThey also provide a nice description of the evolution of the market for sponsored search.\nVarian [18] also studies slot auctions under a setting of complete information.\nHe focuses on \"symmetric\" equilibria, which are a refinement of Nash equilibria appropriate for slot auctions.\nHe provides bounds on the revenue obtained in equilibrium.\nHe also gives bounds that can be used to infer bidder values given their bids, and performs some empirical analysis using these results.\nIn contrast, we focus instead on efficiency and provide bounds on the deviation from efficiency in complete-information equilibria.\n2.\nPRELIMINARIES\nWe focus on a slot auction for a single keyword.\nIn a setting of incomplete information, a bidder knows only distributions over others' private information (value per click and relevance).\nWith complete information, a bidder knows others' private information, and so does not need to rely on distributions to strategize.\nWe first describe the model for the case with incomplete information, and drop the distributional information from the model when we come to the complete-information case in Section 4.\n2.1 The Model\nThere is a fixed number K of slots to be allocated among N bidders.\nWe assume without loss of generality that K ≤ N, since superfluous slots can remain blank.\nBidder i assigns a value of Xi to each click received on its advertisement, regardless of this advertisement's rank .9 The probability that i's advertisement will be clicked if viewed is Ai ∈ [0, 1].\nWe refer to Ai as bidder i's relevance.\nWe refer to Ri = AiXi as bidder i's revenue.\nThe Xi, Ai, and Ri are random 9Indeed Kitts et al. [10] find that in their sample of actual click data, the correlation between rank and conversion rate is not statistically significant.\nHowever, for the purposes of our model it is also important that bidders believe that conversion rate does not vary with rank.\nvariables and we denote their realizations by xi, αi, and ri respectively.\nThe probability that an advertisement will be viewed if placed in slot j is ryj ∈ [0, 1].\nWe assume ry1> rye>...> ryK.\nHence bidder i's advertisement will have a clickthrough rate of ryjαi if placed in slot j. Of course, an advertisement does not receive any clicks if it is not allocated a slot.\nEach bidder's value and relevance pair (Xi, Ai) is independently and identically distributed on [0, ¯ x] × [0, 1] according to a continuous density function f that has full support on its domain.\nThe density f and slot probabilities ry1,..., ryK are common knowledge.\nOnly bidder i knows the realization xi of its value per click Xi.\nBoth bidder i and the seller know the realization αi of Ai, but this realization remains unobservable to the other bidders.\nWe assume that bidders have quasi-linear utility functions.\nThat is, the expected utility to bidder i of obtaining the slot of rank j at a price of b per click is ui (j, b) = ryj αi (xi − b) If the advertising firms bidding in the slot auction are riskneutral and have ample liquidity, quasi-linearity is a reasonable assumption.\nThe assumptions of independence, symmetry, and riskneutrality made above are all quite standard in single-item auction theory [11, 19].\nThe assumption that clickthrough rate decays monotonically with lower slots--by the same factors for each agent--is unique to the slot auction problem.\nWe view it as a main contribution of our work to show that this assumption allows for tractable analysis of the slot auction problem using standard tools from singleitem auction theory.\nIt also allows for interesting results in the complete information case.\nA common model of decaying clickthrough rate is the exponential decay model, where ryk = δk − 1 with decay δ> 1.\nFeng et al. [7] state that\ntheir actual clickthrough data is fitted extremely well by an exponential decay model with δ = 1.428.\nOur model lacks budget constraints, which are an important feature of real slot auctions.\nWith budget constraints keyword auctions cannot be considered independently of one another, because the budget must be allocated across multiple keywords--a single advertiser typically bids on multiple keywords relevant to his business.\nIntroducing this element into the model is an important next step for future work .10\n2.2 Auction Formats\nIn a slot auction a bidder provides to the seller a declared value per click ˜xi (xi, αi) which depends on his true value and relevance.\nWe often denote this declared value (bid) by ˜xi for short.\nSince a bidder's relevance αi is observable to the seller, the bidder cannot misrepresent it.\nWe denote the kth highest of the N declared values by ˜x (k), and the kth highest of the N declared revenues by ˜r (k), where the declared revenue of bidder i is ˜ri = αi˜xi.\nWe consider two types of allocation rules, \"rank by bid\" (RBB) and \"rank by revenue\" (RBR): 10Models with budget constraints have begun to appear in this research area.\nAbrams [1] and Borgs et al. [3] design multi-unit auctions for budget-constrained bidders, which can be interpreted as slot auctions, with a focus on revenue optimization and truthfulness.\nMehta et al. [14] address the problem of matching user queries to budget-constrained advertisers so as to maximize revenue.\nRBB.\nSlot k goes to bidder i if and only if ˜xi = ˜x (k).\nRBR.\nSlot k goes to bidder i if and only if ˜ri = ˜r (k).\nWe will commonly represent an allocation by a one-to-one function σ: [K]--+ [N], where [n] is the set of integers {1, 2,..., n}.\nHence slot k goes to bidder σ (k).\nWe also consider two different types of payment rules.\nNote that no matter what the payment rule, a bidder that is not allocated a slot will pay 0 since his listing cannot receive any clicks.\nFirst-price.\nThe bidder allocated slot k, namely σ (k), pays ˜xσ (k) per click under both the RBB and RBR allocation rules.\nSecond-price.\nIf k <N, bidder σ (k) pays ˜xσ (k +1) per click under the RBB rule, and pays ˜rσ (k +1) / ασ (k) per click under the RBR rule.\nIf k = N, bidder σ (k) pays 0 per click .11 Intuitively, a second-price payment rule sets a bidder's payment to the lowest bid it could have declared while maintaining the same ranking, given the allocation rule used.\nOverture introduced the first slot auction design in 1997, using a first-price RBB scheme.\nGoogle then followed in 2000 with a second-price RBR scheme.\nIn 2002, Overture (at this point acquired by Yahoo!) then switched to second pricing but still allocates using RBB.\nOne possible reason for the switch is given in Section 4.\nWe assume that ties are broken as follows in the event that two agents make the exact same bid or declare the same revenue.\nThere is a permutation of the agents κ: [N]--+ [N] that is fixed beforehand.\nIf the bids of agents i and j are tied, then agent i obtains a higher slot if and only if κ (i) <κ (j).\nThis is consistent with the practice in real slot auctions where ties are broken by the bidders' order of arrival.\n3.\nINCOMPLETE INFORMATION\n3.1 Incentives\n3.2 Equilibrium Analysis\n3.3 Efficiency\n3.4 Revenue\n4.\nCOMPLETE INFORMATION\n4.1 Equilibrium Analysis\n4.2 Efficiency\nPk ak\n5.\nCONCLUSIONS\n6.\nFUTURE WORK\nIntroducing budget constraints into the model is a natural next step for future work.\nThe complication here lies in the fact that budgets are often set for entire campaigns rather than single keywords.\nAssuming that the optimal choice of budget can be made independent of the choice of bid for a specific keyword, it can be shown that it is a dominant-strategy to report this optimal budget with one's bid.\nThe problem is then to ascertain that bids and budgets can indeed be optimized separately, or to find a plausible model where deriving equilibrium bids and budgets together is tractable.\nIdentifying a condition on the distribution over value and relevance that actually does yield a revenue ranking of RBB and RBR (such as correlation between value and relevance, perhaps) would yield a more satisfactory characterization of their relative revenue properties.\nPlacing bounds on the revenue obtained in a complete information equilibrium is also a relevant question.\nBecause the incomplete information case is such a close generalization of the most basic single-item auction model, it would be interesting to see which standard results from single-item auction theory (e.g. results with risk-averse bidders, an endogenous number of bidders, asymmetries, etc. .\n.)\nautomatically generalize and which do not, to fully understand the structural differences between single-item and slot auctions."},"lvl-4":{"kind":"string","value":"An Analysis of Alternative Slot Auction Designs for Sponsored Search\nABSTRACT\nBillions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for placement alongside web search results.\nSlots for ad listings are allocated via an auction-style mechanism where the higher a merchant bids, the more likely his ad is to appear above other ads on the page.\nIn this paper we analyze the incentive, efficiency, and revenue properties of two slot auction designs: \"rank by bid\" (RBB) and \"rank by revenue\" (RBR), which correspond to stylized versions of the mechanisms currently used by Yahoo! and Google, respectively.\nWe also consider first - and second-price payment rules together with each of these allocation rules, as both have been used historically.\nWe consider both the \"short-run\" incomplete information setting and the \"long-run\" complete information setting.\nWith incomplete information, neither RBB nor RBR are truthful with either first or second pricing.\nWe find that the informational requirements of RBB are much weaker than those of RBR, but that RBR is efficient whereas RBB is not.\nWe also show that no revenue ranking of RBB and RBR is possible given an arbitrary distribution over bidder values and relevance.\nWith complete information, we find that no equilibrium exists with first pricing using either RBB or RBR.\nWe show that there typically exists a multitude of equilibria with second pricing, and we bound the divergence of (economic) value in such equilibria from the value obtained assuming all merchants bid truthfully.\n1.\nINTRODUCTION\nPricewaterhouseCoopers estimates that 2004 industry-wide sponsored search revenues were $3.9 billion, or 40% of total Internet advertising revenues . '\nSponsored search is a form of advertising where merchants pay to appear alongside web search results.\n.)\nmay bid to to have their listings featured alongside the standard \"algorithmic\" search listings.\nBy convention, sponsored search advertisers generally pay per click, meaning that they pay only when a user clicks on their ad, and do not pay if their ad is displayed but not clicked.\nThough many people claim to systematically ignore sponsored search ads, Majestic Research reports that\nWe call each position in the list a slot.\nGenerally, advertisements that appear in a higher ranked slot (higher on the page) garner more attention and more clicks from users.\nThus, all else being equal, merchants generally prefer higher ranked slots to lower ranked slots.\nAs mentioned, bids are expressed as a maximum willingness to pay per click.\nFor example, a forty-cent bid by HostRocket for \"web hosting\" means HostRocket is willing to pay up to forty cents every time a user clicks on their ad .5 The auctioneer (the search engine6) evaluates the bids and allocates slots to advertisers.\nIn principle, the allocation decision can be altered with each new incoming search query, so in effect new auctions clear continuously over time as search queries arrive.\nMany allocation rules are plausible.\nIn this paper, we investigate two allocation rules, roughly corresponding to the two allocation rules used by Yahoo! and Google.\nThe \"rank by bid\" (RBB) allocation assigns slots in order of bids, with higher ranked slots going to higher bidders.\nThe \"rank by revenue\" (RBR) allocation assigns slots in order of the product of bid times expected relevance, where relevance is the proportion of users that click on the merchant's ad after viewing it.\nIn our model, we assume that an ad's expected relevance is known to the auctioneer and the advertiser (but not necessarily to other advertisers), and that clickthrough rate decays monotonically with lower ranked slots.\nengine are not always the same entity.\nFor example Google runs the sponsored search ads for AOL web search, with revenue being shared.\nSimilarly, Yahoo! currently runs the sponsored search ads for MSN web search, though Microsoft will begin independent operations soon.\ntion in subtle ways.\nFirst, a single bid per merchant is used to allocate multiple non-identical slots.\nSecond, the bid is communicated not as a direct preference over slots, but as a preference for clicks that depend stochastically on slot allocation.\nWe investigate a number of economic properties of RBB and RBR slot auctions.\nWe consider the \"short-run\" incomplete information case in Section 3, adapting and extending standard analyses of single-item auctions.\nIn Section 4 we turn to the \"long-run\" complete information case; our characterization results here draw on techniques from linear programming.\nThroughout, important observations are highlighted as claims supported by examples.\nOur contributions are as follows:\n• We show that with multiple slots, bidders do not reveal their true values with either RBB or RBR, and with either first - or second-pricing.\n• With incomplete information, we find that the informational requirements of playing the equilibrium bid are much weaker for RBB than for RBR, because bidders need not know any information about each others' relevance (or even their own) with RBB.\n• With incomplete information, we prove that RBR is efficient but that RBB is not.\n• We show via a simple example that no general revenue ranking of RBB and RBR is possible.\n• We prove that in a complete-information setting, firstprice slot auctions have no pure strategy Nash equilibrium, but that there always exists a pure-strategy equilibrium with second pricing.\n• We provide a constant-factor bound on the deviation from efficiency that can occur in the equilibrium of a second-price slot auction.\nIn Section 2 we specify our model of bidders and the various slot auction formats.\nIn Section 3.1 we study the incentive properties of each format, asking in which cases agents would bid truthfully.\nThis is useful for the efficiency and revenue results in later sections.\nIt should become clear in this section that slot auctions in our model are a straightforward generalization of single-item auctions.\nSections 3.3 and 3.4 address questions of efficiency and revenue under incomplete information, respectively.\nIn Section 4.1 we determine whether pure-strategy equilibria exist for the various auction formats, under complete information.\nIn Section 4.2 we derive bounds on the deviation from efficiency in the pure-strategy equilibria of secondprice slot auctions.\nOur approach is positive rather than normative.\nWe aim to clarify the incentive, efficiency, and revenue properties of two slot auction designs currently in use, under settings of\nincomplete and complete information.\nWe do not attempt to derive the \"optimal\" mechanism for a slot auction.\nRelated work.\nFeng et al. [7] compare the revenue performance of various ranking mechanisms for slot auctions in a model with incomplete information, much as we do in Section 3.4, but they obtain their results via simulations whereas we perform an equilibrium analysis.\nLiu and Chen [12] study properties of slot auctions under incomplete information.\nTheir setting is essentially the same as ours, except they restrict their attention to a model with a single slot and a binary type for bidder relevance (high or low).\nThey find that RBR is efficient, but that no general revenue ranking of RBB and RBR is possible, which agrees with our results.\nThey also take a design approach and show how the auctioneer should assign relevance scores to optimize its revenue.\nEdelman et al. [6] model the slot auction problem both as a static game of complete information and a dynamic game of incomplete information.\nThey do not view slot auctions as static games of incomplete information as we do, but do study them as dynamic games of incomplete information and derive results on the uniqueness and revenue properties of the resulting equilibria.\nThey also provide a nice description of the evolution of the market for sponsored search.\nVarian [18] also studies slot auctions under a setting of complete information.\nHe focuses on \"symmetric\" equilibria, which are a refinement of Nash equilibria appropriate for slot auctions.\nHe provides bounds on the revenue obtained in equilibrium.\nHe also gives bounds that can be used to infer bidder values given their bids, and performs some empirical analysis using these results.\nIn contrast, we focus instead on efficiency and provide bounds on the deviation from efficiency in complete-information equilibria.\n2.\nPRELIMINARIES\nWe focus on a slot auction for a single keyword.\nIn a setting of incomplete information, a bidder knows only distributions over others' private information (value per click and relevance).\nWith complete information, a bidder knows others' private information, and so does not need to rely on distributions to strategize.\nWe first describe the model for the case with incomplete information, and drop the distributional information from the model when we come to the complete-information case in Section 4.\n2.1 The Model\nThere is a fixed number K of slots to be allocated among N bidders.\nWe assume without loss of generality that K ≤ N, since superfluous slots can remain blank.\nBidder i assigns a value of Xi to each click received on its advertisement, regardless of this advertisement's rank .9 The probability that i's advertisement will be clicked if viewed is Ai ∈ [0, 1].\nWe refer to Ai as bidder i's relevance.\nWe refer to Ri = AiXi as bidder i's revenue.\nHowever, for the purposes of our model it is also important that bidders believe that conversion rate does not vary with rank.\nThe probability that an advertisement will be viewed if placed in slot j is ryj ∈ [0, 1].\nWe assume ry1> rye>...> ryK.\nHence bidder i's advertisement will have a clickthrough rate of ryjαi if placed in slot j. Of course, an advertisement does not receive any clicks if it is not allocated a slot.\nThe density f and slot probabilities ry1,..., ryK are common knowledge.\nOnly bidder i knows the realization xi of its value per click Xi.\nBoth bidder i and the seller know the realization αi of Ai, but this realization remains unobservable to the other bidders.\nWe assume that bidders have quasi-linear utility functions.\nThat is, the expected utility to bidder i of obtaining the slot of rank j at a price of b per click is ui (j, b) = ryj αi (xi − b) If the advertising firms bidding in the slot auction are riskneutral and have ample liquidity, quasi-linearity is a reasonable assumption.\nThe assumptions of independence, symmetry, and riskneutrality made above are all quite standard in single-item auction theory [11, 19].\nThe assumption that clickthrough rate decays monotonically with lower slots--by the same factors for each agent--is unique to the slot auction problem.\nWe view it as a main contribution of our work to show that this assumption allows for tractable analysis of the slot auction problem using standard tools from singleitem auction theory.\nIt also allows for interesting results in the complete information case.\nA common model of decaying clickthrough rate is the exponential decay model, where ryk = δk − 1 with decay δ> 1.\ntheir actual clickthrough data is fitted extremely well by an exponential decay model with δ = 1.428.\nOur model lacks budget constraints, which are an important feature of real slot auctions.\nWith budget constraints keyword auctions cannot be considered independently of one another, because the budget must be allocated across multiple keywords--a single advertiser typically bids on multiple keywords relevant to his business.\nIntroducing this element into the model is an important next step for future work .10\n2.2 Auction Formats\nIn a slot auction a bidder provides to the seller a declared value per click ˜xi (xi, αi) which depends on his true value and relevance.\nWe often denote this declared value (bid) by ˜xi for short.\nSince a bidder's relevance αi is observable to the seller, the bidder cannot misrepresent it.\nWe denote the kth highest of the N declared values by ˜x (k), and the kth highest of the N declared revenues by ˜r (k), where the declared revenue of bidder i is ˜ri = αi˜xi.\nWe consider two types of allocation rules, \"rank by bid\" (RBB) and \"rank by revenue\" (RBR): 10Models with budget constraints have begun to appear in this research area.\nAbrams [1] and Borgs et al. [3] design multi-unit auctions for budget-constrained bidders, which can be interpreted as slot auctions, with a focus on revenue optimization and truthfulness.\nMehta et al. [14] address the problem of matching user queries to budget-constrained advertisers so as to maximize revenue.\nRBB.\nSlot k goes to bidder i if and only if ˜xi = ˜x (k).\nRBR.\nSlot k goes to bidder i if and only if ˜ri = ˜r (k).\nHence slot k goes to bidder σ (k).\nWe also consider two different types of payment rules.\nNote that no matter what the payment rule, a bidder that is not allocated a slot will pay 0 since his listing cannot receive any clicks.\nFirst-price.\nThe bidder allocated slot k, namely σ (k), pays ˜xσ (k) per click under both the RBB and RBR allocation rules.\nSecond-price.\nIf k <N, bidder σ (k) pays ˜xσ (k +1) per click under the RBB rule, and pays ˜rσ (k +1) / ασ (k) per click under the RBR rule.\nIf k = N, bidder σ (k) pays 0 per click .11 Intuitively, a second-price payment rule sets a bidder's payment to the lowest bid it could have declared while maintaining the same ranking, given the allocation rule used.\nOverture introduced the first slot auction design in 1997, using a first-price RBB scheme.\nGoogle then followed in 2000 with a second-price RBR scheme.\nIn 2002, Overture (at this point acquired by Yahoo!) then switched to second pricing but still allocates using RBB.\nOne possible reason for the switch is given in Section 4.\nWe assume that ties are broken as follows in the event that two agents make the exact same bid or declare the same revenue.\nIf the bids of agents i and j are tied, then agent i obtains a higher slot if and only if κ (i) <κ (j).\nThis is consistent with the practice in real slot auctions where ties are broken by the bidders' order of arrival.\n6.\nFUTURE WORK\nIntroducing budget constraints into the model is a natural next step for future work.\nThe complication here lies in the fact that budgets are often set for entire campaigns rather than single keywords.\nThe problem is then to ascertain that bids and budgets can indeed be optimized separately, or to find a plausible model where deriving equilibrium bids and budgets together is tractable.\nPlacing bounds on the revenue obtained in a complete information equilibrium is also a relevant question.\nBecause the incomplete information case is such a close generalization of the most basic single-item auction model, it would be interesting to see which standard results from single-item auction theory (e.g. results with risk-averse bidders, an endogenous number of bidders, asymmetries, etc. .\n.)\nautomatically generalize and which do not, to fully understand the structural differences between single-item and slot auctions."}}},{"rowIdx":96,"cells":{"id":{"kind":"string","value":"J-55"},"title":{"kind":"string","value":"From Optimal Limited To Unlimited Supply Auctions"},"abstract":{"kind":"string","value":"We investigate the class of single-round, sealed-bid auctions for a set of identical items to bidders who each desire one unit. We adopt the worst-case competitive framework defined by [9, 5] that compares the profit of an auction to that of an optimal single-price sale of least two items. In this paper, we first derive an optimal auction for three items, answering an open question from [8]. Second, we show that the form of this auction is independent of the competitive framework used. Third, we propose a schema for converting a given limited-supply auction into an unlimited supply auction. Applying this technique to our optimal auction for three items, we achieve an auction with a competitive ratio of 3.25, which improves upon the previously best-known competitive ratio of 3.39 from [7]. Finally, we generalize a result from [8] and extend our understanding of the nature of the optimal competitive auction by showing that the optimal competitive auction occasionally offers prices that are higher than all bid values."},"keyphrases":{"kind":"list like","value":["unlimit suppli","auction","ratio","mechan design","competit analysi","benchmark","aggreg auction","bound","prefer","distribut"],"string":"[\n \"unlimit suppli\",\n \"auction\",\n \"ratio\",\n \"mechan design\",\n \"competit analysi\",\n \"benchmark\",\n \"aggreg auction\",\n \"bound\",\n \"prefer\",\n \"distribut\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","U","M","U","M","U","U","U"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"U\",\n \"M\",\n \"U\",\n \"M\",\n \"U\",\n \"U\",\n \"U\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"From Optimal Limited To Unlimited Supply Auctions Jason D. Hartline Microsoft Research 1065 La Avenida) Mountain View, CA 94043 hartline@microsoft.com Robert McGrew ∗ Computer Science Department Stanford University Stanford, CA 94305 bmcgrew@stanford.edu ABSTRACT We investigate the class of single-round, sealed-bid auctions for a set of identical items to bidders who each desire one unit.\nWe adopt the worst-case competitive framework defined by [9, 5] that compares the profit of an auction to that of an optimal single-price sale of least two items.\nIn this paper, we first derive an optimal auction for three items, answering an open question from [8].\nSecond, we show that the form of this auction is independent of the competitive framework used.\nThird, we propose a schema for converting a given limited-supply auction into an unlimited supply auction.\nApplying this technique to our optimal auction for three items, we achieve an auction with a competitive ratio of 3.25, which improves upon the previously best-known competitive ratio of 3.39 from [7].\nFinally, we generalize a result from [8] and extend our understanding of the nature of the optimal competitive auction by showing that the optimal competitive auction occasionally offers prices that are higher than all bid values.\nCategories and Subject Descriptors F.2.2 [Analysis of Algorithms and Problem Complexity]: Nonnumerical Algorithms and Problems; J.4 [Social and Behavioral Sciences]: Economics General Terms Algorithms, Economics, Theory 1.\nINTRODUCTION The research area of optimal mechanism design looks at designing a mechanism to produce the most desirable outcome for the entity running the mechanism.\nThis problem is well studied for the auction design problem where the optimal mechanism is the one that brings the seller the most profit.\nHere, the classical approach is to design such a mechanism given the prior distribution from which the bidders'' preferences are drawn (See e.g., [12, 4]).\nRecently Goldberg et al. [9] introduced the use of worst-case competitive analysis (See e.g., [3]) to analyze the performance of auctions that have no knowledge of the prior distribution.\nThe goal of such work is to design an auction that achieves a large constant fraction of the profit attainable by an optimal mechanism that knows the prior distribution in advance.\nPositive results in this direction are fueled by the observation that in auctions for a number of identical units, much of the distribution from which the bidders are drawn can be deduced on the fly by the auction as it is being run [9, 14, 2].\nThe performance of an auction in such a worst-case competitive analysis is measured by its competitive ratio, the ratio between a benchmark performance and the auction``s performance on the input distribution that maximizes this ratio.\nThe holy grail of the worstcase competitive analysis of auctions is the auction that achieves the optimal competitive ratio (as small as possible).\nSince [9] this search has led to improved understanding of the nature of the optimal auction, the techniques for on-the-fly pricing in these scenarios, and the competitive ratio of the optimal auction [5, 7, 8].\nIn this paper we continue this line of research by improving in all of these directions.\nFurthermore, we give evidence corroborating the conjecture that the form of the optimal auction is independent of the benchmark used in the auction``s competitive analysis.\nThis result further validates the use of competitive analysis in gauging auction performance.\nWe consider the single item, multi-unit, unit-demand auction problem.\nIn such an auction there are many units of a single item available for sale to bidders who each desire only one unit.\nEach bidder has a valuation representing how much the item is worth to him.\nThe auction is performed by soliciting a sealed bid from each of the bidders and deciding on the allocation of units to bidders and the prices to be paid by the bidders.\nThe bidders are assumed to bid so as to maximize their personal utility, the difference between their valuation and the price they pay.\nTo handle the problem of designing and analyzing auctions where bidders may falsely declare their valuations to get a better deal, we will adopt the solution concept of truthful mechanism design (see, e.g., [9, 15, 13]).\nIn a truthful auction, revealing one``s true valuation as one``s bid is an optimal strategy for each bidder regardless of the bids of the other bidders.\nIn this paper, we will restrict our attention to truthful (a.k.a., incentive compatible or strategyproof) auctions.\nA particularly interesting special case of the auction problem is the unlimited supply case.\nIn this case the number of units for sale is at least the number of bidders in the auction.\nThis is natural for the sale of digital goods where there is negligible cost for duplicating 175 and distributing the good.\nPay-per-view television and downloadable audio files are examples of such goods.\nThe competitive framework introduced in [9] and further refined in [5] uses the profit of the optimal omniscient single priced mechanism that sells at least two units as the benchmark for competitive analysis.\nThe assumption that two or more units are sold is necessary because in the worst case it is impossible to obtain a constant fraction of the profit of the optimal mechanism when it sells only one unit [9].\nIn this framework for competitive analysis, an auction is said to be β-competitive if it achieves a profit that is within a factor of β ≥ 1 of the benchmark profit on every input.\nThe optimal auction is the one which is β-competitive with the minimum value of β.\nPrevious to this work, the best known auction for the unlimited supply case had a competitive ratio of 3.39 [7] and the best lower bound known was 2.42 [8].\nFor the limited supply case, auctions can achieve substantially better competitive ratios.\nWhen there are only two units for sale, the optimal auction gives a competitive ratio of 2, which matches the lower bound for two units.\nWhen there are three units for sale, the best previously known auction had a competitive ratio of 2.3, compared with a lower bound of 13/6 ≈ 2.17 [8].\nThe results of this paper are as follows: • We give the auction for three units that is optimally competitive against the profit of the omniscient single priced mechanism that sells at least two units.\nThis auction achieves a competitive ratio of 13/6, matching the lower bound from [8] (Section 3).\n• We show that the form of the optimal auction is independent of the benchmark used in competitive analysis.\nIn doing so, we give an optimal three bidder auction for generalized benchmarks (Section 4).\n• We give a general technique for converting a limited supply auction into an unlimited supply auction where it is possible to use the competitive ratio of the limited supply auction to obtain a bound on the competitive ratio of the unlimited supply auction.\nWe refer to auctions derived from this framework as aggregation auctions (Section 5).\n• We improve on the best known competitive ratio by proving that the aggregation auction constructed from our optimal three-unit auction is 3.25-competitive (Section 5.1).\n• Assuming that the conjecture that the optimal -unit auction has a competitive ratio that matches the lower bound proved in [8], we show that this optimal auction for ≥ 3 on some inputs will occasionally offer prices that are higher than any bid in that input (Section 6).\nFor the three-unit case where we have shown that the lower bound of [8] is tight, this observation led to our construction of the optimal three-unit auction.\n2.\nDEFINITIONS AND BACKGROUND We consider single-round, sealed-bid auctions for a set of identical units of an item to bidders who each desire one unit.\nAs mentioned in the introduction, we adopt the game-theoretic solution concept of truthful mechanism design.\nA useful simplification of the problem of designing truthful auctions is obtained through the following algorithmic characterization [9].\nRelated formulations to this one have appeared in numerous places in recent literature (e.g., [1, 14, 5, 10]).\nDEFINITION 1.\nGiven a bid vector of n bids, b = (b1, ... , bn), let b-i denote the vector of with bi replaced with a `?''\n, i.e., b-i = (b1, ... , bi−1, ?\n, bi+1, ... , bn).\nDEFINITION 2.\nLet f be a function from bid vectors (with a `?'')\nto prices (non-negative real numbers).\nThe deterministic bidindependent auction defined by f, BIf , works as follows.\nFor each bidder i: 1.\nSet ti = f(b-i).\n2.\nIf ti < bi, bidder i wins at price ti.\n3.\nIf ti > bi, bidder i loses.\n4.\nOtherwise, (ti = bi) the auction can either accept the bid at price ti or reject it.\nA randomized bid-independent auction is a distribution over deterministic bid-independent auctions.\nThe proof of the following theorem can be found, for example, in [5].\nTHEOREM 1.\nAn auction is truthful if and only if it is equivalent to a bid-independent auction.\nGiven this equivalence, we will use the the terminology bidindependent and truthful interchangeably.\nFor a randomized bid-independent auction, f(b-i) is a random variable.\nWe denote the probability density of f(b-i) at z by ρb-i (z).\nWe denote the profit of a truthful auction A on input b as A(b).\nThe expected profit of the auction, E[A(b)], is the sum of the expected payments made by each bidder, which we denote by pi(b) for bidder i. Clearly, the expected payment of each bid satisfies pi(b) = bi 0 xρb-i (x)dx.\n2.1 Competitive Framework We now review the competitive framework from [5].\nIn order to evaluate the performance of auctions with respect to the goal of profit maximization, we introduce the optimal single price omniscient auction F and the related omniscient auction F(2) .\nDEFINITION 3.\nGive a vector b = (b1, ... , bn), let b(i) represent the i-th largest value in b.\nThe optimal single price omniscient auction, F, is defined as follows.\nAuction F on input b determines the value k such that kb(k) is maximized.\nAll bidders with bi ≥ b(k) win at price b(k); all remaining bidders lose.\nThe profit of F on input b is thus F(b) = max1≤k≤n kb(k).\nIn the competitive framework of [5] and subsequent papers, the performance of a truthful auction is gauged in comparison to F(2) , the optimal singled priced auction that sells at least two units.\nThe profit of F(2) is max2≤k≤n kb(k) There are a number of reasons to choose this benchmark for comparison, interested readers should see [5] or [6] for a more detailed discussion.\nLet A be a truthful auction.\nWe say that A is β-competitive against F(2) (or just β-competitive) if for all bid vectors b, the expected profit of A on b satisfies E[A(b)] ≥ F(2) (b) β .\nIn Section 4 we generalize this framework to other profit benchmarks.\n176 2.2 Scale Invariant and Symmetric Auctions A symmetric auction is one where the auction outcome is unchanged when the input bids arrive in a different permutation.\nGoldberg et al. [8] show that a symmetric auction achieves the optimal competitive ratio.\nThis is natural as the profit benchmark we consider is symmetric, and it allows us to consider only symmetric auctions when looking for the one with the optimal competitive ratio.\nAn auction defined by bid-independent function f is scale invariant if, for all i and all z, Pr[f(b-i) ≥ z] = Pr[f(cb-i) ≥ cz].\nIt is conjectured that the assumption of scale invariance is without loss of generality.\nThus, we are motivated to consider symmetric scale-invariant auctions.\nWhen specifying a symmetric scaleinvariant auction we can assume that f is only a function of the relative magnitudes of the n − 1 bids in b-i and that one of the bids, bj = 1.\nIt will be convenient to specify such auctions via the density function of f(b-i), ρb-i (z).\nIt is enough to specify such a density function of the form ρ1,z1,...,zn−1 (z) with 1 ≤ zi ≤ zi+1.\n2.3 Limited Supply Versus Unlimited Supply Following [8], throughout the remainder of this paper we will be making the assumption that n = , i.e., the number of bidders is equal to the number of units for sale.\nThis is without loss of generality as (a) any lower bound that applies to the n = case also extends to the case where n ≥ [8], and (b) there is a reduction from the unlimited supply auction problem to the limited supply auction problem that takes an unlimited supply auction that is β-competitive with F(2) and constructs a limited supply auction parameterized by that is β-competitive with F(2, ) , the optimal omniscient auction that sells between 2 and units [6].\nHenceforth, we will assume that we are in the unlimited supply case, and we will examine lower bounds for limited supply problems by placing a restriction on the number of bidders in the auction.\n2.4 Lower Bounds and Optimal Auctions Frequently in this paper, we will refer to the best known lower bound on the competitive ratio of truthful auctions: THEOREM 2.\n[8] The competitive ratio of any auction on n bidders is at least 1 − n i=2 −1 n i−1 i i − 1 n − 1 i − 1 .\nDEFINITION 4.\nLet Υn denote the n-bidder auction that achieves the optimal competitive ratio.\nThis bound is derived by analyzing the performance of any auction on the following distribution B.\nIn each random bid vector B, each bid Bi is drawn i.i.d. from the distribution such that Pr[Bi ≥ s] ≤ 1/s for all s ∈ S.\nIn the two-bidder case, this lower bound is 2.\nThis is achieved by Υ2 which is the 1-unit Vickrey auction.1 In the three-bidder case, this lower bound is 13/6.\nIn the next section, we define the auction Υ3 which matches this lower bound.\nIn the four-bidder case, this lower bound is 96/215.\nIn the limit as the number of bidders grows, this lower bound approaches a number which is approximately 2.42.\nIt is conjectured that this lower bound is tight for any number of bidders and the optimal auction, Υn, matches it.\n1 The 1-unit Vickrey auction sells to the highest bidder at the second highest bid value.\n2.5 Profit Extraction In this section we review the truthful profit extraction mechanism ProfitExtractR.\nThis mechanism is a special case of a general cost-sharing schema due to Moulin and Shenker [11].\nThe goal of profit extraction is, given bids b, to extract a target value R of profit from some subset of the bidders.\nProfitExtractR: Given bids b, find the largest k such that the highest k bidders can equally share the cost R. Charge each of these bidders R/k.\nIf no subset of bidders can cover the cost, the mechanism has no winners.\nImportant properties of this auction are as follows: • ProfitExtractR is truthful.\n• If R ≤ F(b), ProfitExtractR(b) = R; otherwise it has no winners and no revenue.\nWe will use this profit extraction mechanism in Section 5 with the following intuition.\nSuch a profit extractor makes it possible to treat this subset of bidders as a single bid with value F(S).\nNote that given a single bid, b, a truthful mechanism might offer it price t and if t ≤ b then the bidder wins and pays t; otherwise the bidder pays nothing (and loses).\nLikewise, a mechanism can offer the set of bidders S a target revenue R.\nIf R ≤ F(2) (S), then ProfitExtractR raises R from S; otherwise, the it raises no revenue from S. 3.\nAN OPTIMAL AUCTION FOR THREE BIDDERS In this section we define the optimal auction for three bidders, Υ3, and prove that it indeed matches the known lower bound of 13/6.\nWe follow the definition and proof with a discussion of how this auction was derived.\nDEFINITION 5.\nΥ3 is scale-invariant and symmetric and given by the bid-independent function with density function ρ1,x(z) = ⎧ ⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩ For x ≤ 3/2 1 with probability 9/13 z with probability density g(z) for z > 3/2 For x > 3/2⎧ ⎪⎨ ⎪⎩ 1 with probability 9/13 − x 3/2 zg(z)dz x with probability x 3/2 (z + 1)g(z)dz z with probability density g(z) for z > x where g(x) = 2/13 (x−1)3 .\nTHEOREM 3.\nThe Υ3 auction has a competitive ratio of 13/6 ≈ 2.17, which is optimal.\nFurthermore, the auction raises exactly 6 13 F(2) on every input with non-identical bids.\nPROOF.\nConsider the bids 1, x, y, with 1 < x < y.\nThere are three cases.\nCASE 1 (x < y ≤ 3/2): F(2) = 3.\nThe auction must raise expected revenue of at least 18/13 on these bids.\nThe bidder with valuation x will pay 1 with 9/13, and the bidder with valuation y will pay 1 with probability 9/13.\nTherefore Υ3 raises 18/13 on these bids.\nCASE 2 (x ≤ 3/2 < y): F(2) = 3.\nThe auction must raise expected revenue of at least 18/13 on these bids.\nThe bidder with 177 valuation x will pay 9/13 − y 3/2 zg(z)dz in expectation.\nThe bidder with valuation y will pay 9/13 + y 3/2 zg(z)dz in expectation.\nTherefore Υ3 raises 18/13 on these bids.\nCASE 3 (3/2 < x ≤ y): F(2) = 2x.\nThe auction must raise expected revenue of at least 12x/13 on these bids.\nConsider the revenue raised from all three bidders: E[Υ3(b)] = p(1, x, y) + p(x, 1, y) + p(y, 1, x) = 0 + 9/13 − y 3/2 zg(z)dz + 9/13 − x 3/2 zg(z)dz + x x 3/2 (z + 1)g(z)dz + y x zg(z)dz = 18/13 + (x − 2) x 3/2 zg(z)dz + x x 3/2 g(z)dz = 12x/13.\nThe final equation comes from substituting in g(x) = 2/13 (x−1)3 and expanding the integrals.\nNote that the fraction of F(2) raised on every input is identical.\nIf any of the inequalities 1 ≤ x ≤ y are not strict, the same proof applies giving a lower bound on the auction``s profit; however, this bound may no longer be tight.\nMotivation for Υ3 In this section, we will conjecture that a particular input distribution is worst-case, and show, as a consequence, that all inputs are worstcase in the optimal auction.\nBy applying this consequence, we will derive an optimal auction for three bidders.\nA truthful, randomized auction on n bidders can be represented by a randomized function f : Rn−1 × n → R that maps masked bid vectors to prices in R. By normalization, we can assume that the lowest possible bid is 1.\nRecall that ρb-i (z) = Pr[f(b-i) = z].\nThe optimal auction for the finite auction problem can be found by the following optimization problem in which the variables are ρb-i (z): maximize r subject to n i=1 bi z=1 zρb-i (z)dz ≥ rF(2) (b) ∞ z=1 ρb-i (z)dz = 1 ρb-i (z) ≥ 0 This set of integral inequalities is difficult to maximize over.\nHowever, by guessing which constraints are tight and which are slack at the optimum, we will be able to derive a set of differential equations for which any feasible solution is an optimal auction.\nAs we discuss in Section 2.4, in [8], the authors define a distribution and use it to find a lower bound on the competitive ratio of the optimal auction.\nFor two bidders, this bid distribution is the worst-case input distribution.\nWe guess (and later verify) that this distribution is the worst-case input distribution for three bidders as well.\nSince this distribution has full support over the set of all bid vectors and a worst-case distribution puts positive probability only on worst-case inputs, we can therefore assume that all but a measure zero set of inputs is worst-case for the optimal auction.\nIn the optimal two-bidder auction, all inputs with non-identical bids are worst-case, so we will assume the same for three bidders.\nThe guess that these constraints are tight allows us to transform the optimization problem into a feasibility problem constrained by differential equations.\nIf the solution to these equations has value matching the lower bound obtained from the worst-case distribution, then this solution is the optimal auction and that our conjectured choice of worst-case distribution is correct.\nIn Section 6 we show that the optimal auction must sometimes place probability mass on sale prices above the highest bid.\nThis motivates considering symmetric scale-invariant auctions for three bidders with probability density function, ρ1,x(z), of the following form: ρ1,x(z) = ⎧ ⎪⎨ ⎪⎩ 1 with discrete probability a(x) x with discrete probability b(x) z with probability density g(z) for z > x In this auction, the sale price for the first bidder is either one of the latter two bids, or higher than either bid with a probability density which is independent of the input.\nThe feasibility problem which arises from the linear optimization problem by assuming the constraints are tight is as follows: a(y) + a(x) + xb(x) + y x zg(z)dz = r max(3, 2x) ∀x < y a(x) + b(x) + ∞ x g(z)dz = 1 a(x) ≥ 0 b(x) ≥ 0 g(z) ≥ 0 Solving this feasibility problem gives the auction Υ3 proposed above.\nThe proof of its optimality validates its proposed form.\nFinding a simple restriction on the form of n-bidder auctions for n > 3 under which the optimal auction can be found analytically as above remains an open problem.\n4.\nGENERALIZED PROFIT BENCHMARKS In this section, we widen our focus beyond auctions that compete with F(2) to consider other benchmarks for an auction``s profit.\nWe will show that, for three bidders, the form of the optimal auction is essentially independent of the benchmark profit used.\nThis results strongly corroborates the worst-case competitive analysis of auctions by showing that our techniques allow us to derive auctions which are competitive against a broad variety of reasonable benchmarks rather than simply against F(2) .\nPrevious work in competitive analysis of auctions has focused on the question of designing the auction with the best competitive ratio against F(2) , the profit of the optimal omniscient single-priced mechanism that sells at least two items.\nHowever, it is reasonable to consider other benchmarks.\nFor instance, one might wish to compete against V∗ , the profit of the k-Vickrey auction with optimal-inhindsight choice of k.2 Alternatively, if an auction is being used as a subroutine in a larger mechanism, one might wish to choose the auction which is optimally competitive with a benchmark specific to that purpose.\nRecall that F(2) (b) = max2≥k≥n kb(k).\nWe can generalize this definition to Gs, parameterized by s = (s2, ... , sn) and defined as: Gs(b) = max 2≤k≤n skb(k).\nWhen considering Gs we assume without loss of generality that si < si+1 as otherwise the constraint imposed by si+1 is irrelevant.\nNote that F(2) is the special case of Gs with si = i, and that V∗ = Gs with si = i − 1.\n2 Recall that the k-Vickrey auction sells a unit to each of the highest k bidders at a price equal to the k + 1st highest bid, b(k+1), achieving a profit of kb(k+1).\n178 Competing with Gs We will now design a three-bidder auction Υs,t 3 that achieves the optimal competitive ratio against Gs,t.\nAs before, we will first find a lower bound on the competitive ratio and then design an auction to meet that bound.\nWe can lower bound the competitive ratio of Υs,t 3 using the same worst-case distribution from [8] that we used against F(2) .\nEvaluating the performance of any auction competing against Gs,t on this distribution will yield the following theorem.\nWe denote the optimal auction for three bidders against Gs,t as Υs,t 3 .\nTHEOREM 4.\nThe optimal three-bidder auction, Υs,t 3 , competing against Gs,t(b) = max(sb(2), tb(3)) has a competitive ratio of at least s2 +t2 2t .\nThe proof can be found in the appendix.\nSimilarly, we can find the optimal auction against Gs,t using the same technique we used to solve for the three bidder auction with the best competitive ratio against F(2) .\nDEFINITION 6.\nΥs,t 3 is scale-invariant and symmetric and given by the bid-independent function with density function ρ1,x(z) = ⎧ ⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨ ⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩ For x ≤ t s 1 with probability t2 s2+t2 z with probability density g(z) for z > t s For x > t s⎧ ⎪⎪⎨ ⎪⎪⎩ 1 with probability t2 s2+t2 − x t s zg(z)dz x with probability x t s (z + 1)g(z)dz z with probability density g(z) for z > x where g(x) = 2(t−s)2 /(s2 +t2 ) (x−1)3 .\nTHEOREM 5.\nΥs,t 3 is s2 +t2 2t -competitive with Gs,t.\nThis auction, like Υ3, can be derived by reducing the optimization problem to a feasibility problem, guessing that the optimal solution has the same form as Υs,t 3 , and solving.\nThe auction is optimal because it matches the lower bound found above.\nNote that the form of Υs,t 3 is essentially the same as for Υ3, but that the probability of each price is scaled depending on the values of s and t.\nThat our auction for three bidders matches the lower bound computed by the input distribution used in [8] is strong evidence that this input distribution is the worst-case input distribution for any number of bidders and any generalized profit benchmark.\nFurthermore, we strongly suspect that for any number of bidders, the form of the optimal auction will be independent of the benchmark used.\n5.\nAGGREGATION AUCTIONS We have seen that optimal auctions for small cases of the limitedsupply model can be found analytically.\nIn this section, we will construct a schema for turning limited supply auctions into unlimited supply auctions with a good competitive ratio.\nAs discussed in Section 2.5, the existence of a profit extractor, ProfitExtractR, allows an auction to treat a set of bids S as a single bid with value F(S).\nGiven n bidders and an auction, Am, for m < n bidders, we can convert the m-bidder auction into an n-bidder auction by randomly partitioning the bidders into m subsets and then treating each subset as a single bidder (via ProfitExtractR) and running the m-bidder auction.\nDEFINITION 7.\nGiven a truthful m-bidder auction, Am, the m-aggregation auction for Am, AggAm , works as follows: 1.\nCast each bid uniformly at random into one of m bins, resulting in bid vectors b(1) , ... , b(m) .\n2.\nFor each bin j, compute the aggregate bid Bj = F(b(j) ).\nLet B be the vector of aggregate bids, and B−j be the aggregate bids for all bins other than j. 3.\nCompute the aggregate price Tj = f(B−j), where f is the bid-independent function for Am.\n4.\nFor each bin j, run ProfitExtractTj on b(j) .\nSince Am and ProfitExtractR are truthful, Tj is computed independently of any bid in bin j and thus the price offered any bidder in b(j) is independent of his bid; therefore, THEOREM 6.\nIf Am is truthful, the m-aggregation auction for Am, AggAm , is truthful.\nNote that this schema yields a new way of understanding the Random Sampling Profit Extraction (RSPE) auction [5] as the simplest case of an aggregation auction.\nIt is the 2-aggregation auction for Υ2, the 1-unit Vickrey auction.\nTo analyze AggAm , consider throwing k balls into m labeled bins.\nLet k represent a configuration of balls in bins, so that ki is equal to the number of balls in bin i, and k(i) is equal to the number of balls in the ith largest bin.\nLet Km,k represent the set of all possible configurations of k balls in m bins.\nWe write the multinomial coefficient of k as k k .\nThe probability that a particular configuration k arises by throwing balls into bins uniformly at random is k k m−k .\nTHEOREM 7.\nLet Am be an auction with competitive ratio β.\nThen the m-aggregation auction for Am, AggAm , raises the following fraction of the optimal revenue F(2) (b): E AggAm (b) F(2) ≥ min k≥2 k∈Km,k F(2) (k) k k βkmk PROOF.\nBy definition, F(2) sells to k ≥ 2 bidders at a single price p. Let kj be the number of such bidders in b(j) .\nClearly, F(b(j) ) ≥ pkj.\nTherefore, F(2) (F(b(1) ), ... , F(b(n) )) F(2)(b) ≥ F(2) (pk1, ... , pkn) pk = F(2) (k1, ... , kn) k The inequality follows from the monotonicity of F(2) , and the equality from the homogeneity of F(2) .\nProfitExtractTj will raise Tj if Tj ≤ Bj , and no profit otherwise.\nThus, E AggAm (b) ≥ E F(2) (B)/β .\nThe theorem follows by rewriting this expectation as a sum over all k in Km,k. 5.1 A 3.25 Competitive Auction We apply the aggregation auction schema to Υ3, our optimal auction for three bidders, to achieve an auction with competitive ratio 3.25.\nThis improves on the previously best known auction which is 3.39-competitive [7].\nTHEOREM 8.\nThe aggregation auction for Υ3 has competitive ratio 3.25.\n179 PROOF.\nBy theorem 7, E AggΥ3 (b) F(2)(b) ≥ min k≥2 k i=1 k−i j=1 F(2) (i, j, k − i − j) k i,j,k−i−j βk3k For k = 2 and k = 3, E AggΥ3 (b) = 2 3 k/β.\nAs k increases, E AggΥ3 (b) /F(2) increases as well.\nSince we do not expect to find a closed-form formula for the revenue, we lower bound F(2) (b) by 3b(3).\nUsing large deviation bounds, one can show that this lower bound is greater than 2 3 k/β for large-enough k, and the remainder can be shown by explicit calculation.\nPlugging in β = 13/6, the competitive ratio is 13/4.\nAs k increases, the competitive ratio approaches 13/6.\nNote that the above bound on the competitive ratio of AggΥ3 is tight.\nTo see this, consider the bid vector with two very large and non-identical bids of h and h + with the remaining bids 1.\nGiven that the competitive ratio of Υ3 is tight on this example, the expected revenue of this auction on this input will be exactly 13/4.\n5.2 A Gs,t-based Aggregation Auction In this section we show that Υ3 is not the optimal auction to use in an aggregation auction.\nOne can do better by choosing the auction that is optimally competitive against a specially tailored benchmark.\nTo see why this might be the case, notice (Table 1) that the fraction of F(2) (b) raised for when there are k = 2 and k = 3 winning bidders in F(2) (b) is substantially smaller than the fraction of F(2) (b) raised when there are more winners.\nThis occurs because the expected ratio between F(2) (B) and F(2) (b) is lower in this case while the competitive ratio of Υ3 is constant.\nIf we chose a three bidder auction that performed better when F(2) has smaller numbers of winners, our aggregation auction would perform better in the worst case.\nOne approach is to compete against a different benchmark that puts more weight than F(2) on solutions with a small number of winners.\nRecall that F(2) is the instance of Gs,t with s = 2 and t = 3.\nBy using the auction that competes optimally against Gs,t with s > 2, while holding t = 3, we will raise a higher fraction of revenue on smaller numbers of winning bidders and a lower fraction of revenue on large numbers of winning bidders.\nWe can numerically optimize the values of s and t in Gs,t(b) in order to achieve the best competitive ratio for the aggregation auction.\nIn fact, this will allow us to improve our competitive ratio slightly.\nTHEOREM 9.\nFor an optimal choice of s and t, the aggregation auction for Υs,t 3 is 3.243-competitive.\nThe proof follows the outline of Theorem 7 and 8 with the optimal choice of s = 2.162 (while t is held constant at 3).\n5.3 Further Reducing the Competitive Ratio There are a number of ways we might attempt to use this aggregation auction schema to continue to push the competitive ratio down.\nIn this section, we give a brief discussion of several attempts.\n5.3.1 AggΥm for m > 3 If the aggregation auction for Υ2 has a competitive ratio of 4 and the aggregation auction for Υ3 has a competitive ratio of 3.25, can we improve the competitive ratio by aggregating Υ4 or Υm for larger m?\nWe conjecture in the negative: for m > 3, the aggregation auction for Υm has a larger competitive ratio than the aggregation auction for Υ3.\nThe primary difficulty in proving this k m = 2 m = 3 m = 4 m = 5 m = 6 m = 7 2 0.25 0.3077 0.3349 0.3508 0.3612 0.3686 3 0.25 0.3077 0.3349 0.3508 0.3612 0.3686 4 0.3125 0.3248 0.3349 0.3438 0.3512 0.3573 5 0.3125 0.3191 0.3244 0.3311 0.3378 0.3439 6 0.3438 0.321 0.3057 0.3056 0.311 0.318 7 0.3438 0.333 0.3081 0.3009 0.3025 0.3074 8 0.3633 0.3229 0.3109 0.3022 0.3002 0.3024 9 0.3633 0.3233 0.3057 0.2977 0.2927 0.292 10 0.377 0.3328 0.308 0.2952 0.2866 0.2837 11 0.377 0.3319 0.3128 0.298 0.2865 0.2813 12 0.3872 0.3358 0.3105 0.3001 0.2894 0.2827 13 0.3872 0.3395 0.3092 0.2976 0.2905 0.2841 14 0.3953 0.3391 0.312 0.2961 0.2888 0.2835 15 0.3953 0.3427 0.3135 0.2973 0.2882 0.2825 16 0.4018 0.3433 0.3128 0.298 0.2884 0.2823 17 0.4018 0.3428 0.3129 0.2967 0.2878 0.282 18 0.4073 0.3461 0.3133 0.2959 0.2859 0.2808 19 0.4073 0.3477 0.3137 0.2962 0.2844 0.2789 20 0.4119 0.3486 0.3148 0.2973 0.2843 0.2777 21 0.4119 0.3506 0.3171 0.298 0.2851 0.2775 22 0.4159 0.3519 0.3189 0.2986 0.2863 0.2781 23 0.4159 0.3531 0.3202 0.2995 0.2872 0.2791 24 0.4194 0.3539 0.3209 0.3003 0.2878 0.2797 25 0.4194 0.3548 0.3218 0.3012 0.2886 0.2801 Table 1: E A(b)/F(2) (b) for AggΥm as a function of k, the optimal number of winners in F(2) (b).\nThe lowest value for each column is printed in bold.\nconjecture lies in the difficulty of finding a closed-form solution for the formula of Theorem 7.\nWe can, however, evaluate this formula numerically for different values of m and k, assuming that the competitive ratio for Υm matches the lower bound for m given by Theorem 2.\nTable 1 shows, for each value of m and k, the fraction of F(2) raised by the aggregation auction for AggΥm when there are k winning bidders, assuming the lower bound of Theorem 2 is tight.\n5.3.2 Convex combinations of AggΥm As can be seen in Table 1, when m > 3, the worst-case value of k is no longer 2 and 3, but instead an increasing function of m.\nAn aggregation auction for Υm outperforms the aggregation auction for Υ3 when there are two or three winning bidders, while the aggregation auction for Υ3 outperforms the other when there are at least six winning bidders.\nThus, for instance, an auction which randomizes between aggregation auctions for Υ3 and Υ4 will have a worst-case which is better than that of either auction alone.\nLarger combinations of auctions will allow more room to optimize the worst-case.\nHowever, we suspect that no convex combination of aggregation auctions will have a competitive ratio lower than 3.\nFurthermore, note that we cannot yet claim the existence of a good auction via this technique as the optimal auction Υn for n > 3 is not known and it is only conjectured that the bound given by Theorem 2 and represented in Table 1 is correct for Υn.\n6.\nA LOWER BOUND FOR CONSERVATIVE AUCTIONS In this section, we define a class of auctions that never offer a sale price which is higher than any bid in the input and prove a lower bound on the competitive ratio of these auctions.\nAs this 180 lower bound is stronger than the lower bound of Theorem 2 for n ≥ 3, it shows that the optimal auction must occasionally charge a sales price higher than any bid in the input.\nSpecifically, this result partially explains the form of the optimal three bidder auction.\nDEFINITION 8.\nWe say an auction BIf is conservative if its bidindependent function f satisfies f(b-i) ≤ max(b-i).\nWe can now state our lower bound for conservative auctions.\nTHEOREM 10.\nLet A be a conservative auction for n bidders.\nThen the competitive ratio of A is at least 3n−2 n .\nCOROLLARY 1.\nThe competitive ratio of any conservative auction for an arbitrary number of bidders is at least three.\nFor a two-bidder auction, this restriction does not prevent optimality.\nΥ2, the 1-unit Vickrey auction, is conservative.\nFor larger numbers of bidders, however, the restriction to conservative auctions does affect the competitive ratio.\nFor the three-bidder case, Υ3 has competitive ratio 2.17, while the best conservative auction is no better than 2.33-competitive.\nThe k-Vickrey auction and the Random Sampling Optimal Price auction [9] are conservative auctions.\nThe Random Sampling Profit Extraction auction [5] and the CORE auction [7], on the other hand, use the ProfitExtractR mechanism as a subroutine and thus sometimes offer a sale price which is higher than the highest input bid value.\nIn [8], the authors define a restricted auction as one on which, for any input, the sale prices are drawn from the set of input bid values.\nThe class of conservative auctions can be viewed as a generalization of the class of restricted auctions and therefore our result below gives lower bounds on the performance of a broader class of auctions.\nWe will prove Theorem 10 with the aid of the following lemma: LEMMA 1.\nLet A be a conservative auction with competitive ratio 1/r for n bidders.\nLet h n. Let h0 = 1 and hk = kh otherwise.\nThen, for all k and H ≥ kh, Pr[f(1, 1, ... , 1, H) ≤ hk] ≥ nr−1 n−1 + k(3nr−2r−n n−1 ).\nPROOF.\nThe lemma is proved by strong induction on k. First some notation that will be convenient.\nFor any particular k and H we will be considering the bid vector b = (1, ... , 1, hk, H) and placing bounds on ρb-i (z).\nSince we can assume without loss of generality that the auction is symmetric, we will notate b-1 as b with any one of the 1-valued bids masked.\nSimilarly we notate b-hk (resp.\nb-H ) as b with the hk-valued bid (resp.\nH-valued bid) masked.\nWe will also let p1(b), phk (b), and pH (b) represent the expected payment of a 1-valued, hk-valued, and H-valued bidder in A on b, respectively (note by symmetry the expected payment for all 1-valued bidders is the same).\nBase case (k = 0, hk = 1): A must raise revenue of at least rn on b = (1, ... , 1, 1, H): rn ≤ pH (b) + (n − 1)p1(b) = 1 + (n − 1) 1 0 xρb-1 (x)dx ≤ 1 + (n − 1) 1 0 ρb-1 (x)dx The second inequality follows from the conservatism of the underlying auction.\nThe base case follows trivially from the final inequality.\nInductive case (k > 0, hk = kh): Let b = (1, ... , 1, hk, H).\nFirst, we will find an upper bound on pH(b) pH (b) = 1 0 xρb-H (x)dx + k i=1 hi hi−1 xρb-H (x)dx (1) ≤ 1 + k i=1 hi hi hi−1 ρb-H (x)dx ≤ 1 + 3nr − 2r − n n − 1 k−1 i=1 ih + kh 1 − nr − 1 n − 1 − (k − 1) 3nr − 2r − n n − 1 (2) = kh n(1 − r) n − 1 + (k − 1) 2 3nr − 2r − n n − 1 + 1.\n(3) Equation (1) follows from the conservatism of A and (2) is from invoking the strong inductive hypothesis with H = kh and the observation that the maximum possible revenue will be found by placing exactly enough probability at each multiple of h to satisfy the constraints of the inductive hypothesis and placing the remaining probability at kh.\nNext, we will find a lower bound on phk (b) by considering the revenue raised by the bids b. Recall that A must obtain a profit of at least rF(2) (b) = 2rkh.\nGiven upper-bounds on the profit from the H-valued, equation bid (3), and the 1-valued bids, the profit from the hk-valued bid must be at least: phk (b) ≥ 2rkh − (n − 2)p1(b) − pH(b) ≥ kh 2r − n(1 − r) n − 1 + (k − 1) 2 3nr − 2r − n n − 1 − O(n).\n(4) In order to lower bound Pr[f(b-hk ) ≤ kh], consider the auction that minimizes it and is consistent with the lower bounds obtained by the strong inductive hypothesis on Pr[f(b-hk ) ≤ ih].\nTo minimize the constraints implied by the strong inductive hypothesis, we place the minimal amount of probability mass required each price level.\nThis gives ρhk (b) with nr−1 n−1 probability at 1 and exactly 3nr−2r−n n−1 at each hi for 1 ≤ i < k. Thus, the profit from offering prices at most hk−1 is nr−1 n−1 −kh(k−1)3nr−2r−n n−1 .\nIn order to satisfy our lower bound, (4), on phk (b), it must put at least 3nr−2r−n n−1 on hk.\nTherefore, the probability that the sale price will be no more than kh on masked bid vector on bid vector b = (1, ... , 1, kh, H) must be at least nr−1 n−1 + k(3nr−2r−n n−1 ).\nGiven Lemma 1, Theorem 10 is simple to prove.\nPROOF.\nLet A be a conservative auction.\nSuppose 3nr−2r−n n−1 = > 0.\nLet k = 1/ , H ≥ kh, and h n. By Lemma 1, Pr[f(1, ... , 1, kh, H) ≤ hk] ≥ nr−1 n−1 + k > 1.\nBut this is a contradiction, so 3nr−2r−n n−1 ≤ 0.\nThus, r ≤ n 3n−2 .\nThe theorem follows.\n7.\nCONCLUSIONS AND FUTURE WORK We have found the optimal auction for the three-unit limitedsupply case, and shown that its structure is essentially independent of the benchmark used in its competitive analysis.\nWe have then used this auction to derive the best known auction for the unlimited supply case.\nOur work leaves many interesting open questions.\nWe found that the lower bound of [8] is matched by an auction for three bidders, 181 even when competing against generalized benchmarks.\nThe most interesting open question from our work is whether the lower bound from Theorem 2 can be matched by an auction for more than three bidders.\nWe conjecture that it can.\nSecond, we consider whether our techniques can be extended to find optimal auctions for greater numbers of bidders.\nThe use of our analytic solution method requires knowledge of a restricted class of auctions which is large enough to contain an optimal auction but small enough that the optimal auction in this class can be found explicitly through analytic methods.\nNo class of auctions which meets these criteria is known even for the four bidder case.\nAlso, when the number of bidders is greater than three, it might be the case that the optimal auction is not expressible in terms of elementary functions.\nAnother interesting set of open questions concerns aggregation auctions.\nAs we show, the aggregation auction for Υ3 outperforms the aggregation auction for Υ2 and it appears that the aggregation auction for Υ3 is better than Υm for m > 3.\nWe leave verification of this conjecture for future work.\nWe also show that Υ3 is not the best three-bidder auction for use in an aggregation auction, but the auction that beats it is able to reduce the competitive ratio of the overall auction only a little bit.\nIt would be interesting to know whether for any m there is an m-aggregation auction that substantially improves on the competitive ratio of AggΥm .\nFinally, we remark that very little is known about the structure of the optimal competitive auction.\nIn our auction Υ3, the sales price for a given bidder is restricted either to be one of the other bid values or to be higher than all other bid values.\nThe optimal auction for two bidders, the 1-unit Vickrey auction, also falls within this class of auctions, as its sales prices are restricted to bid values.\nWe conjecture that an optimal auction for any number of bidders lies within this class.\nOur paper provides partial evidence for this conjecture: the lower bound of Section 6 on conservative auctions shows that the optimal auction must offer sales prices higher than any bid value if the lower bound of Theorem 2 is tight, as is conjectured.\nIt remains to show that optimal auctions otherwise only offer sales prices at bid values.\n8.\nACKNOWLEDGEMENTS The authors wish to thank Yoav Shoham and Noga Alon for helpful discussions.\n9.\nREFERENCES [1] A. Archer and E. Tardos.\nTruthful mechanisms for one-parameter agents.\nIn Proc.\nof the 42nd IEEE Symposium on Foundations of Computer Science, 2001.\n[2] S. Baliga and R. Vohra.\nMarket research and market design.\nAdvances in Theoretical Economics, 3, 2003.\n[3] A. Borodin and R. El-Yaniv.\nOnline Computation and Competitive Analysis.\nCambridge University Press, 1998.\n[4] J. Bulow and J. Roberts.\nThe Simple Economics of Optimal Auctions.\nThe Journal of Political Economy, 97:1060-90, 1989.\n[5] A. Fiat, A. V. Goldberg, J. D. Hartline, and A. R. Karlin.\nCompetitive generalized auctions.\nIn Proc.\n34th ACM Symposium on the Theory of Computing, pages 72-81.\nACM, 2002.\n[6] A. Goldberg, J. Hartline, A. Karlin, M. Saks, and A. Wright.\nCompetitive auctions and digital goods.\nGames and Economic Behavior, 2002.\nSubmitted for publication.\nAn earlier version available as InterTrust Technical Report at URL http://www.star-lab.com/tr/tr-99-01.html.\n[7] A. V. Goldberg and J. D. Hartline.\nCompetitiveness via consensus.\nIn Proc.\n14th Symposium on Discrete Algorithms, pages 215-222.\nACM/SIAM, 2003.\n[8] A. V. Goldberg, J. D. Hartline, A. R. Karlin, and M. E. Saks.\nA lower bound on the competitive ratio of truthful auctions.\nIn Proc.\n21st Symposium on Theoretical Aspects of Computer Science, pages 644-655.\nSpringer, 2004.\n[9] A. V. Goldberg, J. D. Hartline, and A. Wright.\nCompetitive auctions and digital goods.\nIn Proc.\n12th Symposium on Discrete Algorithms, pages 735-744.\nACM/SIAM, 2001.\n[10] D. Lehmann, L. I. O``Callaghan, and Y. Shoham.\nTruth Revelation in Approximately Efficient Combinatorial Auctions.\nIn Proc.\nof 1st ACM Conf.\non E-Commerce, pages 96-102.\nACM Press, New York, 1999.\n[11] H. Moulin and S. Shenker.\nStrategyproof Sharing of Submodular Costs: Budget Balance Versus Efficiency.\nEconomic Theory, 18:511-533, 2001.\n[12] R. Myerson.\nOptimal Auction Design.\nMathematics of Operations Research, 6:58-73, 1981.\n[13] N. Nisan and A. Ronen.\nAlgorithmic Mechanism Design.\nIn Proc.\nof 31st Symp.\non Theory of Computing, pages 129-140.\nACM Press, New York, 1999.\n[14] I. Segal.\nOptimal pricing mechanisms with unknown demand.\nAmerican Economic Review, 16:50929, 2003.\n[15] W. Vickrey.\nCounterspeculation, Auctions, and Competitive Sealed Tenders.\nJ. of Finance, 16:8-37, 1961.\nAPPENDIX A. PROOF OF THEOREM 4 We wish to prove that Υs,t 3 , the optimal auction for three bidders against Gs,t, has competitive ratio at least s2 +t2 2t .\nOur proof follows the outline of the proof of Lemma 5 and Theorem 1 from [8]; however, our case is simpler because we only looking for a bound when n = 3.\nDefine the random bid vector B = (B1, B2, B3) with Pr[Bi > z] = 1/z.\nWe compute EB[Gs,t(B)] by integrating Pr[Gs,t(B) > z].\nThen we use the fact that no auction can have expected profit greater than 3 on B to find a lower bound on the competitive ratio against Gs,t for any auction.\nFor the input distribution B defined above, let B(i) be the ith largest bid.\nDefine the disjoint events H2 = B(2) ≥ z/s ∧ B(3) < z/t, and H3 = B(3) ≥ z/t.\nIntuitively, H3 corresponds to the event that all three bidders win in Gs,t, while H2 corresponds to the event that only the top two bidders win.\nGs,t(B) will be greater than z if either event occurs: Pr[Gs,t(B) > z] = Pr[H2] + Pr[H3] (5) = 3 s z 2 1 − t z + t z 3 (6) Using the identity defined for non-negative continuous random variables that E[X] = ∞ 0 Pr[X > x] dx, we have EB[Gs,t(B)] = t + ∞ t 3 s z 2 1 − t z + t z 3 dz (7) = 3 s2 + t2 2t (8) Given that, for any auction A, EB[EA[A(B)]] ≤ 3 [8], it is clear that EB[Gs,t(B)] EB[EA[A(B)]] ≥ s2 +t2 2t .\nTherefore, there exists some input b for each auction A such that Gs,t(b) EA[A(b)] ≥ s2+t2 2t .\n182"},"lvl-2":{"kind":"string","value":"From Optimal Limited To Unlimited Supply Auctions\nABSTRACT\nWe investigate the class of single-round, sealed-bid auctions for a set of identical items to bidders who each desire one unit.\nWe adopt the worst-case competitive framework defined by [9, 5] that compares the profit of an auction to that of an optimal single-price sale of least two items.\nIn this paper, we first derive an optimal auction for three items, answering an open question from [8].\nSecond, we show that the form of this auction is independent of the competitive framework used.\nThird, we propose a schema for converting a given limited-supply auction into an unlimited supply auction.\nApplying this technique to our optimal auction for three items, we achieve an auction with a competitive ratio of 3.25, which improves upon the previously best-known competitive ratio of 3.39 from [7].\nFinally, we generalize a result from [8] and extend our understanding of the nature of the optimal competitive auction by showing that the optimal competitive auction occasionally offers prices that are higher than all bid values.\n1.\nINTRODUCTION\nThe research area of optimal mechanism design looks at designing a mechanism to produce the most desirable outcome for the entity running the mechanism.\nThis problem is well studied for the auction design problem where the optimal mechanism is the one * This work was supported in part by an NSF fellowship and NSF ITR 0205633.\nthat brings the seller the most profit.\nHere, the classical approach is to design such a mechanism given the prior distribution from which the bidders' preferences are drawn (See e.g., [12, 4]).\nRecently Goldberg et al. [9] introduced the use of worst-case competitive analysis (See e.g., [3]) to analyze the performance of auctions that have no knowledge of the prior distribution.\nThe goal of such work is to design an auction that achieves a large constant fraction of the profit attainable by an optimal mechanism that knows the prior distribution in advance.\nPositive results in this direction are fueled by the observation that in auctions for a number of identical units, much of the distribution from which the bidders are drawn can be deduced on the fly by the auction as it is being run [9, 14, 2].\nThe performance of an auction in such a worst-case competitive analysis is measured by its competitive ratio, the ratio between a benchmark performance and the auction's performance on the input distribution that maximizes this ratio.\nThe holy grail of the worstcase competitive analysis of auctions is the auction that achieves the optimal competitive ratio (as small as possible).\nSince [9] this search has led to improved understanding of the nature of the optimal auction, the techniques for on-the-fly pricing in these scenarios, and the competitive ratio of the optimal auction [5, 7, 8].\nIn this paper we continue this line of research by improving in all of these directions.\nFurthermore, we give evidence corroborating the conjecture that the form of the optimal auction is independent of the benchmark used in the auction's competitive analysis.\nThis result further validates the use of competitive analysis in gauging auction performance.\nWe consider the single item, multi-unit, unit-demand auction problem.\nIn such an auction there are many units of a single item available for sale to bidders who each desire only one unit.\nEach bidder has a valuation representing how much the item is worth to him.\nThe auction is performed by soliciting a sealed bid from each of the bidders and deciding on the allocation of units to bidders and the prices to be paid by the bidders.\nThe bidders are assumed to bid so as to maximize their personal utility, the difference between their valuation and the price they pay.\nTo handle the problem of designing and analyzing auctions where bidders may falsely declare their valuations to get a better deal, we will adopt the solution concept of truthful mechanism design (see, e.g., [9, 15, 13]).\nIn a truthful auction, revealing one's true valuation as one's bid is an optimal strategy for each bidder regardless of the bids of the other bidders.\nIn this paper, we will restrict our attention to truthful (a.k.a., incentive compatible or strategyproof) auctions.\nA particularly interesting special case of the auction problem is the unlimited supply case.\nIn this case the number of units for sale is at least the number of bidders in the auction.\nThis is natural for the sale of digital goods where there is negligible cost for duplicating\nand distributing the good.\nPay-per-view television and downloadable audio files are examples of such goods.\nThe competitive framework introduced in [9] and further refined in [5] uses the profit of the optimal omniscient single priced mechanism that sells at least two units as the benchmark for competitive analysis.\nThe assumption that two or more units are sold is necessary because in the worst case it is impossible to obtain a constant fraction of the profit of the optimal mechanism when it sells only one unit [9].\nIn this framework for competitive analysis, an auction is said to be β-competitive if it achieves a profit that is within a factor of β ≥ 1 of the benchmark profit on every input.\nThe optimal auction is the one which is β-competitive with the minimum value of β.\nPrevious to this work, the best known auction for the unlimited supply case had a competitive ratio of 3.39 [7] and the best lower bound known was 2.42 [8].\nFor the limited supply case, auctions can achieve substantially better competitive ratios.\nWhen there are only two units for sale, the optimal auction gives a competitive ratio of 2, which matches the lower bound for two units.\nWhen there are three units for sale, the best previously known auction had a competitive ratio of 2.3, compared with a lower bound of 13/6 ≈ 2.17 [8].\nThe results of this paper are as follows:\n• We give the auction for three units that is optimally competitive against the profit of the omniscient single priced mechanism that sells at least two units.\nThis auction achieves a competitive ratio of 13/6, matching the lower bound from [8] (Section 3).\n• We show that the form of the optimal auction is independent of the benchmark used in competitive analysis.\nIn doing so, we give an optimal three bidder auction for generalized benchmarks (Section 4).\n• We give a general technique for converting a limited supply auction into an unlimited supply auction where it is possible to use the competitive ratio of the limited supply auction to obtain a bound on the competitive ratio of the unlimited supply auction.\nWe refer to auctions derived from this framework as aggregation auctions (Section 5).\n• We improve on the best known competitive ratio by proving that the aggregation auction constructed from our optimal three-unit auction is 3.25-competitive (Section 5.1).\n• Assuming that the conjecture that the optimal ~ - unit auction\nhas a competitive ratio that matches the lower bound proved in [8], we show that this optimal auction for ~ ≥ 3 on some inputs will occasionally offer prices that are higher than any bid in that input (Section 6).\nFor the three-unit case where we have shown that the lower bound of [8] is tight, this observation led to our construction of the optimal three-unit auction.\n2.\nDEFINITIONS AND BACKGROUND\nWe consider single-round, sealed-bid auctions for a set of ~ identical units of an item to bidders who each desire one unit.\nAs mentioned in the introduction, we adopt the game-theoretic solution concept of truthful mechanism design.\nA useful simplification of the problem of designing truthful auctions is obtained through the following algorithmic characterization [9].\nRelated formulations to this one have appeared in numerous places in recent literature (e.g., [1, 14, 5, 10]).\nDEFINITION 1.\nGiven a bid vector of n bids, b = (b1,..., bn), let b-i denote the vector of with bi replaced with a `? '\n, i.e.,\n1.\nSet ti = f (b-i).\n2.\nIfti <bi, bidder i wins at price ti.\n3.\nIf ti> bi, bidder i loses.\n4.\nOtherwise, (ti = bi) the auction can either accept the bid at price ti or reject it.\nA randomized bid-independent auction is a distribution over deterministic bid-independent auctions.\nThe proof of the following theorem can be found, for example, in [5].\nGiven this equivalence, we will use the the terminology bidindependent and truthful interchangeably.\nFor a randomized bid-independent auction, f (b-i) is a random variable.\nWe denote the probability density of f (b-i) at z by ρb-i (z).\nWe denote the profit of a truthful auction A on input b as A (b).\nThe expected profit of the auction, E [A (b)], is the sum of the expected payments made by each bidder, which we denote by pi (b) for bidder i. Clearly, the expected payment of each bid satisfies\n2.1 Competitive Framework\nWe now review the competitive framework from [5].\nIn order to evaluate the performance of auctions with respect to the goal of profit maximization, we introduce the optimal single price omniscient auction F and the related omniscient auction F (2).\nDEFINITION 3.\nGive a vector b = (b1,..., bn), let b (i) represent the i-th largest value in b.\nThe optimal single price omniscient auction, F, is defined as follows.\nAuction F on input b determines the value k such that kb (k) is maximized.\nAll bidders with bi ≥ b (k) win at price b (k); all remaining bidders lose.\nThe profit of F on input b is thus F (b) = max1 ≤ k ≤ n kb (k).\nIn the competitive framework of [5] and subsequent papers, the performance of a truthful auction is gauged in comparison to F (2), the optimal singled priced auction that sells at least two units.\nThe profit of F (2) is max2 ≤ k ≤ n kb (k) There are a number of reasons to choose this benchmark for comparison, interested readers should see [5] or [6] for a more detailed discussion.\nLet A be a truthful auction.\nWe say that A is β-competitive against F (2) (or just β-competitive) if for all bid vectors b, the expected profit of A on b satisfies β.\nIn Section 4 we generalize this framework to other profit benchmarks.\n2.2 Scale Invariant and Symmetric Auctions\nA symmetric auction is one where the auction outcome is unchanged when the input bids arrive in a different permutation.\nGoldberg et al. [8] show that a symmetric auction achieves the optimal competitive ratio.\nThis is natural as the profit benchmark we consider is symmetric, and it allows us to consider only symmetric auctions when looking for the one with the optimal competitive ratio.\nAn auction defined by bid-independent function f is scale invariant if, for all i and all z, Pr [f (b-i)> z] = Pr [f (cb-i)> cz].\nIt is conjectured that the assumption of scale invariance is without loss of generality.\nThus, we are motivated to consider symmetric scale-invariant auctions.\nWhen specifying a symmetric scaleinvariant auction we can assume that f is only a function of the relative magnitudes of the n − 1 bids in b-i and that one of the bids, bj = 1.\nIt will be convenient to specify such auctions via the density function of f (b-i), ρb-i (z).\nIt is enough to specify such a density function of the form ρ1, z1,..., zn − 1 (z) with 1 <zi <zi +1.\n2.3 Limited Supply Versus Unlimited Supply\nFollowing [8], throughout the remainder of this paper we will be making the assumption that n = f, i.e., the number of bidders is equal to the number of units for sale.\nThis is without loss of generality as (a) any lower bound that applies to the n = f case also extends to the case where n> f [8], and (b) there is a reduction from the unlimited supply auction problem to the limited supply auction problem that takes an unlimited supply auction that is 3-competitive with F (2) and constructs a limited supply auction parameterized by f that is 3-competitive with F (2,,1), the optimal omniscient auction that sells between 2 and f units [6].\nHenceforth, we will assume that we are in the unlimited supply case, and we will examine lower bounds for limited supply problems by placing a restriction on the number of bidders in the auction.\n2.4 Lower Bounds and Optimal Auctions\nFrequently in this paper, we will refer to the best known lower bound on the competitive ratio of truthful auctions:\n2.5 Profit Extraction\nIn this section we review the truthful profit extraction mechanism ProfitExtractR.\nThis mechanism is a special case of a general cost-sharing schema due to Moulin and Shenker [11].\nThe goal of profit extraction is, given bids b, to extract a target value R of profit from some subset of the bidders.\nProfitExtractR: Given bids b, find the largest k such that the highest k bidders can equally share the cost R. Charge each of these bidders R/k.\nIf no subset of bidders can cover the cost, the mechanism has no winners.\nImportant properties of this auction are as follows:\n• ProfitExtractR is truthful.\n• If R <F (b), ProfitExtractR (b) = R; otherwise it has no winners and no revenue.\nWe will use this profit extraction mechanism in Section 5 with the following intuition.\nSuch a profit extractor makes it possible to treat this subset of bidders as a single bid with value F (S).\nNote that given a single bid, b, a truthful mechanism might offer it price t and if t <b then the bidder wins and pays t; otherwise the bidder pays nothing (and loses).\nLikewise, a mechanism can offer the set of bidders S a target revenue R.\nIf R <F (2) (S), then ProfitExtractR raises R from S; otherwise, the it raises no revenue from S.\n3.\nAN OPTIMAL AUCTION FOR THREE BIDDERS\nIn this section we define the optimal auction for three bidders, Υ3, and prove that it indeed matches the known lower bound of 13/6.\nWe follow the definition and proof with a discussion of how this auction was derived.\nDEFINITION 5.\nΥ3 is scale-invariant and symmetric and given by the bid-independent function with density function\nTHEOREM 2.\n[8] The competitive ratio of any auction on n bidders is at least DEFINITION 4.\nLet Υn denote the n-bidder auction that achieves the optimal competitive ratio.\nThis bound is derived by analyzing the performance of any auction on the following distribution B.\nIn each random bid vector B, each bid Bi is drawn i.i.d. from the distribution such that Pr [Bi> s] <1/s for all s E S.\nIn the two-bidder case, this lower bound is 2.\nThis is achieved by Υ2 which is the 1-unit Vickrey auction .1 In the three-bidder case, this lower bound is 13/6.\nIn the next section, we define the auction Υ3 which matches this lower bound.\nIn the four-bidder case, this lower bound is 96/215.\nIn the limit as the number of bidders grows, this lower bound approaches a number which is approximately 2.42.\nIt is conjectured that this lower bound is tight for any number of bidders and the optimal auction, Υn, matches it.\n1The 1-unit Vickrey auction sells to the highest bidder at the second highest bid value.\nwhere g (x) = 2/13 (x − 1) 3.\nPROOF.\nConsider the bids 1, x, y, with 1 <x <y.\nThere are three cases.\nCASE 1 (x <y <3/2): F (2) = 3.\nThe auction must raise expected revenue of at least 18/13 on these bids.\nThe bidder with valuation x will pay 1 with 9/13, and the bidder with valuation y will pay 1 with probability 9/13.\nTherefore Υ3 raises 18/13 on these bids.\nCASE 2 (x <3/2 <y): F (2) = 3.\nThe auction must raise expected revenue of at least 18/13 on these bids.\nThe bidder with\nvaluation x will pay 9/13 − fy3/2 zg (z) dz in expectation.\nThe bidder with valuation y will pay 9/13 + fy3/2 zg (z) dz in expectation.\nTherefore Υ3 raises 18/13 on these bids.\nCASE 3 (3/2 <x <y): F (2) = 2x.\nThe auction must raise expected revenue of at least 12x/13 on these bids.\nConsider the revenue raised from all three bidders:\nThe final equation comes from substituting in g (x) = 2/13 (x − 1) 3 and expanding the integrals.\nNote that the fraction of F (2) raised on every input is identical.\nIf any of the inequalities 1 <x <y are not strict, the same proof applies giving a lower bound on the auction's profit; however, this bound may no longer be tight.\nMotivation for Υ3\nIn this section, we will conjecture that a particular input distribution is worst-case, and show, as a consequence, that all inputs are worstcase in the optimal auction.\nBy applying this consequence, we will derive an optimal auction for three bidders.\nA truthful, randomized auction on n bidders can be represented by a randomized function f: Rn − 1 x n--+ R that maps masked bid vectors to prices in R. By normalization, we can assume that the lowest possible bid is 1.\nRecall that Pb-i (z) = Pr [f (b-i) = z].\nThe optimal auction for the finite auction problem can be found by the following optimization problem in which the variables are\nThis set of integral inequalities is difficult to maximize over.\nHowever, by guessing which constraints are tight and which are slack at the optimum, we will be able to derive a set of differential equations for which any feasible solution is an optimal auction.\nAs we discuss in Section 2.4, in [8], the authors define a distribution and use it to find a lower bound on the competitive ratio of the optimal auction.\nFor two bidders, this bid distribution is the worst-case input distribution.\nWe guess (and later verify) that this distribution is the worst-case input distribution for three bidders as well.\nSince this distribution has full support over the set of all bid vectors and a worst-case distribution puts positive probability only on worst-case inputs, we can therefore assume that all but a measure zero set of inputs is worst-case for the optimal auction.\nIn the optimal two-bidder auction, all inputs with non-identical bids are worst-case, so we will assume the same for three bidders.\nThe guess that these constraints are tight allows us to transform the optimization problem into a feasibility problem constrained by differential equations.\nIf the solution to these equations has value matching the lower bound obtained from the worst-case distribution, then this solution is the optimal auction and that our conjectured choice of worst-case distribution is correct.\nIn Section 6 we show that the optimal auction must sometimes place probability mass on sale prices above the highest bid.\nThis motivates considering symmetric scale-invariant auctions for three bidders with probability density function, P1, x (z), of the following form: 1 with discrete probability a (x) x with discrete probability b (x) z with probability density g (z) for z> x In this auction, the sale price for the first bidder is either one of the latter two bids, or higher than either bid with a probability density which is independent of the input.\nThe feasibility problem which arises from the linear optimization problem by assuming the constraints are tight is as follows:\nSolving this feasibility problem gives the auction Υ3 proposed above.\nThe proof of its optimality validates its proposed form.\nFinding a simple restriction on the form of n-bidder auctions for n> 3 under which the optimal auction can be found analytically as above remains an open problem.\n4.\nGENERALIZED PROFIT BENCHMARKS\nIn this section, we widen our focus beyond auctions that compete with F (2) to consider other benchmarks for an auction's profit.\nWe will show that, for three bidders, the form of the optimal auction is essentially independent of the benchmark profit used.\nThis results strongly corroborates the worst-case competitive analysis of auctions by showing that our techniques allow us to derive auctions which are competitive against a broad variety of reasonable benchmarks rather than simply against F (2).\nPrevious work in competitive analysis of auctions has focused on the question of designing the auction with the best competitive ratio against F (2), the profit of the optimal omniscient single-priced mechanism that sells at least two items.\nHowever, it is reasonable to consider other benchmarks.\nFor instance, one might wish to compete against V ∗, the profit of the k-Vickrey auction with optimal-inhindsight choice of k. 2 Alternatively, if an auction is being used as a subroutine in a larger mechanism, one might wish to choose the auction which is optimally competitive with a benchmark specific to that purpose.\nRecall that F (2) (b) = max2 ≥ k ≥ n kb (k).\nWe can generalize this definition to 9 $, parameterized by s = (s2,..., sn) and defined as:\nWhen considering 9 $we assume without loss of generality that si <si +1 as otherwise the constraint imposed by si +1 is irrelevant.\nNote that F (2) is the special case of 9 $with si = i, and that V ∗ = 9 $with si = i − 1.\n2Recall that the k-Vickrey auction sells a unit to each of the highest k bidders at a price equal to the k + 1st highest bid, b (k +1), achieving a profit of kb (k +1).\nCompeting with Gs\nWe will now design a three-bidder auction Υs, t 3 that achieves the optimal competitive ratio against Gs, t.\nAs before, we will first find a lower bound on the competitive ratio and then design an auction to meet that bound.\nWe can lower bound the competitive ratio of Υs, t 3 using the same worst-case distribution from [8] that we used against F (2).\nEvaluating the performance of any auction competing against Gs, t on this distribution will yield the following theorem.\nWe denote the optimal auction for three bidders against Gs, t as Υs, t\nThe proof can be found in the appendix.\nSimilarly, we can find the optimal auction against Gs, t using the same technique we used to solve for the three bidder auction with the best competitive ratio against F (2).\nDEFINITION 6.\nΥs, t\n3 is scale-invariant and symmetric and given by the bid-independent function with density function where g (x) = 2 (t − s) 2 / (s2 + t2)\nThis auction, like Υ3, can be derived by reducing the optimization problem to a feasibility problem, guessing that the optimal solution has the same form as Υs, t 3, and solving.\nThe auction is optimal because it matches the lower bound found above.\nNote that the form of Υs, t 3 is essentially the same as for Υ3, but that the probability of each price is scaled depending on the values of s and t.\nThat our auction for three bidders matches the lower bound computed by the input distribution used in [8] is strong evidence that this input distribution is the worst-case input distribution for any number of bidders and any generalized profit benchmark.\nFurthermore, we strongly suspect that for any number of bidders, the form of the optimal auction will be independent of the benchmark used.\n5.\nAGGREGATION AUCTIONS\nWe have seen that optimal auctions for small cases of the limitedsupply model can be found analytically.\nIn this section, we will construct a schema for turning limited supply auctions into unlimited supply auctions with a good competitive ratio.\nAs discussed in Section 2.5, the existence of a profit extractor, ProfitExtractR, allows an auction to treat a set of bids S as a single bid with value F (S).\nGiven n bidders and an auction, Am, for m <n bidders, we can convert the m-bidder auction into an n-bidder auction by randomly partitioning the bidders into m subsets and then treating each subset as a single bidder (via ProfitExtractR) and running the m-bidder auction.\nDEFINITION 7.\nGiven a truthful m-bidder auction, Am, the m-aggregation auction for Am, AggAm, works as follows:\n1.\nCast each bid uniformly at random into one of m bins, resulting in bid vectors b (1),..., b (m).\n2.\nFor each bin j, compute the aggregate bid Bj = F (b (j)).\nLet B be the vector of aggregate bids, and B − j be the aggregate bids for all bins other than j. 3.\nCompute the aggregate price Tj = f (B − j), where f is the bid-independent function for Am.\n4.\nFor each bin j, run ProfitExtractTj on b (j).\nSince Am and ProfitExtractR are truthful, Tj is computed independently of any bid in bin j and thus the price offered any bidder in b (j) is independent of his bid; therefore, THEOREM 6.\nIf Am is truthful, the m-aggregation auction for Am, AggAm, is truthful.\nNote that this schema yields a new way of understanding the Random Sampling Profit Extraction (RSPE) auction [5] as the simplest case of an aggregation auction.\nIt is the 2-aggregation auction for Υ2, the 1-unit Vickrey auction.\nTo analyze AggAm, consider throwing k balls into m labeled bins.\nLet k represent a configuration of balls in bins, so that ki is equal to the number of balls in bin i, and k (i) is equal to the number of balls in the ith largest bin.\nLet Km, k represent the set of all possible configurations of k balls in m bins.\nWe write the multinomial coefficient of k as (k).\nThe probability that a particular configu\nThen the m-aggregation auction for Am, AggAm, raises the following fraction of the optimal revenue F (2) (b):\nThe inequality follows from the monotonicity of F (2), and the equality from the homogeneity of F (2).\nProfitExtractTj will raise Tj if Tj ≤ Bj, and no profit other] wise.\nThus, E [AggAm (b)] ≥ E IF (2) (B) / β.\nThe theorem follows by rewriting this expectation as a sum over all k in Km, k.\n5.1 A 3.25 Competitive Auction\nWe apply the aggregation auction schema to Υ3, our optimal auction for three bidders, to achieve an auction with competitive ratio 3.25.\nThis improves on the previously best known auction which is 3.39-competitive [7].\nTHEOREM 8.\nThe aggregation auction for Υ3 has competitive ratio 3.25.\nPROOF.\nBy theorem 7,\nFor k = 2 and k = 3, E [AggΥ3 (b)] = 23 k /,3.\nAs k increases, E [AggΥ3 (b)] / F (2) increases as well.\nSince we do not expect to find a closed-form formula for the revenue, we lower bound F (2) (b) by 3b (3).\nUsing large deviation bounds, one can show that this lower bound is greater than 23 k /,3 for large-enough k, and the remainder can be shown by explicit calculation.\nPlugging in,3 = 13/6, the competitive ratio is 13/4.\nAs k increases, the competitive ratio approaches 13/6.\nNote that the above bound on the competitive ratio of AggΥ3 is tight.\nTo see this, consider the bid vector with two very large and non-identical bids of h and h + e with the remaining bids 1.\nGiven that the competitive ratio of Υ3 is tight on this example, the expected revenue of this auction on this input will be exactly 13/4.\n5.2 A Gs,t-based Aggregation Auction\nIn this section we show that Υ3 is not the optimal auction to use in an aggregation auction.\nOne can do better by choosing the auction that is optimally competitive against a specially tailored benchmark.\nTo see why this might be the case, notice (Table 1) that the fraction of F (2) (b) raised for when there are k = 2 and k = 3 winning bidders in F (2) (b) is substantially smaller than the fraction of F (2) (b) raised when there are more winners.\nThis occurs because the expected ratio between F (2) (B) and F (2) (b) is lower in this case while the competitive ratio of Υ3 is constant.\nIf we chose a three bidder auction that performed better when F (2) has smaller numbers of winners, our aggregation auction would perform better in the worst case.\nOne approach is to compete against a different benchmark that puts more weight than F (2) on solutions with a small number of winners.\nRecall that F (2) is the instance of Gs, t with s = 2 and t = 3.\nBy using the auction that competes optimally against Gs, t with s> 2, while holding t = 3, we will raise a higher fraction of revenue on smaller numbers of winning bidders and a lower fraction of revenue on large numbers of winning bidders.\nWe can numerically optimize the values of s and t in Gs, t (b) in order to achieve the best competitive ratio for the aggregation auction.\nIn fact, this will allow us to improve our competitive ratio slightly.\nTHEOREM 9.\nFor an optimal choice of s and t, the aggregation auction for Υs, t\n3 is 3.243-competitive.\nThe proof follows the outline of Theorem 7 and 8 with the optimal choice of s = 2.162 (while t is held constant at 3).\n5.3 Further Reducing the Competitive Ratio\nThere are a number of ways we might attempt to use this aggregation auction schema to continue to push the competitive ratio down.\nIn this section, we give a brief discussion of several attempts.\n5.3.1 AggΥm for m> 3\nIf the aggregation auction for Υ2 has a competitive ratio of 4 and the aggregation auction for Υ3 has a competitive ratio of 3.25, can we improve the competitive ratio by aggregating Υ4 or Υm for larger m?\nWe conjecture in the negative: for m> 3, the aggregation auction for Υm has a larger competitive ratio than the aggregation auction for Υ3.\nThe primary difficulty in proving this\nTable 1: E A (b) / F (2) (b) for AggΥm as a function of k, the\noptimal number of winners in F (2) (b).\nThe lowest value for each column is printed in bold.\nconjecture lies in the difficulty of finding a closed-form solution for the formula of Theorem 7.\nWe can, however, evaluate this formula numerically for different values of m and k, assuming that the competitive ratio for Υm matches the lower bound for m given by Theorem 2.\nTable 1 shows, for each value of m and k, the fraction of F (2) raised by the aggregation auction for AggΥm when there are k winning bidders, assuming the lower bound of Theorem 2 is tight.\n5.3.2 Convex combinations of AggΥm\nAs can be seen in Table 1, when m> 3, the worst-case value of k is no longer 2 and 3, but instead an increasing function of m.\nAn aggregation auction for Υm outperforms the aggregation auction for Υ3 when there are two or three winning bidders, while the aggregation auction for Υ3 outperforms the other when there are at least six winning bidders.\nThus, for instance, an auction which randomizes between aggregation auctions for Υ3 and Υ4 will have a worst-case which is better than that of either auction alone.\nLarger combinations of auctions will allow more room to optimize the worst-case.\nHowever, we suspect that no convex combination of aggregation auctions will have a competitive ratio lower than 3.\nFurthermore, note that we cannot yet claim the existence of a good auction via this technique as the optimal auction Υn for n> 3 is not known and it is only conjectured that the bound given by Theorem 2 and represented in Table 1 is correct for Υn.\n6.\nA LOWER BOUND FOR CONSERVATIVE AUCTIONS\nIn this section, we define a class of auctions that never offer a sale price which is higher than any bid in the input and prove a lower bound on the competitive ratio of these auctions.\nAs this\nlower bound is stronger than the lower bound of Theorem 2 for n ≥ 3, it shows that the optimal auction must occasionally charge a sales price higher than any bid in the input.\nSpecifically, this result partially explains the form of the optimal three bidder auction.\nDEFINITION 8.\nWe say an auction BIf is conservative if its bidindependent function f satisfies f (b-i) ≤ max (b-i).\nWe can now state our lower bound for conservative auctions.\nTHEOREM 10.\nLet A be a conservative auction for n bidders.\nThen the competitive ratio of A is at least 3n-2n.\nCOROLLARY 1.\nThe competitive ratio ofany conservative auction for an arbitrary number of bidders is at least three.\nFor a two-bidder auction, this restriction does not prevent optimality.\nΥ2, the 1-unit Vickrey auction, is conservative.\nFor larger numbers of bidders, however, the restriction to conservative auctions does affect the competitive ratio.\nFor the three-bidder case, Υ3 has competitive ratio 2.17, while the best conservative auction is no better than 2.33-competitive.\nThe k-Vickrey auction and the Random Sampling Optimal Price auction [9] are conservative auctions.\nThe Random Sampling Profit Extraction auction [5] and the CORE auction [7], on the other hand, use the ProfitExtractR mechanism as a subroutine and thus sometimes offer a sale price which is higher than the highest input bid value.\nIn [8], the authors define a restricted auction as one on which, for any input, the sale prices are drawn from the set of input bid values.\nThe class of conservative auctions can be viewed as a generalization of the class of restricted auctions and therefore our result below gives lower bounds on the performance of a broader class of auctions.\nWe will prove Theorem 10 with the aid of the following lemma:\nPROOF.\nThe lemma is proved by strong induction on k. First some notation that will be convenient.\nFor any particular k and H we will be considering the bid vector b = (1,..., 1, hk, H) and placing bounds on ρb-i (z).\nSince we can assume without loss of generality that the auction is symmetric, we will notate b-1 as b with any one of the 1-valued bids masked.\nSimilarly we notate b-hk (resp.\nb-H) as b with the hk-valued bid (resp.\nH-valued bid) masked.\nWe will also let p1 (b), phk (b), and pH (b) represent the expected payment of a 1-valued, hk-valued, and H-valued bidder in A on b, respectively (note by symmetry the expected payment for all 1-valued bidders is the same).\nBase case (k = 0, hk = 1): A must raise revenue of at least rn on b = (1,..., 1, 1, H):\nThe second inequality follows from the conservatism of the underlying auction.\nThe base case follows trivially from the final inequality.\nInductive case (k> 0, hk = kh): Let b = (1,..., 1, hk, H).\nFirst, we will find an upper bound on pH (b)\nEquation (1) follows from the conservatism of A and (2) is from invoking the strong inductive hypothesis with H = kh and the observation that the maximum possible revenue will be found by placing exactly enough probability at each multiple of h to satisfy the constraints of the inductive hypothesis and placing the remaining probability at kh.\nNext, we will find a lower bound on phk (b) by considering the revenue raised by the bids b. Recall that A must obtain a profit of at least rF (2) (b) = 2rkh.\nGiven upper-bounds on the profit from the H-valued, equation bid (3), and the 1-valued bids, the profit from the hk-valued bid must be at least:\nIn order to lower bound Pr [f (b-hk) ≤ kh], consider the auction that minimizes it and is consistent with the lower bounds obtained by the strong inductive hypothesis on Pr [f (b-hk) ≤ ih].\nTo minimize the constraints implied by the strong inductive hypothesis, we place the minimal amount of probability mass required each price level.\nThis gives ρhk (b) with nr-1 n-1 probability at 1 and exactly 3nr-2r-n n-1 at each hi for 1 ≤ i <k. Thus, the profit from offering prices at most hk-1 is nr-1\non hk.\nTherefore, the probability that the sale price will be no more than kh on masked bid vector on bid vector b = (1,..., 1, kh, H) must be at least nr-1 n-1 + k (3nr-2r-n n-1).\nGiven Lemma 1, Theorem 10 is simple to prove.\nPROOF.\nLet A be a conservative auction.\nSuppose 3nr-2r-n n-1 =\nn-1 ≤ 0.\nThus, r ≤ n 3n-2.\nThe theorem follows.\n7.\nCONCLUSIONS AND FUTURE WORK\nWe have found the optimal auction for the three-unit limitedsupply case, and shown that its structure is essentially independent of the benchmark used in its competitive analysis.\nWe have then used this auction to derive the best known auction for the unlimited supply case.\nOur work leaves many interesting open questions.\nWe found that the lower bound of [8] is matched by an auction for three bidders,\neven when competing against generalized benchmarks.\nThe most interesting open question from our work is whether the lower bound from Theorem 2 can be matched by an auction for more than three bidders.\nWe conjecture that it can.\nSecond, we consider whether our techniques can be extended to find optimal auctions for greater numbers of bidders.\nThe use of our analytic solution method requires knowledge of a restricted class of auctions which is large enough to contain an optimal auction but small enough that the optimal auction in this class can be found explicitly through analytic methods.\nNo class of auctions which meets these criteria is known even for the four bidder case.\nAlso, when the number of bidders is greater than three, it might be the case that the optimal auction is not expressible in terms of elementary functions.\nAnother interesting set of open questions concerns aggregation auctions.\nAs we show, the aggregation auction for Υ3 outperforms the aggregation auction for Υ2 and it appears that the aggregation auction for Υ3 is better than Υm for m> 3.\nWe leave verification of this conjecture for future work.\nWe also show that Υ3 is not the best three-bidder auction for use in an aggregation auction, but the auction that beats it is able to reduce the competitive ratio of the overall auction only a little bit.\nIt would be interesting to know whether for any m there is an m-aggregation auction that substantially improves on the competitive ratio of AggΥm.\nFinally, we remark that very little is known about the structure of the optimal competitive auction.\nIn our auction Υ3, the sales price for a given bidder is restricted either to be one of the other bid values or to be higher than all other bid values.\nThe optimal auction for two bidders, the 1-unit Vickrey auction, also falls within this class of auctions, as its sales prices are restricted to bid values.\nWe conjecture that an optimal auction for any number of bidders lies within this class.\nOur paper provides partial evidence for this conjecture: the lower bound of Section 6 on conservative auctions shows that the optimal auction must offer sales prices higher than any bid value if the lower bound of Theorem 2 is tight, as is conjectured.\nIt remains to show that optimal auctions otherwise only offer sales prices at bid values."},"lvl-3":{"kind":"string","value":"From Optimal Limited To Unlimited Supply Auctions\nABSTRACT\nWe investigate the class of single-round, sealed-bid auctions for a set of identical items to bidders who each desire one unit.\nWe adopt the worst-case competitive framework defined by [9, 5] that compares the profit of an auction to that of an optimal single-price sale of least two items.\nIn this paper, we first derive an optimal auction for three items, answering an open question from [8].\nSecond, we show that the form of this auction is independent of the competitive framework used.\nThird, we propose a schema for converting a given limited-supply auction into an unlimited supply auction.\nApplying this technique to our optimal auction for three items, we achieve an auction with a competitive ratio of 3.25, which improves upon the previously best-known competitive ratio of 3.39 from [7].\nFinally, we generalize a result from [8] and extend our understanding of the nature of the optimal competitive auction by showing that the optimal competitive auction occasionally offers prices that are higher than all bid values.\n1.\nINTRODUCTION\nThe research area of optimal mechanism design looks at designing a mechanism to produce the most desirable outcome for the entity running the mechanism.\nThis problem is well studied for the auction design problem where the optimal mechanism is the one * This work was supported in part by an NSF fellowship and NSF ITR 0205633.\nthat brings the seller the most profit.\nHere, the classical approach is to design such a mechanism given the prior distribution from which the bidders' preferences are drawn (See e.g., [12, 4]).\nRecently Goldberg et al. [9] introduced the use of worst-case competitive analysis (See e.g., [3]) to analyze the performance of auctions that have no knowledge of the prior distribution.\nThe goal of such work is to design an auction that achieves a large constant fraction of the profit attainable by an optimal mechanism that knows the prior distribution in advance.\nPositive results in this direction are fueled by the observation that in auctions for a number of identical units, much of the distribution from which the bidders are drawn can be deduced on the fly by the auction as it is being run [9, 14, 2].\nThe performance of an auction in such a worst-case competitive analysis is measured by its competitive ratio, the ratio between a benchmark performance and the auction's performance on the input distribution that maximizes this ratio.\nThe holy grail of the worstcase competitive analysis of auctions is the auction that achieves the optimal competitive ratio (as small as possible).\nSince [9] this search has led to improved understanding of the nature of the optimal auction, the techniques for on-the-fly pricing in these scenarios, and the competitive ratio of the optimal auction [5, 7, 8].\nIn this paper we continue this line of research by improving in all of these directions.\nFurthermore, we give evidence corroborating the conjecture that the form of the optimal auction is independent of the benchmark used in the auction's competitive analysis.\nThis result further validates the use of competitive analysis in gauging auction performance.\nWe consider the single item, multi-unit, unit-demand auction problem.\nIn such an auction there are many units of a single item available for sale to bidders who each desire only one unit.\nEach bidder has a valuation representing how much the item is worth to him.\nThe auction is performed by soliciting a sealed bid from each of the bidders and deciding on the allocation of units to bidders and the prices to be paid by the bidders.\nThe bidders are assumed to bid so as to maximize their personal utility, the difference between their valuation and the price they pay.\nTo handle the problem of designing and analyzing auctions where bidders may falsely declare their valuations to get a better deal, we will adopt the solution concept of truthful mechanism design (see, e.g., [9, 15, 13]).\nIn a truthful auction, revealing one's true valuation as one's bid is an optimal strategy for each bidder regardless of the bids of the other bidders.\nIn this paper, we will restrict our attention to truthful (a.k.a., incentive compatible or strategyproof) auctions.\nA particularly interesting special case of the auction problem is the unlimited supply case.\nIn this case the number of units for sale is at least the number of bidders in the auction.\nThis is natural for the sale of digital goods where there is negligible cost for duplicating\nand distributing the good.\nPay-per-view television and downloadable audio files are examples of such goods.\nThe competitive framework introduced in [9] and further refined in [5] uses the profit of the optimal omniscient single priced mechanism that sells at least two units as the benchmark for competitive analysis.\nThe assumption that two or more units are sold is necessary because in the worst case it is impossible to obtain a constant fraction of the profit of the optimal mechanism when it sells only one unit [9].\nIn this framework for competitive analysis, an auction is said to be β-competitive if it achieves a profit that is within a factor of β ≥ 1 of the benchmark profit on every input.\nThe optimal auction is the one which is β-competitive with the minimum value of β.\nPrevious to this work, the best known auction for the unlimited supply case had a competitive ratio of 3.39 [7] and the best lower bound known was 2.42 [8].\nFor the limited supply case, auctions can achieve substantially better competitive ratios.\nWhen there are only two units for sale, the optimal auction gives a competitive ratio of 2, which matches the lower bound for two units.\nWhen there are three units for sale, the best previously known auction had a competitive ratio of 2.3, compared with a lower bound of 13/6 ≈ 2.17 [8].\nThe results of this paper are as follows:\n• We give the auction for three units that is optimally competitive against the profit of the omniscient single priced mechanism that sells at least two units.\nThis auction achieves a competitive ratio of 13/6, matching the lower bound from [8] (Section 3).\n• We show that the form of the optimal auction is independent of the benchmark used in competitive analysis.\nIn doing so, we give an optimal three bidder auction for generalized benchmarks (Section 4).\n• We give a general technique for converting a limited supply auction into an unlimited supply auction where it is possible to use the competitive ratio of the limited supply auction to obtain a bound on the competitive ratio of the unlimited supply auction.\nWe refer to auctions derived from this framework as aggregation auctions (Section 5).\n• We improve on the best known competitive ratio by proving that the aggregation auction constructed from our optimal three-unit auction is 3.25-competitive (Section 5.1).\n• Assuming that the conjecture that the optimal ~ - unit auction\nhas a competitive ratio that matches the lower bound proved in [8], we show that this optimal auction for ~ ≥ 3 on some inputs will occasionally offer prices that are higher than any bid in that input (Section 6).\nFor the three-unit case where we have shown that the lower bound of [8] is tight, this observation led to our construction of the optimal three-unit auction.\n2.\nDEFINITIONS AND BACKGROUND\n2.1 Competitive Framework\n2.2 Scale Invariant and Symmetric Auctions\n2.3 Limited Supply Versus Unlimited Supply\n2.4 Lower Bounds and Optimal Auctions\n2.5 Profit Extraction\n3.\nAN OPTIMAL AUCTION FOR THREE BIDDERS\nMotivation for Υ3\n4.\nGENERALIZED PROFIT BENCHMARKS\nCompeting with Gs\nDEFINITION 6.\nΥs, t\n5.\nAGGREGATION AUCTIONS\n5.1 A 3.25 Competitive Auction\n5.2 A Gs,t-based Aggregation Auction\n3 is 3.243-competitive.\n5.3 Further Reducing the Competitive Ratio\n5.3.1 AggΥm for m> 3\n5.3.2 Convex combinations of AggΥm\n6.\nA LOWER BOUND FOR CONSERVATIVE AUCTIONS\n7.\nCONCLUSIONS AND FUTURE WORK\nWe have found the optimal auction for the three-unit limitedsupply case, and shown that its structure is essentially independent of the benchmark used in its competitive analysis.\nWe have then used this auction to derive the best known auction for the unlimited supply case.\nOur work leaves many interesting open questions.\nWe found that the lower bound of [8] is matched by an auction for three bidders,\neven when competing against generalized benchmarks.\nThe most interesting open question from our work is whether the lower bound from Theorem 2 can be matched by an auction for more than three bidders.\nWe conjecture that it can.\nSecond, we consider whether our techniques can be extended to find optimal auctions for greater numbers of bidders.\nThe use of our analytic solution method requires knowledge of a restricted class of auctions which is large enough to contain an optimal auction but small enough that the optimal auction in this class can be found explicitly through analytic methods.\nNo class of auctions which meets these criteria is known even for the four bidder case.\nAlso, when the number of bidders is greater than three, it might be the case that the optimal auction is not expressible in terms of elementary functions.\nAnother interesting set of open questions concerns aggregation auctions.\nAs we show, the aggregation auction for Υ3 outperforms the aggregation auction for Υ2 and it appears that the aggregation auction for Υ3 is better than Υm for m> 3.\nWe leave verification of this conjecture for future work.\nWe also show that Υ3 is not the best three-bidder auction for use in an aggregation auction, but the auction that beats it is able to reduce the competitive ratio of the overall auction only a little bit.\nIt would be interesting to know whether for any m there is an m-aggregation auction that substantially improves on the competitive ratio of AggΥm.\nFinally, we remark that very little is known about the structure of the optimal competitive auction.\nIn our auction Υ3, the sales price for a given bidder is restricted either to be one of the other bid values or to be higher than all other bid values.\nThe optimal auction for two bidders, the 1-unit Vickrey auction, also falls within this class of auctions, as its sales prices are restricted to bid values.\nWe conjecture that an optimal auction for any number of bidders lies within this class.\nOur paper provides partial evidence for this conjecture: the lower bound of Section 6 on conservative auctions shows that the optimal auction must offer sales prices higher than any bid value if the lower bound of Theorem 2 is tight, as is conjectured.\nIt remains to show that optimal auctions otherwise only offer sales prices at bid values."},"lvl-4":{"kind":"string","value":"From Optimal Limited To Unlimited Supply Auctions\nABSTRACT\nWe investigate the class of single-round, sealed-bid auctions for a set of identical items to bidders who each desire one unit.\nWe adopt the worst-case competitive framework defined by [9, 5] that compares the profit of an auction to that of an optimal single-price sale of least two items.\nIn this paper, we first derive an optimal auction for three items, answering an open question from [8].\nSecond, we show that the form of this auction is independent of the competitive framework used.\nThird, we propose a schema for converting a given limited-supply auction into an unlimited supply auction.\nApplying this technique to our optimal auction for three items, we achieve an auction with a competitive ratio of 3.25, which improves upon the previously best-known competitive ratio of 3.39 from [7].\nFinally, we generalize a result from [8] and extend our understanding of the nature of the optimal competitive auction by showing that the optimal competitive auction occasionally offers prices that are higher than all bid values.\n1.\nINTRODUCTION\nThis problem is well studied for the auction design problem where the optimal mechanism is the one * This work was supported in part by an NSF fellowship and NSF ITR 0205633.\nthat brings the seller the most profit.\nRecently Goldberg et al. [9] introduced the use of worst-case competitive analysis (See e.g., [3]) to analyze the performance of auctions that have no knowledge of the prior distribution.\nThe goal of such work is to design an auction that achieves a large constant fraction of the profit attainable by an optimal mechanism that knows the prior distribution in advance.\nThe performance of an auction in such a worst-case competitive analysis is measured by its competitive ratio, the ratio between a benchmark performance and the auction's performance on the input distribution that maximizes this ratio.\nThe holy grail of the worstcase competitive analysis of auctions is the auction that achieves the optimal competitive ratio (as small as possible).\nSince [9] this search has led to improved understanding of the nature of the optimal auction, the techniques for on-the-fly pricing in these scenarios, and the competitive ratio of the optimal auction [5, 7, 8].\nFurthermore, we give evidence corroborating the conjecture that the form of the optimal auction is independent of the benchmark used in the auction's competitive analysis.\nThis result further validates the use of competitive analysis in gauging auction performance.\nWe consider the single item, multi-unit, unit-demand auction problem.\nIn such an auction there are many units of a single item available for sale to bidders who each desire only one unit.\nEach bidder has a valuation representing how much the item is worth to him.\nThe auction is performed by soliciting a sealed bid from each of the bidders and deciding on the allocation of units to bidders and the prices to be paid by the bidders.\nIn a truthful auction, revealing one's true valuation as one's bid is an optimal strategy for each bidder regardless of the bids of the other bidders.\nIn this paper, we will restrict our attention to truthful (a.k.a., incentive compatible or strategyproof) auctions.\nA particularly interesting special case of the auction problem is the unlimited supply case.\nIn this case the number of units for sale is at least the number of bidders in the auction.\nThis is natural for the sale of digital goods where there is negligible cost for duplicating\nand distributing the good.\nThe competitive framework introduced in [9] and further refined in [5] uses the profit of the optimal omniscient single priced mechanism that sells at least two units as the benchmark for competitive analysis.\nIn this framework for competitive analysis, an auction is said to be β-competitive if it achieves a profit that is within a factor of β ≥ 1 of the benchmark profit on every input.\nThe optimal auction is the one which is β-competitive with the minimum value of β.\nPrevious to this work, the best known auction for the unlimited supply case had a competitive ratio of 3.39 [7] and the best lower bound known was 2.42 [8].\nFor the limited supply case, auctions can achieve substantially better competitive ratios.\nWhen there are only two units for sale, the optimal auction gives a competitive ratio of 2, which matches the lower bound for two units.\nWhen there are three units for sale, the best previously known auction had a competitive ratio of 2.3, compared with a lower bound of 13/6 ≈ 2.17 [8].\nThe results of this paper are as follows:\n• We give the auction for three units that is optimally competitive against the profit of the omniscient single priced mechanism that sells at least two units.\nThis auction achieves a competitive ratio of 13/6, matching the lower bound from [8] (Section 3).\n• We show that the form of the optimal auction is independent of the benchmark used in competitive analysis.\nIn doing so, we give an optimal three bidder auction for generalized benchmarks (Section 4).\n• We give a general technique for converting a limited supply auction into an unlimited supply auction where it is possible to use the competitive ratio of the limited supply auction to obtain a bound on the competitive ratio of the unlimited supply auction.\nWe refer to auctions derived from this framework as aggregation auctions (Section 5).\n• We improve on the best known competitive ratio by proving that the aggregation auction constructed from our optimal three-unit auction is 3.25-competitive (Section 5.1).\n• Assuming that the conjecture that the optimal ~ - unit auction\nhas a competitive ratio that matches the lower bound proved in [8], we show that this optimal auction for ~ ≥ 3 on some inputs will occasionally offer prices that are higher than any bid in that input (Section 6).\nFor the three-unit case where we have shown that the lower bound of [8] is tight, this observation led to our construction of the optimal three-unit auction.\n7.\nCONCLUSIONS AND FUTURE WORK\nWe have found the optimal auction for the three-unit limitedsupply case, and shown that its structure is essentially independent of the benchmark used in its competitive analysis.\nWe have then used this auction to derive the best known auction for the unlimited supply case.\nOur work leaves many interesting open questions.\nWe found that the lower bound of [8] is matched by an auction for three bidders,\neven when competing against generalized benchmarks.\nThe most interesting open question from our work is whether the lower bound from Theorem 2 can be matched by an auction for more than three bidders.\nWe conjecture that it can.\nSecond, we consider whether our techniques can be extended to find optimal auctions for greater numbers of bidders.\nNo class of auctions which meets these criteria is known even for the four bidder case.\nAlso, when the number of bidders is greater than three, it might be the case that the optimal auction is not expressible in terms of elementary functions.\nAnother interesting set of open questions concerns aggregation auctions.\nAs we show, the aggregation auction for Υ3 outperforms the aggregation auction for Υ2 and it appears that the aggregation auction for Υ3 is better than Υm for m> 3.\nWe leave verification of this conjecture for future work.\nWe also show that Υ3 is not the best three-bidder auction for use in an aggregation auction, but the auction that beats it is able to reduce the competitive ratio of the overall auction only a little bit.\nIt would be interesting to know whether for any m there is an m-aggregation auction that substantially improves on the competitive ratio of AggΥm.\nFinally, we remark that very little is known about the structure of the optimal competitive auction.\nIn our auction Υ3, the sales price for a given bidder is restricted either to be one of the other bid values or to be higher than all other bid values.\nThe optimal auction for two bidders, the 1-unit Vickrey auction, also falls within this class of auctions, as its sales prices are restricted to bid values.\nWe conjecture that an optimal auction for any number of bidders lies within this class.\nIt remains to show that optimal auctions otherwise only offer sales prices at bid values."}}},{"rowIdx":97,"cells":{"id":{"kind":"string","value":"C-71"},"title":{"kind":"string","value":"A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks"},"abstract":{"kind":"string","value":"We propose ι, a novel index for evaluation of point-distribution. ι is the minimum distance between each pair of points normalized by the average distance between each pair of points. We find that a set of points that achieve a maximum value of ι result in a honeycomb structure. We propose that ι can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs). To validate this idea, we formulate a general sensor-grouping problem for WSNs and provide a general sensing model. We show that locally maximizing ι at sensor nodes is a good approach to solve this problem with an algorithm called Maximizing-ι Node-Deduction (MIND). Simulation results verify that MIND outperforms a greedy algorithm that exploits sensor-redundancy we design. This demonstrates a good application of employing ι in coverage-related problems for WSNs."},"keyphrases":{"kind":"list like","value":["point-distribut index","sensor-group","wireless sensor network","honeycomb structur","surveil","redund","fault toler","node-deduct process","increment coverag qualiti algorithm","sleep configur protocol","sensor coverag","sensor group"],"string":"[\n \"point-distribut index\",\n \"sensor-group\",\n \"wireless sensor network\",\n \"honeycomb structur\",\n \"surveil\",\n \"redund\",\n \"fault toler\",\n \"node-deduct process\",\n \"increment coverag qualiti algorithm\",\n \"sleep configur protocol\",\n \"sensor coverag\",\n \"sensor group\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","P","U","U","U","M","M","U","M","M"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"U\",\n \"U\",\n \"U\",\n \"M\",\n \"M\",\n \"U\",\n \"M\",\n \"M\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks Yangfan Zhou Haixuan Yang Michael R. Lyu Edith C.-H.\nNgai Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong, China {yfzhou, hxyang, lyu, chngai}@cse.\ncuhk.edu.hk ABSTRACT We propose ι, a novel index for evaluation of point-distribution.\nι is the minimum distance between each pair of points normalized by the average distance between each pair of points.\nWe find that a set of points that achieve a maximum value of ι result in a honeycomb structure.\nWe propose that ι can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs).\nTo validate this idea, we formulate a general sensorgrouping problem for WSNs and provide a general sensing model.\nWe show that locally maximizing ι at sensor nodes is a good approach to solve this problem with an algorithm called Maximizingι Node-Deduction (MIND).\nSimulation results verify that MIND outperforms a greedy algorithm that exploits sensor-redundancy we design.\nThis demonstrates a good application of employing ι in coverage-related problems for WSNs.\nCategories and Subject Descriptors C.2.4 [Computer - Communication Networks]: Network Architecture and Design; C.3 [Special-Purpose and Application-Based Systems]: Realtime and Embedded Systems General Terms Theory, Algorithms, Design, Verification, Performance 1.\nINTRODUCTION A wireless sensor network (WSN) consists of a large number of in-situ battery-powered sensor nodes.\nA WSN can collect the data about physical phenomena of interest [1].\nThere are many potential applications of WSNs, including environmental monitoring and surveillance, etc. [1][11].\nIn many application scenarios, WSNs are employed to conduct surveillance tasks in adverse, or even worse, in hostile working environments.\nOne major problem caused is that sensor nodes are subjected to failures.\nTherefore, fault tolerance of a WSN is critical.\nOne way to achieve fault tolerance is that a WSN should contain a large number of redundant nodes in order to tolerate node failures.\nIt is vital to provide a mechanism that redundant nodes can be working in sleeping mode (i.e., major power-consuming units such as the transceiver of a redundant sensor node can be shut off) to save energy, and thus to prolong the network lifetime.\nRedundancy should be exploited as much as possible for the set of sensors that are currently taking charge in the surveillance work of the network area [6].\nWe find that the minimum distance between each pair of points normalized by the average distance between each pair of points serves as a good index to evaluate the distribution of the points.\nWe call this index, denoted by ι, the normalized minimum distance.\nIf points are moveable, we find that maximizing ι results in a honeycomb structure.\nThe honeycomb structure poses that the coverage efficiency is the best if each point represents a sensor node that is providing surveillance work.\nEmploying ι in coverage-related problems is thus deemed promising.\nThis enlightens us that maximizing ι is a good approach to select a set of sensors that are currently taking charge in the surveillance work of the network area.\nTo explore the effectiveness of employing ι in coverage-related problems, we formulate a sensorgrouping problem for high-redundancy WSNs.\nAn algorithm called Maximizing-ι Node-Deduction (MIND) is proposed in which redundant sensor nodes are removed to obtain a large ι for each set of sensors that are currently taking charge in the surveillance work of the network area.\nWe also introduce another greedy solution called Incremental Coverage Quality Algorithm (ICQA) for this problem, which serves as a benchmark to evaluate MIND.\nThe main contribution of this paper is twofold.\nFirst, we introduce a novel index ι for evaluation of point-distribution.\nWe show that maximizing ι of a WSN results in low redundancy of the network.\nSecond, we formulate a general sensor-grouping problem for WSNs and provide a general sensing model.\nWith the MIND algorithm we show that locally maximizing ι among each sensor node and its neighbors is a good approach to solve this problem.\nThis demonstrates a good application of employing ι in coveragerelated problems.\nThe rest of the paper is organized as follows.\nIn Section 2, we introduce our point-distribution index ι.\nWe survey related work and formulate a sensor-grouping problem together with a general sensing model in Section 3.\nSection 4 investigates the application of ι in this grouping problem.\nWe propose MIND for this problem 1171 and introduce ICQA as a benchmark.\nIn Section 5, we present our simulation results in which MIND and ICQA are compared.\nSection 6 provides conclusion remarks.\n2.\nTHE NORMALIZED MINIMUM DISTANCE ι: A POINT-DISTRIBUTION INDEX Suppose there are n points in a Euclidean space Ω.\nThe coordinates of these points are denoted by xi (i = 1, ..., n).\nIt may be necessary to evaluate how the distribution of these points is.\nThere are many metrics to achieve this goal.\nFor example, the Mean Square Error from these points to their mean value can be employed to calculate how these points deviate from their mean (i.e., their central).\nIn resource-sharing evaluation, the Global Fairness Index (GFI) is often employed to measure how even the resource distributes among these points [8], when xi represents the amount of resource that belong to point i.\nIn WSNs, GFI is usually used to calculate how even the remaining energy of sensor nodes is.\nWhen n is larger than 2 and the points do not all overlap (That points all overlap means xi = xj, ∀ i, j = 1, 2, ..., n).\nWe propose a novel index called the normalized minimum distance, namely ι, to evaluate the distribution of the points.\nι is the minimum distance between each pair of points normalized by the average distance between each pair of points.\nIt is calculated by: ι = min(||xi − xj||) µ (∀ i, j = 1, 2, ..., n; and i = j) (1) where ||xi − xj|| denotes the Euclidean distance between point i and point j in Ω, the min(·) function calculates the minimum distance between each pair of points, and µ is the average distance between each pair of points, which is: µ = ( Pn i=1 Pn j=1,j=i ||xi − xj||) n(n − 1) (2) ι measures how well the points separate from one another.\nObviously, ι is in interval [0, 1].\nι is equal to 1 if and only if n is equal to 3 and these three points forms an equilateral triangle.\nι is equal to zero if any two points overlap.\nι is a very interesting value of a set of points.\nIf we consider each xi (∀i = 1, ..., n) is a variable in Ω, how these n points would look like if ι is maximized?\nAn algorithm is implemented to generate the topology in which ι is locally maximized (The algorithm can be found in [19]).\nWe consider a 2-dimensional space.\nWe select n = 10, 20, 30, ..., 100 and perform this algorithm.\nIn order to avoid that the algorithm converge to local optimum, we select different random seeds to generate the initial points for 1000 time and obtain the best one that results in the largest ι when the algorithm converges.\nFigure 1 demonstrates what the resulting topology looks like when n = 20 as an example.\nSuppose each point represents a sensor node.\nIf the sensor coverage model is the Boolean coverage model [15][17][18][14] and the coverage radius of each node is the same.\nIt is exciting to see that this topology results in lowest redundancy because the Vonoroi diagram [2] formed by these nodes (A Vonoroi diagram formed by a set of nodes partitions a space into a set of convex polygons such that points inside a polygon are closest to only one particular node) is a honeycomb-like structure1 .\nThis enlightens us that ι may be employed to solve problems related to sensor-coverage of an area.\nIn WSNs, it is desirable 1 This is how base stations of a wireless cellular network are deployed and why such a network is called a cellular one.\n0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160 X Y Figure 1: Node Number = 20, ι = 0.435376 that the active sensor nodes that are performing surveillance task should separate from one another.\nUnder the constraint that the sensing area should be covered, the more each node separates from the others, the less the redundancy of the coverage is.\nι indicates the quality of such separation.\nIt should be useful for approaches on sensor-coverage related problems.\nIn our following discussions, we will show the effectiveness of employing ι in sensor-grouping problem.\n3.\nTHE SENSOR-GROUPING PROBLEM In many application scenarios, to achieve fault tolerance, a WSN contains a large number of redundant nodes in order to tolerate node failures.\nA node sleeping-working schedule scheme is therefore highly desired to exploit the redundancy of working sensors and let as many nodes as possible sleep.\nMuch work in the literature is on this issue [6].\nYan et al introduced a differentiated service in which a sensor node finds out its responsible working duration with cooperation of its neighbors to ensure the coverage of sampled points [17].\nYe et al developed PEAS in which sensor nodes wake up randomly over time, probe their neighboring nodes, and decide whether they should begin to take charge of surveillance work [18].\nXing et al exploited a probabilistic distributed detection model with a protocol called Coordinating Grid (Co-Grid) [16].\nWang et al designed an approach called Coverage Configuration Protocol (CCP) which introduced the notion that the coverage degree of intersection-points of the neighboring nodes'' sensing-perimeters indicates the coverage of a convex region [15].\nIn our recent work [7], we also provided a sleeping configuration protocol, namely SSCP, in which sleeping eligibility of a sensor node is determined by a local Voronoi diagram.\nSSCP can provide different levels of redundancy to maintain different requirements of fault tolerance.\nThe major feature of the aforementioned protocols is that they employ online distributed and localized algorithms in which a sensor node determines its sleeping eligibility and/or sleeping time based on the coverage requirement of its sensing area with some information provided by its neighbors.\nAnother major approach for sensor node sleeping-working scheduling issue is to group sensor nodes.\nSensor nodes in a network are divided into several disjoint sets.\nEach set of sensor nodes are able to maintain the required area surveillance work.\nThe sensor nodes are scheduled according to which set they belong to.\nThese sets work successively.\nOnly one set of sensor nodes work at any time.\nWe call the issue sensor-grouping problem.\nThe major advantage of this approach is that it avoids the overhead caused by the processes of coordination of sensor nodes to make decision on whether a sensor node is a candidate to sleep or 1172 work and how long it should sleep or work.\nSuch processes should be performed from time to time during the lifetime of a network in many online distributed and localized algorithms.\nThe large overhead caused by such processes is the main drawback of the online distributed and localized algorithms.\nOn the contrary, roughly speaking, this approach groups sensor nodes in one time and schedules when each set of sensor nodes should be on duty.\nIt does not require frequent decision-making on working/sleeping eligibility2 .\nIn [13] by Slijepcevic et al, the sensing area is divided into regions.\nSensor nodes are grouped with the most-constrained leastconstraining algorithm.\nIt is a greedy algorithm in which the priority of selecting a given sensor is determined by how many uncovered regions this sensor covers and the redundancy caused by this sensor.\nIn [5] by Cardei et al, disjoint sensor sets are modeled as disjoint dominating sets.\nAlthough maximum dominating sets computation is NP-complete, the authors proposed a graphcoloring based algorithm.\nCardei et al also studied similar problem in the domain of covering target points in [4].\nThe NP-completeness of the problem is proved and a heuristic that computes the sets are proposed.\nThese algorithms are centralized solutions of sensorgrouping problem.\nHowever, global information (e.g., the location of each in-network sensor node) of a large scale WSN is also very expensive to obtained online.\nAlso it is usually infeasible to obtain such information before sensor nodes are deployed.\nFor example, sensor nodes are usually deployed in a random manner and the location of each in-network sensor node is determined only after a node is deployed.\nThe solution of sensor-grouping problem should only base on locally obtainable information of a sensor node.\nThat is to say, nodes should determine which group they should join in a fully distributed way.\nHere locally obtainable information refers to a node``s local information and the information that can be directly obtained from its adjacent nodes, i.e., nodes within its communication range.\nIn Subsection 3.1, we provide a general problem formulation of the sensor-grouping problem.\nDistributed-solution requirement is formulated in this problem.\nIt is followed by discussion in Subsection 3.2 on a general sensing model, which serves as a given condition of the sensor-grouping problem formulation.\nTo facilitate our discussions, the notations in our following discussions are described as follows.\n• n: The number in-network sensor nodes.\n• S(j) (j = 1, 2, ..., m): The jth set of sensor nodes where m is the number of sets.\n• L(i) (i = 1, 2, ..., n): The physical location of node i. • φ: The area monitored by the network: i.e., the sensing area of the network.\n• R: The sensing radius of a sensor node.\nWe assume that a sensor node can only be responsible to monitor a circular area centered at the node with a radius equal to R.\nThis is a usual assumption in work that addresses sensor-coverage related problems.\nWe call this circular area the sensing area of a node.\n3.1 Problem Formulation We assume that each sensor node can know its approximate physical location.\nThe approximate location information is obtainable if each sensor node carries a GPS receiver or if some localization algorithms are employed (e.g., [3]).\n2 Note that if some nodes die, a re-grouping process might also be performed to exploit the remaining nodes in a set of sensor nodes.\nHow to provide this mechanism is beyond the scope of this paper and yet to be explored.\nProblem 1.\nGiven: • The set of each sensor node i``s sensing neighbors N(i) and the location of each member in N(i); • A sensing model which quantitatively describes how a point P in area φ is covered by sensor nodes that are responsible to monitor this point.\nWe call this quantity the coverage quality of P. • The coverage quality requirement in φ, denoted by s.\nWhen the coverage of a point is larger than this threshold, we say this point is covered.\nFor each sensor node i, make a decision on which group S(j) it should join so that: • Area φ can be covered by sensor nodes in each set S(j) • m, the number of sets S(j) is maximized.\nIn this formulation, we call sensor nodes within a circular area centered at a sensor node i with a radius equal to 2 · R the sensing neighbors of node i.\nThis is because sensors nodes in this area, together with node i, may be cooperative to ensure the coverage of a point inside node i``s sensing area.\nWe assume that the communication range of a sensor node is larger than 2 · R, which is also a general assumption in work that addresses sensor-coverage related problems.\nThat is to say, the first given condition in Problem 1 is the information that can be obtained directly from a node``s adjacent nodes.\nIt is therefore locally obtainable information.\nThe last two given conditions in this problem formulation can be programmed into a node before it is deployed or by a node-programming protocol (e.g., [9]) during network runtime.\nTherefore, the given conditions can all be easily obtained by a sensor-grouping scheme with fully distributed implementation.\nWe reify this problem with a realistic sensing model in next subsection.\n3.2 A General Sensing Model As WSNs are usually employed to monitor possible events in a given area, it is therefore a design requirement that an event occurring in the network area must/may be successfully detected by sensors.\nThis issue is usually formulated as how to ensure that an event signal omitted in an arbitrary point in the network area can be detected by sensor nodes.\nObviously, a sensing model is required to address this problem so that how a point in the network area is covered can be modeled and quantified.\nThus the coverage quality of a WSN can be evaluated.\nDifferent applications of WSNs employ different types of sensors, which surely have widely different theoretical and physical characteristics.\nTherefore, to fulfill different application requirements, different sensing models should be constructed based on the characteristics of the sensors employed.\nA simple theoretical sensing model is the Boolean sensing model [15][18][17][14].\nBoolean sensing model assumes that a sensor node can always detect an event occurring in its responsible sensing area.\nBut most sensors detect events according to the signal strength sensed.\nEvent signals usually fade in relation to the physical distance between an event and the sensor.\nThe larger the distance, the weaker the event signals that can be sensed by the sensor, which results in a reduction of the probability that the event can be successfully detected by the sensor.\nAs in WSNs, event signals are usually electromagnetic, acoustic, or photic signals, they fade exponentially with the increasing of 1173 their transmit distance.\nSpecifically, the signal strength E(d) of an event that is received by a sensor node satisfies: E(d) = α dβ (3) where d is the physical distance from the event to the sensor node; α is related to the signal strength omitted by the event; and β is signal fading factor which is typically a positive number larger than or equal to 2.\nUsually, α and β are considered as constants.\nBased on this notion, to be more reasonable, researchers propose collaborative sensing model to capture application requirements: Area coverage can be maintained by a set of collaborative sensor nodes: For a point with physical location L, the point is considered covered by the collaboration of i sensors (denoted by k1, ..., ki) if and only if the following two equations holds [7][10][12].\n∀j = 1, ..., i; L(kj) − L < R. (4) C(L) = iX j=1 (E( L(kj) − L ) > s. (5) C(L) is regarded as the coverage quality of location L in the network area [7][10][12].\nHowever, we notice that defining the sensibility as the sum of the sensed signal strength by each collaborative sensor implies a very special application: Applications must employ the sum of the signal strength to achieve decision-making.\nTo capture generally realistic application requirement, we modify the definition described in Equation (5).\nThe model we adopt in this paper is described in details as follows.\nWe consider the probability P(L, kj ) that an event located at L can be detected by sensor kj is related to the signal strength sensed by kj.\nFormally, P(L, kj) = γE(d) = δ ( L(kj) − L / + 1)β , (6) where γ is a constant and δ = γα is a constant too.\nnormalizes the distance to a proper scale and the +1 item is to avoid infinite value of P(L, kj).\nThe probability that an event located at L can be detected by any collaborative sensors that satisfied Equation (4) is: P (L) = 1 − iY j=1 (1 − P(L, kj )).\n(7) As the detection probability P (L) reasonably determines how an event occurring at location L can be detected by the networks, it is a good measure of the coverage quality of location L in a WSN.\nSpecifically, Equation (5) is modified to: C(L) = P (L) = 1 − iY j=1 [1 − δ ( L(kj) − L / + 1)β ] > s. (8) To sum it up, we consider a point at location L is covered if Equation (4) and (8) hold.\n4.\nMAXIMIZING-ι NODE-DEDUCTION ALGORITHM FOR SENSOR-GROUPING PROBLEM Before we process to introduce algorithms to solve the sensor grouping problem, let us define the margin (denoted by θ) of an area φ monitored by the network as the band-like marginal area of φ and all the points on the outer perimeter of θ is ρ distance away from all the points on the inner perimeter of θ.\nρ is called the margin length.\nIn a practical network, sensor nodes are usually evenly deployed in the network area.\nObviously, the number of sensor nodes that can sense an event occurring in the margin of the network is smaller than the number of sensor nodes that can sense an event occurring in other area of the network.\nBased on this consideration, in our algorithm design, we ensure the coverage quality of the network area except the margin.\nThe information on φ and ρ is networkbased.\nEach in-network sensor node can be pre-programmed or on-line informed about φ and ρ, and thus calculate whether a point in its sensing area is in the margin or not.\n4.1 Maximizing-ι Node-Deduction Algorithm The node-deduction process of our Maximizing-ι Node-Deduction Algorithm (MIND) is simple.\nA node i greedily maximizes ι of the sub-network composed by itself, its ungrouped sensing neighbors, and the neighbors that are in the same group of itself.\nUnder the constraint that the coverage quality of its sensing area should be ensured, node i deletes nodes in this sub-network one by one.\nThe candidate to be pruned satisfies that: • It is an ungrouped node.\n• The deletion of the node will not result in uncovered-points inside the sensing area of i.\nA candidate is deleted if the deletion of the candidate results in largest ι of the sub-network compared to the deletion of other candidates.\nThis node-deduction process continues until no candidate can be found.\nThen all the ungrouped sensing neighbors that are not deleted are grouped into the same group of node i.\nWe call the sensing neighbors that are in the same group of node i the group sensing neighbors of node i.\nWe then call node i a finished node, meaning that it has finished the above procedure and the sensing area of the node is covered.\nThose nodes that have not yet finished this procedure are called unfinished nodes.\nThe above procedure initiates at a random-selected node that is not in the margin.\nThe node is grouped to the first group.\nIt calculates the resulting group sensing neighbors of it based on the above procedure.\nIt informs these group sensing neighbors that they are selected in the group.\nThen it hands over the above procedure to an unfinished group sensing neighbors that is the farthest from itself.\nThis group sensing neighbor continues this procedure until no unfinished neighbor can be found.\nThen the first group is formed (Algorithmic description of this procedure can be found at [19]).\nAfter a group is formed, another random-selected ungrouped node begins to group itself to the second group and initiates the above procedure.\nIn this way, groups are formed one by one.\nWhen a node that involves in this algorithm found out that the coverage quality if its sensing area, except what overlaps the network margin, cannot be ensured even if all its ungrouped sensing neighbors are grouped into the same group as itself, the algorithm stops.\nMIND is based on locally obtainable information of sensor nodes.\nIt is a distributed algorithm that serves as an approximate solution of Problem 1.\n4.2 Incremental Coverage Quality Algorithm: A Benchmark for MIND To evaluate the effectiveness of introducing ι in the sensor-group problem, another algorithm for sensor-group problem called Incremental Coverage Quality Algorithm (ICQA) is designed.\nOur aim 1174 is to evaluate how an idea, i.e., MIND, based on locally maximize ι performs.\nIn ICQA, a node-selecting process is as follows.\nA node i greedily selects an ungrouped sensing neighbor in the same group as itself one by one, and informs the neighbor it is selected in the group.\nThe criterion is: • The selected neighbor is responsible to provide surveillance work for some uncovered parts of node i``s sensing area.\n(i.e., the coverage quality requirement of the parts is not fulfilled if this neighbor is not selected.)\n• The selected neighbor results in highest improvement of the coverage quality of the neighbor``s sensing area.\nThe improvement of the coverage quality, mathematically, should be the integral of the the improvements of all points inside the neighbor``s sensing area.\nA numerical approximation is employed to calculate this improvement.\nDetails are presented in our simulation study.\nThis node-selecting process continues until the sensing area of node i is entirely covered.\nIn this way, node i``s group sensing neighbors are found.\nThe above procedure is handed over as what MIND does and new groups are thus formed one by one.\nAnd the condition that ICQA stops is the same as MIND.\nICQA is also based on locally obtainable information of sensor nodes.\nICQA is also a distributed algorithm that serves as an approximate solution of Problem 1.\n5.\nSIMULATION RESULTS To evaluate the effectiveness of employing ι in sensor-grouping problem, we build simulation surveillance networks.\nWe employ MIND and ICQA to group the in-network sensor nodes.\nWe compare the grouping results with respect to how many groups both algorithms find and how the performance of the resulting groups are.\nDetailed settings of the simulation networks are shown in Table 1.\nIn simulation networks, sensor nodes are randomly deployed in a uniform manner in the network area.\nTable 1: The settings of the simulation networks Area of sensor field 400m*400m ρ 20m R 80m α, β, γ and 1.0, 2.0, 1.0 and 100.0 s 0.6 For evaluating the coverage quality of the sensing area of a node, we divide the sensing area of a node into several regions and regard the coverage quality of the central point in each region as a representative of the coverage quality of the region.\nThis is a numerical approximation.\nLarger number of such regions results in better approximation.\nAs sensor nodes are with low computational capacity, there is a tradeoff between the number of such regions and the precision of the resulting coverage quality of the sensing area of a node.\nIn our simulation study, we set this number 12.\nFor evaluating the improvement of coverage quality in ICQA, we sum up all the improvements at each region-center as the total improvement.\n5.1 Number of Groups Formed by MIND and ICQA We set the total in-network node number to different values and let the networks perform MIND and ICQA.\nFor each n, simulations run with several random seeds to generate different networks.\nResults are averaged.\nFigure 2 shows the group numbers found in networks with different n``s. 500 1000 1500 2000 0 5 10 15 20 25 30 35 40 45 50 Total in−network node number Totalnumberofgroupsfound ICQA MMNP Figure 2: The number of groups found by MIND and ICQA We can see that MIND always outperforms ICQA in terms of the number of groups formed.\nObviously, the larger the number of groups can be formed, the more the redundancy of each group is exploited.\nThis output shows that an approach like MIND that aim to maximize ι of the resulting topology can exploits redundancy well.\nAs an example, in case that n = 1500, the results of five networks are listed in Table 2.\nTable 2: The grouping results of five networks with n = 1500 Net MIND ICQA MIND ICQA Group Number Group Number Average ι Average ι 1 34 31 0.145514 0.031702 2 33 30 0.145036 0.036649 3 33 31 0.156483 0.033578 4 32 31 0.152671 0.029030 5 33 32 0.146560 0.033109 The difference between the average ι of the groups in each network shows that groups formed by MIND result in topologies with larger ι``s.\nIt demonstrates that ι is good indicator of redundancy in different networks.\n5.2 The Performance of the Resulting Groups Although MIND forms more groups than ICQA does, which implies longer lifetime of the networks, another importance consideration is how these groups formed by MIND and ICQA perform.\nWe let 10000 events randomly occur in the network area except the margin.\nWe compare how many events happen at the locations where the quality is less than the requirement s = 0.6 when each resulting group is conducting surveillance work (We call the number of such events the failure number of group).\nFigure 3 shows the average failure numbers of the resulting groups when different node numbers are set.\nWe can see that the groups formed by MIND outperform those formed by ICQA because the groups formed by MIND result in lower failure numbers.\nThis further demonstrates that MIND is a good approach for sensor-grouping problem.\n1175 500 1000 1500 2000 0 10 20 30 40 50 60 Total in−network node number averagefailurenumbers ICQA MMNP Figure 3: The failure numbers of MIND and ICQA 6.\nCONCLUSION This paper proposes ι, a novel index for evaluation of pointdistribution.\nι is the minimum distance between each pair of points normalized by the average distance between each pair of points.\nWe find that a set of points that achieve a maximum value of ι result in a honeycomb structure.\nWe propose that ι can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs).\nWe set out to validate this idea by employing ι to a sensorgrouping problem.\nWe formulate a general sensor-grouping problem for WSNs and provide a general sensing model.\nWith an algorithm called Maximizing-ι Node-Deduction (MIND), we show that maximizing ι at sensor nodes is a good approach to solve this problem.\nSimulation results verify that MIND outperforms a greedy algorithm that exploits sensor-redundancy we design in terms of the number and the performance of the groups formed.\nThis demonstrates a good application of employing ι in coverage-related problems.\n7.\nACKNOWLEDGEMENT The work described in this paper was substantially supported by two grants, RGC Project No.\nCUHK4205/04E and UGC Project No.\nAoE/E-01/99, of the Hong Kong Special Administrative Region, China.\n8.\nREFERENCES [1] I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci.\nA survey on wireless sensor networks.\nIEEE Communications Magazine, 40(8):102-114, 2002.\n[2] F. Aurenhammer.\nVononoi diagram - a survey of a fundamental geometric data structure.\nACM Computing Surveys, 23(2):345-405, September 1991.\n[3] N. Bulusu, J. Heidemann, and D. Estrin.\nGPS-less low-cost outdoor localization for very small devices.\nIEEE Personal Communication, October 2000.\n[4] M. Cardei and D.-Z.\nDu.\nImproving wireless sensor network lifetime through power aware organization.\nACM Wireless Networks, 11(3), May 2005.\n[5] M. Cardei, D. MacCallum, X. Cheng, M. Min, X. Jia, D. Li, and D.-Z.\nDu.\nWireless sensor networks with energy efficient organization.\nJournal of Interconnection Networks, 3(3-4), December 2002.\n[6] M. Cardei and J. Wu.\nCoverage in wireless sensor networks.\nIn Handbook of Sensor Networks, (eds.\nM. Ilyas and I. Magboub), CRC Press, 2004.\n[7] X. Chen and M. R. Lyu.\nA sensibility-based sleeping configuration protocol for dependable wireless sensor networks.\nCSE Technical Report, The Chinese University of Hong Kong, 2005.\n[8] R. Jain, W. Hawe, and D. Chiu.\nA quantitative measure of fairness and discrimination for resource allocation in shared computer systems.\nTechnical Report DEC-TR-301, September 1984.\n[9] S. S. Kulkarni and L. Wang.\nMNP: Multihop network reprogramming service for sensor networks.\nIn Proc.\nof the 25th International Conference on Distributed Computing Systems (ICDCS), June 2005.\n[10] B. Liu and D. Towsley.\nA study on the coverage of large-scale sensor networks.\nIn Proc.\nof the 1st IEEE International Conference on Mobile ad-hoc and Sensor Systems, Fort Lauderdale, FL, October 2004.\n[11] A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson.\nWireless sensor networks for habitat monitoring.\nIn Proc.\nof the ACM International Workshop on Wireless Sensor Networks and Applications, 2002.\n[12] S. Megerian, F. Koushanfar, G. Qu, G. Veltri, and M. Potkonjak.\nExplosure in wirless sensor networks: Theory and pratical solutions.\nWireless Networks, 8, 2002.\n[13] S. Slijepcevic and M. Potkonjak.\nPower efficient organization of wireless sensor networks.\nIn Proc.\nof the IEEE International Conference on Communications (ICC), volume 2, Helsinki, Finland, June 2001.\n[14] D. Tian and N. D. Georganas.\nA node scheduling scheme for energy conservation in large wireless sensor networks.\nWireless Communications and Mobile Computing, 3:272-290, May 2003.\n[15] X. Wang, G. Xing, Y. Zhang, C. Lu, R. Pless, and C. Gill.\nIntegrated coverage and connectivity configuration in wireless sensor networks.\nIn Proc.\nof the 1st ACM International Conference on Embedded Networked Sensor Systems (SenSys), Los Angeles, CA, November 2003.\n[16] G. Xing, C. Lu, R. Pless, and J. A. O´ Sullivan.\nCo-Grid: an efficient converage maintenance protocol for distributed sensor networks.\nIn Proc.\nof the 3rd International Symposium on Information Processing in Sensor Networks (IPSN), Berkeley, CA, April 2004.\n[17] T. Yan, T. He, and J. A. Stankovic.\nDifferentiated surveillance for sensor networks.\nIn Proc.\nof the 1st ACM International Conference on Embedded Networked Sensor Systems (SenSys), Los Angeles, CA, November 2003.\n[18] F. Ye, G. Zhong, J. Cheng, S. Lu, and L. Zhang.\nPEAS: A robust energy conserving protocol for long-lived sensor networks.\nIn Proc.\nof the 23rd International Conference on Distributed Computing Systems (ICDCS), Providence, Rhode Island, May 2003.\n[19] Y. Zhou, H. Yang, and M. R. Lyu.\nA point-distribution index and its application in coverage-related problems.\nCSE Technical Report, The Chinese University of Hong Kong, 2006.\n1176"},"lvl-2":{"kind":"string","value":"A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks\nABSTRACT\nWe propose t, a novel index for evaluation of point-distribution.\nt is the minimum distance between each pair of points normalized by the average distance between each pair of points.\nWe find that a set of points that achieve a maximum value of t result in a honeycomb structure.\nWe propose that t can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs).\nTo validate this idea, we formulate a general sensorgrouping problem for WSNs and provide a general sensing model.\nWe show that locally maximizing t at sensor nodes is a good approach to solve this problem with an algorithm called Maximizingt Node-Deduction (MIND).\nSimulation results verify that MIND outperforms a greedy algorithm that exploits sensor-redundancy we design.\nThis demonstrates a good application of employing t in coverage-related problems for WSNs.\n1.\nINTRODUCTION\nA wireless sensor network (WSN) consists of a large number of in-situ battery-powered sensor nodes.\nA WSN can collect the data about physical phenomena of interest [1].\nThere are many potential applications of WSNs, including environmental monitoring and surveillance, etc. [1] [11].\nIn many application scenarios, WSNs are employed to conduct surveillance tasks in adverse, or even worse, in hostile working environments.\nOne major problem caused is that sensor nodes are subjected to failures.\nTherefore, fault tolerance of a WSN is critical.\nOne way to achieve fault tolerance is that a WSN should contain a large number of redundant nodes in order to tolerate node failures.\nIt is vital to provide a mechanism that redundant nodes can be working in sleeping mode (i.e., major power-consuming units such as the transceiver of a redundant sensor node can be shut off) to save energy, and thus to prolong the network lifetime.\nRedundancy should be exploited as much as possible for the set of sensors that are currently taking charge in the surveillance work of the network area [6].\nWe find that the minimum distance between each pair of points normalized by the average distance between each pair of points serves as a good index to evaluate the distribution of the points.\nWe call this index, denoted by t, the normalized minimum distance.\nIf points are moveable, we find that maximizing t results in a honeycomb structure.\nThe honeycomb structure poses that the coverage efficiency is the best if each point represents a sensor node that is providing surveillance work.\nEmploying t in coverage-related problems is thus deemed promising.\nThis enlightens us that maximizing t is a good approach to select a set of sensors that are currently taking charge in the surveillance work of the network area.\nTo explore the effectiveness of employing t in coverage-related problems, we formulate a sensorgrouping problem for high-redundancy WSNs.\nAn algorithm called Maximizing-t Node-Deduction (MIND) is proposed in which redundant sensor nodes are removed to obtain a large t for each set of sensors that are currently taking charge in the surveillance work of the network area.\nWe also introduce another greedy solution called Incremental Coverage Quality Algorithm (ICQA) for this problem, which serves as a benchmark to evaluate MIND.\nThe main contribution of this paper is twofold.\nFirst, we introduce a novel index t for evaluation of point-distribution.\nWe show that maximizing t of a WSN results in low redundancy of the network.\nSecond, we formulate a general sensor-grouping problem for WSNs and provide a general sensing model.\nWith the MIND algorithm we show that locally maximizing t among each sensor node and its neighbors is a good approach to solve this problem.\nThis demonstrates a good application of employing t in coveragerelated problems.\nThe rest of the paper is organized as follows.\nIn Section 2, we introduce our point-distribution index t.\nWe survey related work and formulate a sensor-grouping problem together with a general sensing model in Section 3.\nSection 4 investigates the application of t in this grouping problem.\nWe propose MIND for this problem\nand introduce ICQA as a benchmark.\nIn Section 5, we present our simulation results in which MIND and ICQA are compared.\nSection 6 provides conclusion remarks.\n2.\nTHE NORMALIZED MINIMUM DISTANCE ι: A POINT-DISTRIBUTION INDEX\nSuppose there are n points in a Euclidean space Ω.\nThe coordinates of these points are denoted by xi (i = 1,..., n).\nIt may be necessary to evaluate how the distribution of these points is.\nThere are many metrics to achieve this goal.\nFor example, the Mean Square Error from these points to their mean value can be employed to calculate how these points deviate from their mean (i.e., their central).\nIn resource-sharing evaluation, the Global Fairness Index (GFI) is often employed to measure how even the resource distributes among these points [8], when xi represents the amount of resource that belong to point i.\nIn WSNs, GFI is usually used to calculate how even the remaining energy of sensor nodes is.\nWhen n is larger than 2 and the points do not all overlap (That points all overlap means xi = xj, ∀ i, j = 1, 2,..., n).\nWe propose a novel index called the normalized minimum distance, namely ι, to evaluate the distribution of the points.\nι is the minimum distance\nwhere | | xi − xj | | denotes the Euclidean distance between point i and point j in Ω, the min (·) function calculates the minimum distance between each pair of points, and µ is the average distance between each pair of points, which is:\nι measures how well the points separate from one another.\nObviously, ι is in interval [0, 1].\nι is equal to 1 if and only if n is equal to 3 and these three points forms an equilateral triangle.\nι is equal to zero if any two points overlap.\nι is a very interesting value of a set of points.\nIf we consider each xi (∀ i = 1,..., n) is a variable in Ω, how these n points would look like if ι is maximized?\nAn algorithm is implemented to generate the topology in which ι is locally maximized (The algorithm can be found in [19]).\nWe consider a 2-dimensional space.\nWe select n = 10, 20, 30,..., 100 and perform this algorithm.\nIn order to avoid that the algorithm converge to local optimum, we select different random seeds to generate the initial points for 1000 time and obtain the best one that results in the largest ι when the algorithm converges.\nFigure 1 demonstrates what the resulting topology looks like when n = 20 as an example.\nSuppose each point represents a sensor node.\nIf the sensor coverage model is the Boolean coverage model [15] [17] [18] [14] and the coverage radius of each node is the same.\nIt is exciting to see that this topology results in lowest redundancy because the Vonoroi diagram [2] formed by these nodes (A Vonoroi diagram formed by a set of nodes partitions a space into a set of convex polygons such that points inside a polygon are closest to only one particular node) is a honeycomb-like structure1.\nThis enlightens us that ι may be employed to solve problems related to sensor-coverage of an area.\nIn WSNs, it is desirable\nFigure 1: Node Number = 20, ι = 0.435376\nthat the active sensor nodes that are performing surveillance task should separate from one another.\nUnder the constraint that the sensing area should be covered, the more each node separates from the others, the less the redundancy of the coverage is.\nι indicates the quality of such separation.\nIt should be useful for approaches on sensor-coverage related problems.\nIn our following discussions, we will show the effectiveness of employing ι in sensor-grouping problem.\n3.\nTHE SENSOR-GROUPING PROBLEM\nIn many application scenarios, to achieve fault tolerance, a WSN contains a large number of redundant nodes in order to tolerate node failures.\nA node sleeping-working schedule scheme is therefore highly desired to exploit the redundancy of working sensors and let as many nodes as possible sleep.\nMuch work in the literature is on this issue [6].\nYan et al introduced a differentiated service in which a sensor node finds out its responsible working duration with cooperation of its neighbors to ensure the coverage of sampled points [17].\nYe et al developed PEAS in which sensor nodes wake up randomly over time, probe their neighboring nodes, and decide whether they should begin to take charge of surveillance work [18].\nXing et al exploited a probabilistic distributed detection model with a protocol called Coordinating Grid (Co-Grid) [16].\nWang et al designed an approach called Coverage Configuration Protocol (CCP) which introduced the notion that the coverage degree of intersection-points of the neighboring nodes' sensing-perimeters indicates the coverage of a convex region [15].\nIn our recent work [7], we also provided a sleeping configuration protocol, namely SSCP, in which sleeping eligibility of a sensor node is determined by a local Voronoi diagram.\nSSCP can provide different levels of redundancy to maintain different requirements of fault tolerance.\nThe major feature of the aforementioned protocols is that they employ online distributed and localized algorithms in which a sensor node determines its sleeping eligibility and/or sleeping time based on the coverage requirement of its sensing area with some information provided by its neighbors.\nAnother major approach for sensor node sleeping-working scheduling issue is to group sensor nodes.\nSensor nodes in a network are divided into several disjoint sets.\nEach set of sensor nodes are able to maintain the required area surveillance work.\nThe sensor nodes are scheduled according to which set they belong to.\nThese sets work successively.\nOnly one set of sensor nodes work at any time.\nWe call the issue sensor-grouping problem.\nThe major advantage of this approach is that it avoids the overhead caused by the processes of coordination of sensor nodes to make decision on whether a sensor node is a candidate to sleep or\nwork and how long it should sleep or work.\nSuch processes should be performed from time to time during the lifetime of a network in many online distributed and localized algorithms.\nThe large overhead caused by such processes is the main drawback of the online distributed and localized algorithms.\nOn the contrary, roughly speaking, this approach groups sensor nodes in one time and schedules when each set of sensor nodes should be on duty.\nIt does not require frequent decision-making on working/sleeping eligibility 2.\nIn [13] by Slijepcevic et al, the sensing area is divided into regions.\nSensor nodes are grouped with the most-constrained leastconstraining algorithm.\nIt is a greedy algorithm in which the priority of selecting a given sensor is determined by how many uncovered regions this sensor covers and the redundancy caused by this sensor.\nIn [5] by Cardei et al, disjoint sensor sets are modeled as disjoint dominating sets.\nAlthough maximum dominating sets computation is NP-complete, the authors proposed a graphcoloring based algorithm.\nCardei et al also studied similar problem in the domain of covering target points in [4].\nThe NP-completeness of the problem is proved and a heuristic that computes the sets are proposed.\nThese algorithms are centralized solutions of sensorgrouping problem.\nHowever, global information (e.g., the location of each in-network sensor node) of a large scale WSN is also very expensive to obtained online.\nAlso it is usually infeasible to obtain such information before sensor nodes are deployed.\nFor example, sensor nodes are usually deployed in a random manner and the location of each in-network sensor node is determined only after a node is deployed.\nThe solution of sensor-grouping problem should only base on locally obtainable information of a sensor node.\nThat is to say, nodes should determine which group they should join in a fully distributed way.\nHere locally obtainable information refers to a node's local information and the information that can be directly obtained from its adjacent nodes, i.e., nodes within its communication range.\nIn Subsection 3.1, we provide a general problem formulation of the sensor-grouping problem.\nDistributed-solution requirement is formulated in this problem.\nIt is followed by discussion in Subsection 3.2 on a general sensing model, which serves as a given condition of the sensor-grouping problem formulation.\nTo facilitate our discussions, the notations in our following discussions are described as follows.\n• n: The number in-network sensor nodes.\n• S (j) (j = 1, 2,..., m): The jth set of sensor nodes where m is the number of sets.\n• L (i) (i = 1, 2,..., n): The physical location of node i. • 0: The area monitored by the network: i.e., the sensing area of the network.\n• R: The sensing radius of a sensor node.\nWe assume that a sensor node can only be responsible to monitor a circular area centered at the node with a radius equal to R.\nThis is a usual assumption in work that addresses sensor-coverage related problems.\nWe call this circular area the sensing area of a node.\n3.1 Problem Formulation\nWe assume that each sensor node can know its approximate physical location.\nThe approximate location information is obtainable if each sensor node carries a GPS receiver or if some localization algorithms are employed (e.g., [3]).\n2Note that if some nodes die, a re-grouping process might also be performed to exploit the remaining nodes in a set of sensor nodes.\nHow to provide this mechanism is beyond the scope of this paper and yet to be explored.\nProblem 1.\nGiven: • The set of each sensor node i's sensing neighbors N (i) and the location of each member in N (i); • A sensing model which quantitatively describes how a point P in area 0 is covered by sensor nodes that are responsible to monitor this point.\nWe call this quantity the coverage quality of P. • The coverage quality requirement in 0, denoted by s.\nWhen the coverage of a point is larger than this threshold, we say this point is covered.\nFor each sensor node i, make a decision on which group S (j) it shouldjoin so that: • Area 0 can be covered by sensor nodes in each set S (j) • m, the number of sets S (j) is maximized.\n■\nIn this formulation, we call sensor nodes within a circular area centered at a sensor node i with a radius equal to 2 · R the sensing neighbors of node i.\nThis is because sensors nodes in this area, together with node i, may be cooperative to ensure the coverage of a point inside node i's sensing area.\nWe assume that the communication range of a sensor node is larger than 2 · R, which is also a general assumption in work that addresses sensor-coverage related problems.\nThat is to say, the first given condition in Problem 1 is the information that can be obtained directly from a node's adjacent nodes.\nIt is therefore locally obtainable information.\nThe last two given conditions in this problem formulation can be programmed into a node before it is deployed or by a node-programming protocol (e.g., [9]) during network runtime.\nTherefore, the given conditions can all be easily obtained by a sensor-grouping scheme with fully distributed implementation.\nWe reify this problem with a realistic sensing model in next subsection.\n3.2 A General Sensing Model\nAs WSNs are usually employed to monitor possible events in a given area, it is therefore a design requirement that an event occurring in the network area must/may be successfully detected by sensors.\nThis issue is usually formulated as how to ensure that an event signal omitted in an arbitrary point in the network area can be detected by sensor nodes.\nObviously, a sensing model is required to address this problem so that how a point in the network area is covered can be modeled and quantified.\nThus the coverage quality of a WSN can be evaluated.\nDifferent applications of WSNs employ different types of sensors, which surely have widely different theoretical and physical characteristics.\nTherefore, to fulfill different application requirements, different sensing models should be constructed based on the characteristics of the sensors employed.\nA simple theoretical sensing model is the Boolean sensing model [15] [18] [17] [14].\nBoolean sensing model assumes that a sensor node can always detect an event occurring in its responsible sensing area.\nBut most sensors detect events according to the signal strength sensed.\nEvent signals usually fade in relation to the physical distance between an event and the sensor.\nThe larger the distance, the weaker the event signals that can be sensed by the sensor, which results in a reduction of the probability that the event can be successfully detected by the sensor.\nAs in WSNs, event signals are usually electromagnetic, acoustic, or photic signals, they fade exponentially with the increasing of\ntheir transmit distance.\nSpecifically, the signal strength E (d) of an event that is received by a sensor node satisfies:\nwhere d is the physical distance from the event to the sensor node; α is related to the signal strength omitted by the event; and β is signal fading factor which is typically a positive number larger than or equal to 2.\nUsually, α and β are considered as constants.\nBased on this notion, to be more reasonable, researchers propose collaborative sensing model to capture application requirements: Area coverage can be maintained by a set of collaborative sensor nodes: For a point with physical location L, the point is considered covered by the collaboration of i sensors (denoted by k1,..., ki) if and only if the following two equations holds [7] [10] [12].\nC (L) is regarded as the coverage quality of location L in the network area [7] [10] [12].\nHowever, we notice that defining the sensibility as the sum of the sensed signal strength by each collaborative sensor implies a very special application: Applications must employ the sum of the signal strength to achieve decision-making.\nTo capture generally realistic application requirement, we modify the definition described in Equation (5).\nThe model we adopt in this paper is described in details as follows.\nWe consider the probability P (L, kj) that an event located at L can be detected by sensor kj is related to the signal strength sensed by kj.\nFormally,\nwhere γ is a constant and δ = γα is a constant too.\n~ normalizes the distance to a proper scale and the \"+1\" item is to avoid infinite value of P (L, kj).\nThe probability that an event located at L can be detected by any collaborative sensors that satisfied Equation (4) is:\nAs the detection probability P (L) reasonably determines how an event occurring at location L can be detected by the networks, it is a good measure of the coverage quality of location L in a WSN.\nSpecifically, Equation (5) is modified to:\nTo sum it up, we consider a point at location L is covered if Equation (4) and (8) hold.\n4.\nMAXIMIZING-ι NODE-DEDUCTION ALGORITHM FOR SENSOR-GROUPING PROBLEM\nBefore we process to introduce algorithms to solve the sensor grouping problem, let us define the margin (denoted by θ) of an area φ monitored by the network as the band-like marginal area of φ and all the points on the outer perimeter of θ is ρ distance away from all the points on the inner perimeter of θ.\nρ is called the margin length.\nIn a practical network, sensor nodes are usually evenly deployed in the network area.\nObviously, the number of sensor nodes that can sense an event occurring in the margin of the network is smaller than the number of sensor nodes that can sense an event occurring in other area of the network.\nBased on this consideration, in our algorithm design, we ensure the coverage quality of the network area except the margin.\nThe information on φ and ρ is networkbased.\nEach in-network sensor node can be pre-programmed or on-line informed about φ and ρ, and thus calculate whether a point in its sensing area is in the margin or not.\n4.1 Maximizing-ι Node-Deduction Algorithm\nThe node-deduction process of our Maximizing-ι Node-Deduction Algorithm (MIND) is simple.\nA node i greedily maximizes ι of the sub-network composed by itself, its ungrouped sensing neighbors, and the neighbors that are in the same group of itself.\nUnder the constraint that the coverage quality of its sensing area should be ensured, node i deletes nodes in this sub-network one by one.\nThe candidate to be pruned satisfies that:\n• It is an ungrouped node.\n• The deletion of the node will not result in uncovered-points inside the sensing area of i.\nA candidate is deleted if the deletion of the candidate results in largest ι of the sub-network compared to the deletion of other candidates.\nThis node-deduction process continues until no candidate can be found.\nThen all the ungrouped sensing neighbors that are not deleted are grouped into the same group of node i.\nWe call the sensing neighbors that are in the same group of node i the group sensing neighbors of node i.\nWe then call node i a finished node, meaning that it has finished the above procedure and the sensing area of the node is covered.\nThose nodes that have not yet finished this procedure are called unfinished nodes.\nThe above procedure initiates at a random-selected node that is not in the margin.\nThe node is grouped to the first group.\nIt calculates the resulting group sensing neighbors of it based on the above procedure.\nIt informs these group sensing neighbors that they are selected in the group.\nThen it hands over the above procedure to an unfinished group sensing neighbors that is the farthest from itself.\nThis group sensing neighbor continues this procedure until no unfinished neighbor can be found.\nThen the first group is formed (Algorithmic description of this procedure can be found at [19]).\nAfter a group is formed, another random-selected ungrouped node begins to group itself to the second group and initiates the above procedure.\nIn this way, groups are formed one by one.\nWhen a node that involves in this algorithm found out that the coverage quality if its sensing area, except what overlaps the network margin, cannot be ensured even if all its ungrouped sensing neighbors are grouped into the same group as itself, the algorithm stops.\nMIND is based on locally obtainable information of sensor nodes.\nIt is a distributed algorithm that serves as an approximate solution of Problem 1.\n4.2 Incremental Coverage Quality Algorithm: A Benchmark for MIND\nTo evaluate the effectiveness of introducing ι in the sensor-group problem, another algorithm for sensor-group problem called Incremental Coverage Quality Algorithm (ICQA) is designed.\nOur aim\nis to evaluate how an idea, i.e., MIND, based on locally maximize t performs.\nIn ICQA, a node-selecting process is as follows.\nA node i greedily selects an ungrouped sensing neighbor in the same group as itself one by one, and informs the neighbor it is selected in the group.\nThe criterion is: • The selected neighbor is responsible to provide surveillance work for some uncovered parts of node i's sensing area.\n(i.e., the coverage quality requirement of the parts is not fulfilled if this neighbor is not selected.)\n• The selected neighbor results in highest improvement of the coverage quality of the neighbor's sensing area.\nThe improvement of the coverage quality, mathematically, should be the integral of the the improvements of all points inside the neighbor's sensing area.\nA numerical approximation is employed to calculate this improvement.\nDetails are presented in our simulation study.\nThis node-selecting process continues until the sensing area of node i is entirely covered.\nIn this way, node i's group sensing neighbors are found.\nThe above procedure is handed over as what MIND does and new groups are thus formed one by one.\nAnd the condition that ICQA stops is the same as MIND.\nICQA is also based on locally obtainable information of sensor nodes.\nICQA is also a distributed algorithm that serves as an approximate solution of Problem 1.\n5.\nSIMULATION RESULTS\nTo evaluate the effectiveness of employing t in sensor-grouping problem, we build simulation surveillance networks.\nWe employ MIND and ICQA to group the in-network sensor nodes.\nWe compare the grouping results with respect to how many groups both algorithms find and how the performance of the resulting groups are.\nDetailed settings of the simulation networks are shown in Table 1.\nIn simulation networks, sensor nodes are randomly deployed in a uniform manner in the network area.\nTable 1: The settings of the simulation networks\nFor evaluating the coverage quality of the sensing area of a node, we divide the sensing area of a node into several regions and regard the coverage quality of the central point in each region as a representative of the coverage quality of the region.\nThis is a numerical approximation.\nLarger number of such regions results in better approximation.\nAs sensor nodes are with low computational capacity, there is a tradeoff between the number of such regions and the precision of the resulting coverage quality of the sensing area of a node.\nIn our simulation study, we set this number 12.\nFor evaluating the improvement of coverage quality in ICQA, we sum up all the improvements at each region-center as the total improvement.\n5.1 Number of Groups Formed by MIND and ICQA\nWe set the total in-network node number to different values and let the networks perform MIND and ICQA.\nFor each n, simulations run with several random seeds to generate different networks.\nResults are averaged.\nFigure 2 shows the group numbers found in networks with different n's.\nFigure 2: The number of groups found by MIND and ICQA\nWe can see that MIND always outperforms ICQA in terms of the number of groups formed.\nObviously, the larger the number of groups can be formed, the more the redundancy of each group is exploited.\nThis output shows that an approach like MIND that aim to maximize t of the resulting topology can exploits redundancy well.\nAs an example, in case that n = 1500, the results of five networks are listed in Table 2.\nTable 2: The grouping results of five networks with n = 1500\nThe difference between the average t of the groups in each network shows that groups formed by MIND result in topologies with larger t's.\nIt demonstrates that t is good indicator of redundancy in different networks.\n5.2 The Performance of the Resulting Groups\nAlthough MIND forms more groups than ICQA does, which implies longer lifetime of the networks, another importance consideration is how these groups formed by MIND and ICQA perform.\nWe let 10000 events randomly occur in the network area except the margin.\nWe compare how many events happen at the locations where the quality is less than the requirement s = 0.6 when each resulting group is conducting surveillance work (We call the number of such events the failure number of group).\nFigure 3 shows the average failure numbers of the resulting groups when different node numbers are set.\nWe can see that the groups formed by MIND outperform those formed by ICQA because the groups formed by MIND result in lower failure numbers.\nThis further demonstrates that MIND is a good approach for sensor-grouping problem.\nFigure 3: The failure numbers of MIND and ICQA\n6.\nCONCLUSION\nThis paper proposes t, a novel index for evaluation of pointdistribution.\nt is the minimum distance between each pair of points normalized by the average distance between each pair of points.\nWe find that a set of points that achieve a maximum value of t result in a honeycomb structure.\nWe propose that t can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs).\nWe set out to validate this idea by employing t to a sensorgrouping problem.\nWe formulate a general sensor-grouping problem for WSNs and provide a general sensing model.\nWith an algorithm called Maximizing-t Node-Deduction (MIND), we show that maximizing t at sensor nodes is a good approach to solve this problem.\nSimulation results verify that MIND outperforms a greedy algorithm that exploits sensor-redundancy we design in terms of the number and the performance of the groups formed.\nThis demonstrates a good application of employing t in coverage-related problems."},"lvl-3":{"kind":"string","value":"A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks\nABSTRACT\nWe propose t, a novel index for evaluation of point-distribution.\nt is the minimum distance between each pair of points normalized by the average distance between each pair of points.\nWe find that a set of points that achieve a maximum value of t result in a honeycomb structure.\nWe propose that t can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs).\nTo validate this idea, we formulate a general sensorgrouping problem for WSNs and provide a general sensing model.\nWe show that locally maximizing t at sensor nodes is a good approach to solve this problem with an algorithm called Maximizingt Node-Deduction (MIND).\nSimulation results verify that MIND outperforms a greedy algorithm that exploits sensor-redundancy we design.\nThis demonstrates a good application of employing t in coverage-related problems for WSNs.\n1.\nINTRODUCTION\nA wireless sensor network (WSN) consists of a large number of in-situ battery-powered sensor nodes.\nA WSN can collect the data about physical phenomena of interest [1].\nThere are many potential applications of WSNs, including environmental monitoring and surveillance, etc. [1] [11].\nIn many application scenarios, WSNs are employed to conduct surveillance tasks in adverse, or even worse, in hostile working environments.\nOne major problem caused is that sensor nodes are subjected to failures.\nTherefore, fault tolerance of a WSN is critical.\nOne way to achieve fault tolerance is that a WSN should contain a large number of redundant nodes in order to tolerate node failures.\nIt is vital to provide a mechanism that redundant nodes can be working in sleeping mode (i.e., major power-consuming units such as the transceiver of a redundant sensor node can be shut off) to save energy, and thus to prolong the network lifetime.\nRedundancy should be exploited as much as possible for the set of sensors that are currently taking charge in the surveillance work of the network area [6].\nWe find that the minimum distance between each pair of points normalized by the average distance between each pair of points serves as a good index to evaluate the distribution of the points.\nWe call this index, denoted by t, the normalized minimum distance.\nIf points are moveable, we find that maximizing t results in a honeycomb structure.\nThe honeycomb structure poses that the coverage efficiency is the best if each point represents a sensor node that is providing surveillance work.\nEmploying t in coverage-related problems is thus deemed promising.\nThis enlightens us that maximizing t is a good approach to select a set of sensors that are currently taking charge in the surveillance work of the network area.\nTo explore the effectiveness of employing t in coverage-related problems, we formulate a sensorgrouping problem for high-redundancy WSNs.\nAn algorithm called Maximizing-t Node-Deduction (MIND) is proposed in which redundant sensor nodes are removed to obtain a large t for each set of sensors that are currently taking charge in the surveillance work of the network area.\nWe also introduce another greedy solution called Incremental Coverage Quality Algorithm (ICQA) for this problem, which serves as a benchmark to evaluate MIND.\nThe main contribution of this paper is twofold.\nFirst, we introduce a novel index t for evaluation of point-distribution.\nWe show that maximizing t of a WSN results in low redundancy of the network.\nSecond, we formulate a general sensor-grouping problem for WSNs and provide a general sensing model.\nWith the MIND algorithm we show that locally maximizing t among each sensor node and its neighbors is a good approach to solve this problem.\nThis demonstrates a good application of employing t in coveragerelated problems.\nThe rest of the paper is organized as follows.\nIn Section 2, we introduce our point-distribution index t.\nWe survey related work and formulate a sensor-grouping problem together with a general sensing model in Section 3.\nSection 4 investigates the application of t in this grouping problem.\nWe propose MIND for this problem\nand introduce ICQA as a benchmark.\nIn Section 5, we present our simulation results in which MIND and ICQA are compared.\nSection 6 provides conclusion remarks.\n2.\nTHE NORMALIZED MINIMUM DISTANCE ι: A POINT-DISTRIBUTION INDEX\n3.\nTHE SENSOR-GROUPING PROBLEM\n3.1 Problem Formulation\n3.2 A General Sensing Model\n4.\nMAXIMIZING-ι NODE-DEDUCTION ALGORITHM FOR SENSOR-GROUPING PROBLEM\n4.1 Maximizing-ι Node-Deduction Algorithm\n4.2 Incremental Coverage Quality Algorithm: A Benchmark for MIND\n5.\nSIMULATION RESULTS\n5.1 Number of Groups Formed by MIND and ICQA\n5.2 The Performance of the Resulting Groups\n6.\nCONCLUSION\nThis paper proposes t, a novel index for evaluation of pointdistribution.\nt is the minimum distance between each pair of points normalized by the average distance between each pair of points.\nWe find that a set of points that achieve a maximum value of t result in a honeycomb structure.\nWe propose that t can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs).\nWe set out to validate this idea by employing t to a sensorgrouping problem.\nWe formulate a general sensor-grouping problem for WSNs and provide a general sensing model.\nWith an algorithm called Maximizing-t Node-Deduction (MIND), we show that maximizing t at sensor nodes is a good approach to solve this problem.\nSimulation results verify that MIND outperforms a greedy algorithm that exploits sensor-redundancy we design in terms of the number and the performance of the groups formed.\nThis demonstrates a good application of employing t in coverage-related problems."},"lvl-4":{"kind":"string","value":"A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks\nABSTRACT\nWe propose t, a novel index for evaluation of point-distribution.\nt is the minimum distance between each pair of points normalized by the average distance between each pair of points.\nWe find that a set of points that achieve a maximum value of t result in a honeycomb structure.\nWe propose that t can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs).\nTo validate this idea, we formulate a general sensorgrouping problem for WSNs and provide a general sensing model.\nWe show that locally maximizing t at sensor nodes is a good approach to solve this problem with an algorithm called Maximizingt Node-Deduction (MIND).\nSimulation results verify that MIND outperforms a greedy algorithm that exploits sensor-redundancy we design.\nThis demonstrates a good application of employing t in coverage-related problems for WSNs.\n1.\nINTRODUCTION\nA wireless sensor network (WSN) consists of a large number of in-situ battery-powered sensor nodes.\nIn many application scenarios, WSNs are employed to conduct surveillance tasks in adverse, or even worse, in hostile working environments.\nOne major problem caused is that sensor nodes are subjected to failures.\nRedundancy should be exploited as much as possible for the set of sensors that are currently taking charge in the surveillance work of the network area [6].\nWe find that the minimum distance between each pair of points normalized by the average distance between each pair of points serves as a good index to evaluate the distribution of the points.\nWe call this index, denoted by t, the normalized minimum distance.\nIf points are moveable, we find that maximizing t results in a honeycomb structure.\nThe honeycomb structure poses that the coverage efficiency is the best if each point represents a sensor node that is providing surveillance work.\nEmploying t in coverage-related problems is thus deemed promising.\nThis enlightens us that maximizing t is a good approach to select a set of sensors that are currently taking charge in the surveillance work of the network area.\nTo explore the effectiveness of employing t in coverage-related problems, we formulate a sensorgrouping problem for high-redundancy WSNs.\nWe also introduce another greedy solution called Incremental Coverage Quality Algorithm (ICQA) for this problem, which serves as a benchmark to evaluate MIND.\nThe main contribution of this paper is twofold.\nFirst, we introduce a novel index t for evaluation of point-distribution.\nWe show that maximizing t of a WSN results in low redundancy of the network.\nSecond, we formulate a general sensor-grouping problem for WSNs and provide a general sensing model.\nWith the MIND algorithm we show that locally maximizing t among each sensor node and its neighbors is a good approach to solve this problem.\nThis demonstrates a good application of employing t in coveragerelated problems.\nThe rest of the paper is organized as follows.\nIn Section 2, we introduce our point-distribution index t.\nWe survey related work and formulate a sensor-grouping problem together with a general sensing model in Section 3.\nSection 4 investigates the application of t in this grouping problem.\nWe propose MIND for this problem\nand introduce ICQA as a benchmark.\nIn Section 5, we present our simulation results in which MIND and ICQA are compared.\nSection 6 provides conclusion remarks.\n6.\nCONCLUSION\nThis paper proposes t, a novel index for evaluation of pointdistribution.\nt is the minimum distance between each pair of points normalized by the average distance between each pair of points.\nWe find that a set of points that achieve a maximum value of t result in a honeycomb structure.\nWe propose that t can serve as a good index to evaluate the distribution of the points, which can be employed in coverage-related problems in wireless sensor networks (WSNs).\nWe set out to validate this idea by employing t to a sensorgrouping problem.\nWe formulate a general sensor-grouping problem for WSNs and provide a general sensing model.\nWith an algorithm called Maximizing-t Node-Deduction (MIND), we show that maximizing t at sensor nodes is a good approach to solve this problem.\nThis demonstrates a good application of employing t in coverage-related problems."}}},{"rowIdx":98,"cells":{"id":{"kind":"string","value":"J-69"},"title":{"kind":"string","value":"Robust Incentive Techniques for Peer-to-Peer Networks"},"abstract":{"kind":"string","value":"Lack of cooperation (free riding) is one of the key problems that confronts today's P2P systems. What makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion, zero-cost identities, and traitors. To tackle these challenges we model the P2P system using the Generalized Prisoner's Dilemma (GPD), and propose the Reciprocative decision function as the basis of a family of incentives techniques. These techniques are fully distributed and include: discriminating server selection, maxflow-based subjective reputation, and adaptive stranger policies. Through simulation, we show that these techniques can drive a system of strategic users to nearly optimal levels of cooperation."},"keyphrases":{"kind":"list like","value":["incent","p2p system","collus","reciproc decis function","reput","adapt stranger polici","selfinterest user","incent for cooper","game-theoret approach","maxflow-base algorithm","reciproc peer","mutual cooper","asymmetr payoff","generos","paramet nomin valu","whitewash","stranger adapt","stranger defect","peer-to-peer","free-ride","cheap pseudonym","prison dilemma"],"string":"[\n \"incent\",\n \"p2p system\",\n \"collus\",\n \"reciproc decis function\",\n \"reput\",\n \"adapt stranger polici\",\n \"selfinterest user\",\n \"incent for cooper\",\n \"game-theoret approach\",\n \"maxflow-base algorithm\",\n \"reciproc peer\",\n \"mutual cooper\",\n \"asymmetr payoff\",\n \"generos\",\n \"paramet nomin valu\",\n \"whitewash\",\n \"stranger adapt\",\n \"stranger defect\",\n \"peer-to-peer\",\n \"free-ride\",\n \"cheap pseudonym\",\n \"prison dilemma\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","P","P","P","M","R","U","U","M","M","U","U","U","U","R","M","U","U","U","R"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"M\",\n \"R\",\n \"U\",\n \"U\",\n \"M\",\n \"M\",\n \"U\",\n \"U\",\n \"U\",\n \"U\",\n \"R\",\n \"M\",\n \"U\",\n \"U\",\n \"U\",\n \"R\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"Robust Incentive Techniques for Peer-to-Peer Networks Michal Feldman1 mfeldman@sims.berkeley.edu Kevin Lai2 klai@hp.com Ion Stoica3 istoica@cs.berkeley.edu John Chuang1 chuang@sims.berkeley.edu 1 School of Information Management and Systems U.C. Berkeley 2 HP Labs 3 Computer Science Division U.C. Berkeley ABSTRACT Lack of cooperation (free riding) is one of the key problems that confronts today``s P2P systems.\nWhat makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion, zero-cost identities, and traitors.\nTo tackle these challenges we model the P2P system using the Generalized Prisoner``s Dilemma (GPD), and propose the Reciprocative decision function as the basis of a family of incentives techniques.\nThese techniques are fully distributed and include: discriminating server selection, maxflowbased subjective reputation, and adaptive stranger policies.\nThrough simulation, we show that these techniques can drive a system of strategic users to nearly optimal levels of cooperation.\nCategories and Subject Descriptors C.2.4 [Computer-Communication Networks]: Distributed Systems; J.4 [Social And Behavioral Sciences]: Economics General Terms Design, Economics 1.\nINTRODUCTION Many peer-to-peer (P2P) systems rely on cooperation among selfinterested users.\nFor example, in a file-sharing system, overall download latency and failure rate increase when users do not share their resources [3].\nIn a wireless ad-hoc network, overall packet latency and loss rate increase when nodes refuse to forward packets on behalf of others [26].\nFurther examples are file preservation [25], discussion boards [17], online auctions [16], and overlay routing [6].\nIn many of these systems, users have natural disincentives to cooperate because cooperation consumes their own resources and may degrade their own performance.\nAs a result, each user``s attempt to maximize her own utility effectively lowers the overall A BC Figure 1: Example of asymmetry of interest.\nA wants service from B, B wants service form C, and C wants service from A. utility of the system.\nAvoiding this tragedy of the commons [18] requires incentives for cooperation.\nWe adopt a game-theoretic approach in addressing this problem.\nIn particular, we use a prisoners'' dilemma model to capture the essential tension between individual and social utility, asymmetric payoff matrices to allow asymmetric transactions between peers, and a learning-based [14] population dynamic model to specify the behavior of individual peers, which can be changed continuously.\nWhile social dilemmas have been studied extensively, P2P applications impose a unique set of challenges, including: • Large populations and high turnover: A file sharing system such as Gnutella and KaZaa can exceed 100, 000 simultaneous users, and nodes can have an average life-time of the order of minutes [33].\n• Asymmetry of interest: Asymmetric transactions of P2P systems create the possibility for asymmetry of interest.\nIn the example in Figure 1, A wants service from B, B wants service from C, and C wants service from A. • Zero-cost identity: Many P2P systems allow peers to continuously switch identities (i.e., whitewash).\nStrategies that work well in traditional prisoners'' dilemma games such as Tit-for-Tat [4] will not fare well in the P2P context.\nTherefore, we propose a family of scalable and robust incentive techniques, based upon a novel Reciprocative decision function, to address these challenges and provide different tradeoffs: • Discriminating Server Selection: Cooperation requires familiarity between entities either directly or indirectly.\nHowever, the large populations and high turnover of P2P systems makes it less likely that repeat interactions will occur with a familiar entity.\nWe show that by having each peer keep a 102 private history of the actions of other peers toward her, and using discriminating server selection, the Reciprocative decision function can scale to large populations and moderate levels of turnover.\n• Shared History: Scaling to higher turnover and mitigating asymmetry of interest requires shared history.\nConsider the example in Figure 1.\nIf everyone provides service, then the system operates optimally.\nHowever, if everyone keeps only private history, no one will provide service because B does not know that A has served C, etc..\nWe show that with shared history, B knows that A served C and consequently will serve A.\nThis results in a higher level of cooperation than with private history.\nThe cost of shared history is a distributed infrastructure (e.g., distributed hash table-based storage) to store the history.\n• Maxflow-based Subjective Reputation: Shared history creates the possibility for collusion.\nIn the example in Figure 1, C can falsely claim that A served him, thus deceiving B into providing service.\nWe show that a maxflow-based algorithm that computes reputation subjectively promotes cooperation despite collusion among 1/3 of the population.\nThe basic idea is that B would only believe C if C had already provided service to B.\nThe cost of the maxflow algorithm is its O(V 3 ) running time, where V is the number of nodes in the system.\nTo eliminate this cost, we have developed a constant mean running time variation, which trades effectiveness for complexity of computation.\nWe show that the maxflow-based algorithm scales better than private history in the presence of colluders without the centralized trust required in previous work [9] [20].\n• Adaptive Stranger Policy: Zero-cost identities allows noncooperating peers to escape the consequences of not cooperating and eventually destroy cooperation in the system if not stopped.\nWe show that if Reciprocative peers treat strangers (peers with no history) using a policy that adapts to the behavior of previous strangers, peers have little incentive to whitewash and whitewashing can be nearly eliminated from the system.\nThe adaptive stranger policy does this without requiring centralized allocation of identities, an entry fee for newcomers, or rate-limiting [13] [9] [25].\n• Short-term History: History also creates the possibility that a previously well-behaved peer with a good reputation will turn traitor and use his good reputation to exploit other peers.\nThe peer could be making a strategic decision or someone may have hijacked her identity (e.g., by compromising her host).\nLong-term history exacerbates this problem by allowing peers with many previous transactions to exploit that history for many new transactions.\nWe show that short-term history prevents traitors from disrupting cooperation.\nThe rest of the paper is organized as follows.\nWe describe the model in Section 2 and the reciprocative decision function in Section 3.\nWe then proceed to the incentive techniques in Section 4.\nIn Section 4.1, we describe the challenges of large populations and high turnover and show the effectiveness of discriminating server selection and shared history.\nIn Section 4.2, we describe collusion and demonstrate how subjective reputation mitigates it.\nIn Section 4.3, we present the problem of zero-cost identities and show how an adaptive stranger policy promotes persistent identities.\nIn Section 4.4, we show how traitors disrupt cooperation and how short-term history deals with them.\nWe discuss related work in Section 5 and conclude in Section 6.\n2.\nMODEL AND ASSUMPTIONS In this section, we present our assumptions about P2P systems and their users, and introduce a model that aims to capture the behavior of users in a P2P system.\n2.1 Assumptions We assume a P2P system in which users are strategic, i.e., they act rationally to maximize their benefit.\nHowever, to capture some of the real-life unpredictability in the behavior of users, we allow users to randomly change their behavior with a low probability (see Section 2.4).\nFor simplicity, we assume a homogeneous system in which all peers issue and satisfy requests at the same rate.\nA peer can satisfy any request, and, unless otherwise specified, peers request service uniformly at random from the population.1 .\nFinally, we assume that all transactions incur the same cost to all servers and provide the same benefit to all clients.\nWe assume that users can pollute shared history with false recommendations (Section 4.2), switch identities at zero-cost (Section 4.3), and spoof other users (Section 4.4).\nWe do not assume any centralized trust or centralized infrastructure.\n2.2 Model To aid the development and study of the incentive schemes, in this section we present a model of the users'' behaviors.\nIn particular, we model the benefits and costs of P2P interactions (the game) and population dynamics caused by mutation, learning, and turnover.\nOur model is designed to have the following properties that characterize a large set of P2P systems: • Social Dilemma: Universal cooperation should result in optimal overall utility, but individuals who exploit the cooperation of others while not cooperating themselves (i.e., defecting) should benefit more than users who do cooperate.\n• Asymmetric Transactions: A peer may want service from another peer while not currently being able to provide the service that the second peer wants.\nTransactions should be able to have asymmetric payoffs.\n• Untraceable Defections: A peer should not be able to determine the identity of peers who have defected on her.\nThis models the difficulty or expense of determining that a peer could have provided a service, but didn``t. For example, in the Gnutella file sharing system [21], a peer may simply ignore queries despite possessing the desired file, thus preventing the querying peer from identifying the defecting peer.\n• Dynamic Population: Peers should be able to change their behavior and enter or leave the system independently and continuously.\n1The exception is discussed in Section 4.1.1 103 Cooperate Defect Cooperate DefectClient Server sc RR / sc ST / sc PP / sc TS / Figure 2: Payoff matrix for the Generalized Prisoner``s Dilemma.\nT, R, P, and S stand for temptation, reward, punishment and sucker, respectively.\n2.3 Generalized Prisoner``s Dilemma The Prisoner``s Dilemma, developed by Flood, Dresher, and Tucker in 1950 [22] is a non-cooperative repeated game satisfying the social dilemma requirement.\nEach game consists of two players who can defect or cooperate.\nDepending how each acts, the players receive a payoff.\nThe players use a strategy to decide how to act.\nUnfortunately, existing work either uses a specific asymmetric payoff matrix or only gives the general form for a symmetric one [4].\nInstead, we use the Generalized Prisoner``s Dilemma (GPD), which specifies the general form for an asymmetric payoff matrix that preserves the social dilemma.\nIn the GPD, one player is the client and one player is the server in each game, and it is only the decision of the server that is meaningful for determining the outome of the transaction.\nA player can be a client in one game and a server in another.\nThe client and server receive the payoff from a generalized payoff matrix (Figure 2).\nRc, Sc, Tc, and Pc are the client``s payoff and Rs, Ss, Ts, and Ps are the server``s payoff.\nA GPD payoff matrix must have the following properties to create a social dilemma: 1.\nMutual cooperation leads to higher payoffs than mutual defection (Rs + Rc > Ps + Pc).\n2.\nMutual cooperation leads to higher payoffs than one player suckering the other (Rs + Rc > Sc + Ts and Rs + Rc > Ss + Tc).\n3.\nDefection dominates cooperation (at least weakly) at the individual level for the entity who decides whether to cooperate or defect: (Ts ≥ Rs and Ps ≥ Ss and (Ts > Rs or Ps > Ss)) The last set of inequalities assume that clients do not incur a cost regardless of whether they cooperate or defect, and therefore clients always cooperate.\nThese properties correspond to similar properties of the classic Prisoner``s Dilemma and allow any form of asymmetric transaction while still creating a social dilemma.\nFurthermore, one or more of the four possible actions (client cooperate and defect, and server cooperate and defect) can be untraceable.\nIf one player makes an untraceable action, the other player does not know the identity of the first player.\nFor example, to model a P2P application like file sharing or overlay routing, we use the specific payoff matrix values shown in Figure 3.\nThis satisfies the inequalities specified above, where only the server can choose between cooperating and defecting.\nIn addition, for this particular payoff matrix, clients are unable to trace server defections.\nThis is the payoff matrix that we use in our simulation results.\nRequest Service Don't Request 7 / -1 0 / 0 0 / 0 0 / 0 Provide Service Ignore Request Client Server Figure 3: The payoff matrix for an application like P2P file sharing or overlay routing.\n2.4 Population Dynamics A characteristic of P2P systems is that peers change their behavior and enter or leave the system independently and continuously.\nSeveral studies [4] [28] of repeated Prisoner``s Dilemma games use an evolutionary model [19] [34] of population dynamics.\nAn evolutionary model is not suitable for P2P systems because it only specifies the global behavior and all changes occur at discrete times.\nFor example, it may specify that a population of 5 100% Cooperate players and 5 100% Defect players evolves into a population with 3 and 7 players, respectively.\nIt does not specify which specific players switched.\nFurthermore, all the switching occurs at the end of a generation instead of continuously, like in a real P2P system.\nAs a result, evolutionary population dynamics do not accurately model turnover, traitors, and strangers.\nIn our model, entities take independent and continuous actions that change the composition of the population.\nTime consists of rounds.\nIn each round, every player plays one game as a client and one game as a server.\nAt the end of a round, a player may: 1) mutate 2) learn, 3) turnover, or 4) stay the same.\nIf a player mutates, she switches to a randomly picked strategy.\nIf she learns, she switches to a strategy that she believes will produce a higher score (described in more detail below).\nIf she maintains her identity after switching strategies, then she is referred to as a traitor.\nIf a player suffers turnover, she leaves the system and is replaced with a newcomer who uses the same strategy as the exiting player.\nTo learn, a player collects local information about the performance of different strategies.\nThis information consists of both her personal observations of strategy performance and the observations of those players she interacts with.\nThis models users communicating out-of-band about how strategies perform.\nLet s be the running average of the performance of a player``s current strategy per round and age be the number of rounds she has been using the strategy.\nA strategy``s rating is RunningAverage(s ∗ age) RunningAverage(age) .\nWe use the age and compute the running average before the ratio to prevent young samples (which are more likely to be outliers) from skewing the rating.\nAt the end of a round, a player switches to highest rated strategy with a probability proportional to the difference in score between her current strategy and the highest rated strategy.\n104 3.\nRECIPROCATIVE DECISION FUNCTION In this section, we present the new decision function, Reciprocative, that is the basis for our incentive techniques.\nA decision function maps from a history of a player``s actions to a decision whether to cooperate with or defect on that player.\nA strategy consists of a decision function, private or shared history, a server selection mechanism, and a stranger policy.\nOur approach to incentives is to design strategies which maximize both individual and social benefit.\nStrategic users will choose to use such strategies and thereby drive the system to high levels of cooperation.\nTwo examples of simple decision functions are 100% Cooperate and 100% Defect.\n100% Cooperate models a naive user who does not yet realize that she is being exploited.\n100% Defect models a greedy user who is intent on exploiting the system.\nIn the absence of incentive techniques, 100% Defect users will quickly dominate the 100% Cooperate users and destroy cooperation in the system.\nOur requirements for a decision function are that (1) it can use shared and subjective history, (2) it can deal with untraceable defections, and (3) it is robust against different patterns of defection.\nPrevious decision functions such as Tit-for-Tat[4] and Image[28] (see Section 5) do not satisfy these criteria.\nFor example, Tit-for-Tat and Image base their decisions on both cooperations and defections, therefore cannot deal with untraceable defections .\nIn this section and the remaining sections we demonstrate how the Reciprocativebased strategies satisfy all of the requirements stated above.\nThe probability that a Reciprocative player cooperates with a peer is a function of its normalized generosity.\nGenerosity measures the benefit an entity has provided relative to the benefit it has consumed.\nThis is important because entities which consume more services than they provide, even if they provide many services, will cause cooperation to collapse.\nFor some entity i, let pi and ci be the services i has provided and consumed, respectively.\nEntity i``s generosity is simply the ratio of the service it provides to the service it consumes: g(i) = pi/ci.\n(1) One possibility is to cooperate with a probability equal to the generosity.\nAlthough this is effective in some cases, in other cases, a Reciprocative player may consume more than she provides (e.g., when initially using the Stranger Defect policy in 4.3).\nThis will cause Reciprocative players to defect on each other.\nTo prevent this situation, a Reciprocative player uses its own generosity as a measuring stick to judge its peer``s generosity.\nNormalized generosity measures entity i``s generosity relative to entity j``s generosity.\nMore concretely, entity i``s normalized generosity as perceived by entity j is gj(i) = g(i)/g(j).\n(2) In the remainder of this section, we describe our simulation framework, and use it to demonstrate the benefits of the baseline Reciprocative decision function.\nParameter Nominal value Section Population Size 100 2.4 Run Time 1000 rounds 2.4 Payoff Matrix File Sharing 2.3 Ratio using 100% Cooperate 1/3 3 Ratio using 100% Defect 1/3 3 Ratio using Reciprocative 1/3 3 Mutation Probability 0.0 2.4 Learning Probability 0.05 2.4 Turnover Probability 0.0001 2.4 Hit Rate 1.0 4.1.1 Table 1: Default simulation parameters.\n3.1 Simulation Framework Our simulator implements the model described in Section 2.\nWe use the asymmetric file sharing payoff matrix (Figure 3) with untraceable defections because it models transactions in many P2P systems like file-sharing and packet forwarding in ad-hoc and overlay networks.\nOur simulation study is composed of different scenarios reflecting the challenges of various non-cooperative behaviors.\nTable 1 presents the nominal parameter values used in our simulation.\nThe Ratio using rows refer to the initial ratio of the total population using a particular strategy.\nIn each scenario we vary the value range of a specific parameter to reflect a particular situation or attack.\nWe then vary the exact properties of the Reciprocative strategy to defend against that situation or attack.\n3.2 Baseline Results 0 20 40 60 80 100 120 0 200 400 600 800 1000 Population Time (a) Total Population: 60 0 20 40 60 80 100 120 0 200 400 600 800 1000 Time (b) Total Population: 120 Defector Cooperator Recip.\nPrivate Figure 4: The evolution of strategy populations over time.\nTime the number of elapsed rounds.\nPopulation is the number of players using a strategy.\nIn this section, we present the dynamics of the game for the basic scenario presented in Table 1 to familiarize the reader and set a baseline for more complicated scenarios.\nFigures 4(a) (60 players) and (b) (120 players) show players switching to higher scoring strategies over time in two separate runs of the simulator.\nEach point in the graph represents the number of players using a particular strategy at one point in time.\nFigures 5(a) and (b) show the corresponding mean overall score per round.\nThis measures the degree of cooperation in the system: 6 is the maximum possible (achieved when everybody cooperates) and 0 is the minimum (achieved when everybody defects).\nFrom the file sharing payoff matrix, a net of 6 means everyone is able to download a file and a 0 means that no one 105 0 1 2 3 4 5 6 0 200 400 600 800 1000 MeanOverallScore/Round Time (a) Total Population: 60 0 1 2 3 4 5 6 0 200 400 600 800 1000 Time (b) Total Population: 120 Figure 5: The mean overall per round score over time.\nis able to do so.\nWe use this metric in all later results to evaluate our incentive techniques.\nFigure 5(a) shows that the Reciprocative strategy using private history causes a system of 60 players to converge to a cooperation level of 3.7, but drops to 0.5 for 120 players.\nOne would expect the 60 player system to reach the optimal level of cooperation (6) because all the defectors are eliminated from the system.\nIt does not because of asymmetry of interest.\nFor example, suppose player B is using Reciprocative with private history.\nPlayer A may happen to ask for service from player B twice in succession without providing service to player B in the interim.\nPlayer B does not know of the service player A has provided to others, so player B will reject service to player A, even though player A is cooperative.\nWe discuss solutions to asymmetry of interest and the failure of Reciprocative in the 120 player system in Section 4.1.\n4.\nRECIPROCATIVE-BASED INCENTIVE TECHNIQUES In this section we present our incentives techniques and evaluate their behavior by simulation.\nTo make the exposition clear we group our techniques by the challenges they address: large populations and high turnover (Section 4.1), collusions (Section 4.2), zero-cost identities (Section 4.3), and traitors (Section 4.4).\n4.1 Large Populations and High Turnover The large populations and high turnover of P2P systems makes it less likely that repeat interactions will occur with a familiar entity.\nUnder these conditions, basing decisions only on private history (records about interactions the peer has been directly involved in) is not effective.\nIn addition, private history does not deal well with asymmetry of interest.\nFor example, if player B has cooperated with others but not with player A himself in the past, player A has no indication of player B``s generosity, thus may unduly defect on him.\nWe propose two mechanisms to alleviate the problem of few repeat transactions: server-selection and shared history.\n4.1.1 Server Selection A natural way to increase the probability of interacting with familiar peers is by discriminating server selection.\nHowever, the asymmetry of transactions challenges selection mechanisms.\nUnlike in the prisoner``s dilemma payoff matrix, where players can benefit one another within a single transaction, transactions in GPD are asymmetric.\nAs a result, a player who selects her donor for the second time without contributing to her in the interim may face a defection.\nIn addition, due to untraceability of defections, it is impossible to maintain blacklists to avoid interactions with known defectors.\nIn order to deal with asymmetric transactions, every player holds (fixed size) lists of both past donors and past recipients, and selects a server from one of these lists at random with equal probabilities.\nThis way, users approach their past recipients and give them a chance to reciprocate.\nIn scenarios with selective users we omit the complete availability assumption to prevent players from being clustered into a lot of very small groups; thus, we assume that every player can perform the requested service with probability p (for the results presented in this section, p = .3).\nIn addition, in order to avoid bias in favor of the selective players, all players (including the non-discriminative ones) select servers for games.\nFigure 6 demonstrates the effectiveness of the proposed selection mechanism in scenarios with large population sizes.\nWe fix the initial ratio of Reciprocative in the population (33%) while varying the population size (between 24 to 1000) (Notice that while in Figures 4(a) and (b), the data points demonstrates the evolution of the system over time, each data point in this figure is the result of an entire simulation for a specific scenario).\nThe figure shows that the Reciprocative decision function using private history in conjunction with selective behavior can scale to large populations.\nIn Figure 7 we fix the population size and vary the turnover rate.\nIt demonstrates that while selective behavior is effective for low turnover rates, as turnover gets higher, selective behavior does not scale.\nThis occurs because selection is only effective as long as players from the past stay alive for long enough such that they can be selected for future games.\n4.1.2 Shared history In order to mitigate asymmetry of interest and scale to higher turnover rate, there is a need in shared history.\nShared history means that every peer keeps records about all of the interactions that occur in the system, regardless of whether he was directly involved in them or not.\nIt allows players to leverage off of the experiences of others in cases of few repeat transactions.\nIt only requires that someone has interacted with a particular player for the entire population to observe it, thus scales better to large populations and high turnovers, and also tolerates asymmetry of interest.\nSome examples of shared history schemes are [20] [23] [28].\nFigure 7 shows the effectiveness of shared history under high turnover rates.\nIn this figure, we fix the population size and vary the turnover rate.\nWhile selective players with private history can only tolerate a moderate turnover, shared history scales to turnovers of up to approximately 0.1.\nThis means that 10% of the players leave the system at the end of each round.\nIn Figure 6 we fix the turnover and vary the population size.\nIt shows that shared history causes the system to converge to optimal cooperation and performance, regardless of the size of the population.\nThese results show that shared history addresses all three challenges of large populations, high turnover, and asymmetry of transactions.\nNevertheless, shared history has two disadvantages.\nFirst, 106 0 1 2 3 4 5 6 0 50 100 150 200 250 300 350 400 MeanOverallScore/Round NumPlayers Shared Non-Sel Private Non-Sel Private Selective Figure 6: Private vs. Shared History as a function of population size.\n0 1 2 3 4 5 6 0.0001 0.001 0.01 0.1 MeanOverallScore/Round Turnover Shared Non-Sel Private Non-Sel Private Selective Figure 7: Performance of selection mechanism under turnover.\nThe x-axis is the turnover rate.\nThe y-axis is the mean overall per round score.\nwhile a decentralized implementation of private history is straightforward, implementation of shared-history requires communication overhead or centralization.\nA decentralized shared history can be implemented, for example, on top of a DHT, using a peer-to-peer storage system [36] or by disseminating information to other entities in a similar way to routing protocols.\nSecond, and more fundamental, shared history is vulnerable to collusion.\nIn the next section we propose a mechanism that addresses this problem.\n4.2 Collusion and Other Shared History Attacks 4.2.1 Collusion While shared history is scalable, it is vulnerable to collusion.\nCollusion can be either positive (e.g. defecting entities claim that other defecting entities cooperated with them) or negative (e.g. entities claim that other cooperative entities defected on them).\nCollusion subverts any strategy in which everyone in the system agrees on the reputation of a player (objective reputation).\nAn example of objective reputation is to use the Reciprocative decision function with shared history to count the total number of cooperations a player has given to and received from all entities in the system; another example is the Image strategy [28].\nThe effect of collusion is magnified in systems with zero-cost identities, where users can create fake identities that report false statements.\nInstead, to deal with collusion, entities can compute reputation subjectively, where player A weighs player B``s opinions based on how much player A trusts player B.\nOur subjective algorithm is based on maxflow [24] [32].\nMaxflow is a graph theoretic problem, which given a directed graph with weighted edges asks what is the greatest rate at which material can be shipped from the source to the target without violating any capacity constraints.\nFor example, in figure 8 each edge is labeled with the amount of traffic that can travel on it.\nThe maxflow algorithm computes the maximum amount of traffic that can go from the source (s) to the target (t) without violating the constraints.\nIn this example, even though there is a loop of high capacity edges, the maxflow between the source and the target is only 2 (the numbers in brackets represent the actual flow on each edge in the solution).\n100(0) 1(1) 5(1) s t 10(1) 100(1) 1(1) 100(1) 20(0) Figure 8: Each edge in the graph is labeled with its capacity and the actual flow it carries in brackets.\nThe maxflow between the source and the target in the graph is 2.\nC C CCCC 100100100100 100 00 0 0 20 20 0 0 A B Figure 9: This graph illustrates the robustness of maxflow in the presence of colluders who report bogus high reputation values.\nWe apply the maxflow algorithm by constructing a graph whose vertices are entities and the edges are the services that entities have received from each other.\nThis information can be stored using the same methods as the shared history.\nA maxflow is the greatest level of reputation the source can give to the sink without violating reputation capacity constraints.\nAs a result, nodes who dishonestly report high reputation values will not be able to subvert the reputation system.\nFigure 9 illustrates a scenario in which all the colluders (labeled with C) report high reputation values for each other.\nWhen node A computes the subjective reputation of B using the maxflow algorithm, it will not be affected by the local false reputation values, rather the maxflow in this case will be 0.\nThis is because no service has been received from any of the colluders.\n107 In our algorithm, the benefit that entity i has received (indirectly) from entity j is the maxflow from j to i. Conversely, the benefit that entity i has provided indirectly to j is the maxflow from i to j.\nThe subjective reputation of entity j as perceived by i is: min maxflow(j to i) maxflow(i to j) , 1 (3) 0 1 2 3 4 5 6 0 100 200 300 400 500 600 700 800 900 1000 MeanOverallScore/Round Population Shared Private Subjective Figure 10: Subjective shared history compared to objective shared history and private history in the presence of colluders.\nAlgorithm 1 CONSTANTTIMEMAXFLOW Bound the mean running time of Maxflow to a constant.\nmethod CTMaxflow(self, src, dst) 1: self.surplus ← self.surplus + self.increment {Use the running mean as a prediction.}\n2: if random() > (0.5∗self.surplus/self.mean iterations) then 3: return None {Not enough surplus to run.}\n4: end if {Get the flow and number of iterations used from the maxflow alg.}\n5: flow, iterations ← Maxflow(self.G, src, dst) 6: self.surplus ← self.surplus − iterations {Keep a running mean of the number of iterations used.}\n7: self.mean iterations ← self.α ∗ self.mean iterations + (1 − self.α) ∗ iterations 8: return flow The cost of maxflow is its long running time.\nThe standard preflowpush maxflow algorithm has a worst case running time of O(V 3 ).\nInstead, we use Algorithm 1 which has a constant mean running time, but sometimes returns no flow even though one exists.\nThe essential idea is to bound the mean number of nodes examined during the maxflow computation.\nThis bounds the overhead, but also bounds the effectiveness.\nDespite this, the results below show that a maxflow-based Reciprocative decision function scales to higher populations than one using private history.\nFigure 10 compares the effectiveness of subjective reputation to objective reputation in the presence of colluders.\nIn these scenarios, defectors collude by claiming that other colluders that they encounter gave them 100 cooperations for that encounter.\nAlso, the parameters for Algorithm 1 are set as follows: increment = 100, α = 0.9.\nAs in previous sections, Reciprocative with private history results in cooperation up to a point, beyond which it fails.\nThe difference here is that objective shared history fails for all population sizes.\nThis is because the Reciprocative players cooperate with the colluders because of their high reputations.\nHowever, subjective history can reach high levels of cooperation regardless of colluders.\nThis is because there are no high weight paths in the cooperation graph from colluders to any non-colluders, so the maxflow from a colluder to any non-colluder is 0.\nTherefore, a subjective Reciprocative player will conclude that that colluder has not provided any service to her and will reject service to the colluder.\nThus, the maxflow algorithm enables Reciprocative to maintain the scalability of shared history without being vulnerable to collusion or requiring centralized trust (e.g., trusted peers).\nSince we bound the running time of the maxflow algorithm, cooperation decreases as the population size increases, but the key point is that the subjective Reciprocative decision function scales to higher populations than one using private history.\nThis advantage only increases over time as CPU power increases and more cycles can be devoted to running the maxflow algorithm (by increasing the increment parameter).\nDespite the robustness of the maxflow algorithm to the simple form of collusion described previously, it still has vulnerabilities to more sophisticated attacks.\nOne is for an entity (the mole) to provide service and then lie positively about other colluders.\nThe other colluders can then exploit their reputation to receive service.\nHowever, the effectiveness of this attack relies on the amount of service that the mole provides.\nSince the mole is paying all of the cost of providing service and receiving none of the benefit, she has a strong incentive to stop colluding and try another strategy.\nThis forces the colluders to use mechanisms to maintain cooperation within their group, which may drive the cost of collusion to exceed the benefit.\n4.2.2 False reports Another attack is for a defector to lie about receiving or providing service to another entity.\nThere are four possibile actions that can be lied about: providing service, not providing service, receiving service, and not receiving service.\nFalsely claiming to receive service is the simple collusion attack described above.\nFalsely claiming not to have provided service provides no benefit to the attacker.\nFalsely claiming to have provided service or not to have received it allows an attacker to boost her own reputation and/or lower the reputation of another entity.\nAn entity may want to lower another entity``s reputation in order to discourage others from selecting it and exclusively use its service.\nThese false claims are clearly identifiable in the shared history as inconsistencies where one entity claims a transaction occurred and another claims it did not.\nTo limit this attack, we modify the maxflow algorithm so that an entity always believes the entity that is closer to him in the flow graph.\nIf both entities are equally distant, then the disputed edge in the flow is not critical to the evaluation and is ignored.\nThis modification prevents those cases where the attacker is making false claims about an entity that is closer than her to the evaluating entity, which prevents her from boosting her own reputation.\nThe remaining possibilities are for the attacker to falsely claim to have provided service to or not to have received it from a victim entity that is farther from the evalulator than her.\nIn these cases, an attacker can only lower the reputation of the victim.\nThe effectiveness of doing this is limited by the number of services provided and received by the attacker, which makes executing this attack expensive.\n108 4.3 Zero-Cost Identities History assumes that entities maintain persistent identities.\nHowever, in most P2P systems, identities are zero-cost.\nThis is desirable for network growth as it encourages newcomers to join the system.\nHowever, this also allows misbehaving users to escape the consequences of their actions by switching to new identities (i.e., whitewashing).\nWhitewashers can cause the system to collapse if they are not punished appropriately.\nUnfortunately, a player cannot tell if a stranger is a whitewasher or a legitimate newcomer.\nAlways cooperating with strangers encourages newcomers to join, but at the same time encourages whitewashing behavior.\nAlways defecting on strangers prevents whitewashing, but discourages newcomers from joining and may also initiate unfavorable cycles of defection.\nThis tension suggests that any stranger policy that has a fixed probability of cooperating with strangers will fail by either being too stingy when most strangers are newcomers or too generous when most strangers are whitewashers.\nOur solution is the Stranger Adaptive stranger policy.\nThe idea is to be generous to strangers when they are being generous and stingy when they are stingy.\nLet ps and cs be the number of services that strangers have provided and consumed, respectively.\nThe probability that a player using Stranger Adaptive helps a stranger is ps/cs.\nHowever, we do not wish to keep these counts permanently (for reasons described in Section 4.4).\nAlso, players may not know when strangers defect because defections are untraceable (as described in Section 2).\nConsequently, instead of keeping ps and cs, we assume that k = ps + cs, where k is a constant and we keep the running ratio r = ps/cs.\nWhen we need to increment ps or cs, we generate the current values of ps and cs from k and r: cs = k/(1 + r) ps = cs ∗ r We then compute the new r as follows: r = (ps + 1)/cs , if the stranger provided service r = ps/(cs + 1) , if the stranger consumed service This method allows us to keep a running ratio that reflects the recent generosity of strangers without knowing when strangers have defected.\n0 1 2 3 4 5 6 0.0001 0.001 0.01 0.1 1 MeanOverallScore/Round Turnover Stranger Cooperate Stranger Defect Stranger Adaptive Figure 11: Different stranger policies for Reciprocative with shared history.\nThe x-axis is the turnover rate on a log scale.\nThe y-axis is the mean overall per round score.\nFigures 11 and 12 compare the effectiveness of the Reciprocative strategy using different policies toward strangers.\nFigure 11 0 1 2 3 4 5 6 0.0001 0.001 0.01 0.1 1 MeanOverallScore/Round Turnover Stranger Cooperate Stranger Defect Stranger Adaptive Figure 12: Different stranger policies for Reciprocative with private history.\nThe x-axis is the turnover rate on a log scale.\nThe y-axis is the mean overall per round score.\ncompares different stranger policies for Reciprocative with shared history, while Figure 12 is with private history.\nIn both figures, the players using the 100% Defect strategy change their identity (whitewash) after every transaction and are indistinguishable from legitimate newcomers.\nThe Reciprocative players using the Stranger Cooperate policy completely fail to achieve cooperation.\nThis stranger policy allows whitewashers to maximize their payoff and consequently provides a high incentive for users to switch to whitewashing.\nIn contrast, Figure 11 shows that the Stranger Defect policy is effective with shared history.\nThis is because whitewashers always appear to be strangers and therefore the Reciprocative players will always defect on them.\nThis is consistent with previous work [13] showing that punishing strangers deals with whitewashers.\nHowever, Figure 12 shows that Stranger Defect is not effective with private history.\nThis is because Reciprocative requires some initial cooperation to bootstrap.\nIn the shared history case, a Reciprocative player can observe that another player has already cooperated with others.\nWith private history, the Reciprocative player only knows about the other players'' actions toward her.\nTherefore, the initial defection dictated by the Stranger Defect policy will lead to later defections, which will prevent Reciprocative players from ever cooperating with each other.\nIn other simulations not shown here, the Stranger Defect stranger policy fails even with shared history when there are no initial 100% Cooperate players.\nFigure 11 shows that with shared history, the Stranger Adaptive policy performs as well as Stranger Defect policy until the turnover rate is very high (10% of the population turning over after every transaction).\nIn these scenarios, Stranger Adaptive is using k = 10 and each player keeps a private r.\nMore importantly, it is significantly better than Stranger Defect policy with private history because it can bootstrap cooperation.\nAlthough the Stranger Defect policy is marginally more effective than Stranger Adaptive at very high rates of turnover, P2P systems are unlikely to operate there because other services (e.g., routing) also cannot tolerate very high turnover.\nWe conclude that of the stranger policies that we have explored, Stranger Adaptive is the most effective.\nBy using Stranger Adaptive, P2P systems with zero-cost identities and a sufficiently low turnover can sustain cooperation without a centralized allocation of identities.\n109 4.4 Traitors Traitors are players who acquire high reputation scores by cooperating for a while, and then traitorously turn into defectors before leaving the system.\nThey model both users who turn deliberately to gain a higher score and cooperators whose identities have been stolen and exploited by defectors.\nA strategy that maintains longterm history without discriminating between old and recent actions becomes highly vulnerable to exploitation by these traitors.\nThe top two graphs in Figure 13 demonstrate the effect of traitors on cooperation in a system where players keep long-term history (never clear history).\nIn these simulations, we run for 2000 rounds and allow cooperative players to keep their identities when switching to the 100% Defector strategy.\nWe use the default values for the other parameters.\nWithout traitors, the cooperative strategies thrive.\nWith traitors, the cooperative strategies thrive until a cooperator turns traitor after 600 rounds.\nAs this cooperator exploits her reputation to achieve a high score, other cooperative players notice this and follow suit via learning.\nCooperation eventually collapses.\nOn the other hand, if we maintain short-term history and/or discounting ancient history vis-a-vis recent history, traitors can be quickly detected, and the overall cooperation level stays high, as shown in the bottom two graphs in Figure 13.\n0 20 40 60 80 100 1K 2K Long-TermHistory No Traitors Population 0 20 40 60 80 100 1K 2K Traitors Defector Cooperator Recip.\nShared 0 20 40 60 80 100 1K 2K Short-TermHistory Time Population 0 20 40 60 80 100 1K 2K Time Figure 13: Keeping long-term vs. short-term history both with and without traitors.\n5.\nRELATED WORK Previous work has examined the incentive problem as applied to societies in general and more recently to Internet applications and peer-to-peer systems in particular.\nA well-known phenomenon in this context is the tragedy of the commons [18] where resources are under-provisioned due to selfish users who free-ride on the system``s resources, and is especially common in large networks [29] [3].\nThe problem has been extensively studied adopting a game theoretic approach.\nThe prisoners'' dilemma model provides a natural framework to study the effectiveness of different strategies in establishing cooperation among players.\nIn a simulation environment with many repeated games, persistent identities, and no collusion, Axelrod [4] shows that the Tit-for-Tat strategy dominates.\nOur model assumes growth follows local learning rather than evolutionary dynamics [14], and also allows for more kinds of attacks.\nNowak and Sigmund [28] introduce the Image strategy and demonstrate its ability to establish cooperation among players despite few repeat transactions by the employment of shared history.\nPlayers using Image cooperate with players whose global count of cooperations minus defections exceeds some threshold.\nAs a result, an Image player is either vulnerable to partial defectors (if the threshold is set too low) or does not cooperate with other Image players (if the threshold is set too high).\nIn recent years, researchers have used economic mechanism design theory to tackle the cooperation problem in Internet applications.\nMechanism design is the inverse of game theory.\nIt asks how to design a game in which the behavior of strategic players results in the socially desired outcome.\nDistributed Algorithmic Mechanism Design seeks solutions within this framework that are both fully distributed and computationally tractable [12].\n[10] and [11] are examples of applying DAMD to BGP routing and multicast cost sharing.\nMore recently, DAMD has been also studied in dynamic environments [38].\nIn this context, demonstrating the superiority of a cooperative strategy (as in the case of our work) is consistent with the objective of incentivizing the desired behavior among selfish players.\nThe unique challenges imposed by peer-to-peer systems inspired additional body of work [5] [37], mainly in the context of packet forwarding in wireless ad-hoc routing [8] [27] [30] [35], and file sharing [15] [31].\nFriedman and Resnick [13] consider the problem of zero-cost identities in online environments and find that in such systems punishing all newcomers is inevitable.\nUsing a theoretical model, they demonstrate that such a system can converge to cooperation only for sufficiently low turnover rates, which our results confirm.\n[6] and [9] show that whitewashing and collusion can have dire consequences for peer-to-peer systems and are difficult to prevent in a fully decentralized system.\nSome commercial file sharing clients [1] [2] provide incentive mechanisms which are enforced by making it difficult for the user to modify the source code.\nThese mechanisms can be circumvented by a skilled user or by a competing company releasing a compatible client without the incentive restrictions.\nAlso, these mechanisms are still vulnerable to zero-cost identities and collusion.\nBitTorrent [7] uses Tit-for-Tat as a method for resource allocation, where a user``s upload rate dictates his download rate.\n6.\nCONCLUSIONS In this paper we take a game theoretic approach to the problem of cooperation in peer-to-peer networks.\nAddressing the challenges imposed by P2P systems, including large populations, high turnover, asymmetry of interest and zero-cost identities, we propose a family of scalable and robust incentive techniques, based upon the Reciprocative decision function, to support cooperative behavior and improve overall system performance.\nWe find that the adoption of shared history and discriminating server selection techniques can mitigate the challenge of few repeat transactions that arises due to large population size, high turnover and asymmetry of interest.\nFurthermore, cooperation can be established even in the presence of zero-cost identities through the use of an adaptive policy towards strangers.\nFinally, colluders and traitors can be kept in check via subjective reputations and short-term history, respectively.\n110 7.\nACKNOWLEDGMENTS We thank Mary Baker, T.J. Giuli, Petros Maniatis, the anonymous reviewer, and our shepherd, Margo Seltzer, for their useful comments that helped improve the paper.\nThis work is supported in part by the National Science Foundation under ITR awards ANI-0085879 and ANI-0331659, and Career award ANI-0133811.\nViews and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of NSF, or the U.S. government.\n8.\nREFERENCES [1] Kazaa.\nhttp://www.kazaa.com.\n[2] Limewire.\nhttp://www.limewire.com.\n[3] ADAR, E., AND HUBERMAN, B. A. Free Riding on Gnutella.\nFirst Monday 5, 10 (October 2000).\n[4] AXELROD, R.\nThe Evolution of Cooperation.\nBasic Books, 1984.\n[5] BURAGOHAIN, C., AGRAWAL, D., AND SURI, S.\nA Game-Theoretic Framework for Incentives in P2P Systems.\nIn International Conference on Peer-to-Peer Computing (Sep 2003).\n[6] CASTRO, M., DRUSCHEL, P., GANESH, A., ROWSTRON, A., AND WALLACH, D. S. 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Distributed Algorithmic Mechanism Design: Recent Results and Future Directions.\nIn Proceedings of the International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications (2002).\n[13] FRIEDMAN, E., AND RESNICK, P.\nThe Social Cost of Cheap Pseudonyms.\nJournal of Economics and Management Strategy 10, 2 (1998), 173-199.\n[14] FUDENBERG, D., AND LEVINE, D. K.\nThe Theory of Learning in Games.\nThe MIT Press, 1999.\n[15] GOLLE, P., LEYTON-BROWN, K., MIRONOV, I., AND LILLIBRIDGE, M. Incentives For Sharing in Peer-to-Peer Networks.\nIn Proceedings of the 3rd ACM conference on Electronic Commerce, October 2001 (2001).\n[16] GROSS, B., AND ACQUISTI, A. Balances of Power on EBay: Peers or Unquals?\nIn Workshop on economics of peer-to-peer networks (2003).\n[17] GU, B., AND JARVENPAA, S. 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Strategyproof mechanisms for ad-hoc network formation.\nIn Workshop on Economics of Peer-to-Peer Systems (June 2003).\n111"},"lvl-2":{"kind":"string","value":"Robust Incentive Techniques for Peer-to-Peer Networks\nLack of cooperation (free riding) is one of the key problems that confronts today's P2P systems.\nWhat makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion, zero-cost identities, and traitors.\nTo tackle these challenges we model the P2P system using the Generalized Prisoner's Dilemma (GPD), and propose the Reciprocative decision function as the basis of a family of incentives techniques.\nThese techniques are fully distributed and include: discriminating server selection, maxflowbased subjective reputation, and adaptive stranger policies.\nThrough simulation, we show that these techniques can drive a system of strategic users to nearly optimal levels of cooperation.\n1.\nINTRODUCTION\nMany peer-to-peer (P2P) systems rely on cooperation among selfinterested users.\nFor example, in a file-sharing system, overall download latency and failure rate increase when users do not share their resources [3].\nIn a wireless ad-hoc network, overall packet latency and loss rate increase when nodes refuse to forward packets on behalf of others [26].\nFurther examples are file preservation [25], discussion boards [17], online auctions [16], and overlay routing [6].\nIn many of these systems, users have natural disincentives to cooperate because cooperation consumes their own resources and may degrade their own performance.\nAs a result, each user's attempt to maximize her own utility effectively lowers the overall\nFigure 1: Example of asymmetry of interest.\nA wants service from B, B wants service form C, and C wants service from A.\nutility of the system.\nAvoiding this \"tragedy of the commons\" [18] requires incentives for cooperation.\nWe adopt a game-theoretic approach in addressing this problem.\nIn particular, we use a prisoners' dilemma model to capture the essential tension between individual and social utility, asymmetric payoff matrices to allow asymmetric transactions between peers, and a learning-based [14] population dynamic model to specify the behavior of individual peers, which can be changed continuously.\nWhile social dilemmas have been studied extensively, P2P applications impose a unique set of challenges, including:\n• Large populations and high turnover: A file sharing system such as Gnutella and KaZaa can exceed 100, 000 simultaneous users, and nodes can have an average life-time of the order of minutes [33].\n• Asymmetry of interest: Asymmetric transactions of P2P systems create the possibility for asymmetry of interest.\nIn the example in Figure 1, A wants service from B, B wants service from C, and C wants service from A. • Zero-cost identity: Many P2P systems allow peers to continuously switch identities (i.e., whitewash).\nStrategies that work well in traditional prisoners' dilemma games such as Tit-for-Tat [4] will not fare well in the P2P context.\nTherefore, we propose a family of scalable and robust incentive techniques, based upon a novel Reciprocative decision function, to address these challenges and provide different tradeoffs:\n• Discriminating Server Selection: Cooperation requires familiarity between entities either directly or indirectly.\nHowever, the large populations and high turnover of P2P systems makes it less likely that repeat interactions will occur with a familiar entity.\nWe show that by having each peer keep a\nprivate history of the actions of other peers toward her, and using discriminating server selection, the Reciprocative decision function can scale to large populations and moderate levels of turnover.\n.\nShared History: Scaling to higher turnover and mitigating asymmetry of interest requires shared history.\nConsider the example in Figure 1.\nIf everyone provides service, then the system operates optimally.\nHowever, if everyone keeps only private history, no one will provide service because B does not know that A has served C, etc. .\nWe show that with shared history, B knows that A served C and consequently will serve A.\nThis results in a higher level of cooperation than with private history.\nThe cost of shared history is a distributed infrastructure (e.g., distributed hash table-based storage) to store the history.\n.\nMaxflow-based Subjective Reputation: Shared history creates the possibility for collusion.\nIn the example in Figure 1, C can falsely claim that A served him, thus deceiving B into providing service.\nWe show that a maxflow-based algorithm that computes reputation subjectively promotes cooperation despite collusion among 1/3 of the population.\nThe basic idea is that B would only believe C if C had already provided service to B.\nThe cost of the maxflow algorithm is its O (V 3) running time, where V is the number of nodes in the system.\nTo eliminate this cost, we have developed a constant mean running time variation, which trades effectiveness for complexity of computation.\nWe show that the maxflow-based algorithm scales better than private history in the presence of colluders without the centralized trust required in previous work [9] [20].\n.\nAdaptive Stranger Policy: Zero-cost identities allows noncooperating peers to escape the consequences of not cooperating and eventually destroy cooperation in the system if not stopped.\nWe show that if Reciprocative peers treat strangers (peers with no history) using a policy that adapts to the behavior of previous strangers, peers have little incentive to whitewash and whitewashing can be nearly eliminated from the system.\nThe adaptive stranger policy does this without requiring centralized allocation of identities, an entry fee for newcomers, or rate-limiting [13] [9] [25].\n.\nShort-term History: History also creates the possibility that a previously well-behaved peer with a good reputation will turn traitor and use his good reputation to exploit other peers.\nThe peer could be making a strategic decision or someone may have hijacked her identity (e.g., by compromising her host).\nLong-term history exacerbates this problem by allowing peers with many previous transactions to exploit that history for many new transactions.\nWe show that short-term history prevents traitors from disrupting cooperation.\nThe rest of the paper is organized as follows.\nWe describe the model in Section 2 and the reciprocative decision function in Section 3.\nWe then proceed to the incentive techniques in Section 4.\nIn Section 4.1, we describe the challenges of large populations and high turnover and show the effectiveness of discriminating server selection and shared history.\nIn Section 4.2, we describe collusion and demonstrate how subjective reputation mitigates it.\nIn Section 4.3, we present the problem of zero-cost identities and show how an adaptive stranger policy promotes persistent identities.\nIn Section 4.4, we show how traitors disrupt cooperation and how short-term history deals with them.\nWe discuss related work in Section 5 and conclude in Section 6.\n2.\nMODEL AND ASSUMPTIONS\nIn this section, we present our assumptions about P2P systems and their users, and introduce a model that aims to capture the behavior of users in a P2P system.\n2.1 Assumptions\nWe assume a P2P system in which users are strategic, i.e., they act rationally to maximize their benefit.\nHowever, to capture some of the real-life unpredictability in the behavior of users, we allow users to randomly change their behavior with a low probability (see Section 2.4).\nFor simplicity, we assume a homogeneous system in which all peers issue and satisfy requests at the same rate.\nA peer can satisfy any request, and, unless otherwise specified, peers request service uniformly at random from the population . '\n.\nFinally, we assume that all transactions incur the same cost to all servers and provide the same benefit to all clients.\nWe assume that users can pollute shared history with false recommendations (Section 4.2), switch identities at zero-cost (Section 4.3), and spoof other users (Section 4.4).\nWe do not assume any centralized trust or centralized infrastructure.\n2.2 Model\nTo aid the development and study of the incentive schemes, in this section we present a model of the users' behaviors.\nIn particular, we model the benefits and costs of P2P interactions (the game) and population dynamics caused by mutation, learning, and turnover.\nOur model is designed to have the following properties that characterize a large set of P2P systems:.\nSocial Dilemma: Universal cooperation should result in optimal overall utility, but individuals who exploit the cooperation of others while not cooperating themselves (i.e., defecting) should benefit more than users who do cooperate.\n.\nAsymmetric Transactions: A peer may want service from another peer while not currently being able to provide the service that the second peer wants.\nTransactions should be able to have asymmetric payoffs.\n.\nUntraceable Defections: A peer should not be able to determine the identity of peers who have defected on her.\nThis models the difficulty or expense of determining that a peer could have provided a service, but didn't.\nFor example, in the Gnutella file sharing system [21], a peer may simply ignore queries despite possessing the desired file, thus preventing the querying peer from identifying the defecting peer.\n.\nDynamic Population: Peers should be able to change their behavior and enter or leave the system independently and continuously.\nFigure 2: Payoff matrix for the Generalized Prisoner's Dilemma.\nT, R, P, and S stand for temptation, reward, punishment and sucker, respectively.\n2.3 Generalized Prisoner's Dilemma\nThe Prisoner's Dilemma, developed by Flood, Dresher, and Tucker in 1950 [22] is a non-cooperative repeated game satisfying the social dilemma requirement.\nEach game consists of two players who can defect or cooperate.\nDepending how each acts, the players receive a payoff.\nThe players use a strategy to decide how to act.\nUnfortunately, existing work either uses a specific asymmetric payoff matrix or only gives the general form for a symmetric one [4].\nInstead, we use the Generalized Prisoner's Dilemma (GPD), which specifies the general form for an asymmetric payoff matrix that preserves the social dilemma.\nIn the GPD, one player is the client and one player is the server in each game, and it is only the decision of the server that is meaningful for determining the outome of the transaction.\nA player can be a client in one game and a server in another.\nThe client and server receive the payoff from a generalized payoff matrix (Figure 2).\nRte, Ste, T., and P. are the client's payoff and R., S., T., and P. are the server's payoff.\nA GPD payoff matrix must have the following properties to create a social dilemma:\n1.\nMutual cooperation leads to higher payoffs than mutual defection (R. + R.> P. + P.).\n2.\nMutual cooperation leads to higher payoffs than one player suckering the other (R. + R> S + T. and R. + R> S. + T.).\n3.\nDefection dominates cooperation (at least weakly) at the individual level for the entity who decides whether to cooperate or defect: (T.> - R. and P.> - S. and (T.> R. or P.> S.))\nThe last set of inequalities assume that clients do not incur a cost regardless of whether they cooperate or defect, and therefore clients always cooperate.\nThese properties correspond to similar properties of the classic Prisoner's Dilemma and allow any form of asymmetric transaction while still creating a social dilemma.\nFurthermore, one or more of the four possible actions (client cooperate and defect, and server cooperate and defect) can be untraceable.\nIf one player makes an untraceable action, the other player does not know the identity of the first player.\nFor example, to model a P2P application like file sharing or overlay routing, we use the specific payoff matrix values shown in Figure 3.\nThis satisfies the inequalities specified above, where only the server can choose between cooperating and defecting.\nIn addition, for this particular payoff matrix, clients are unable to trace server defections.\nThis is the payoff matrix that we use in our simulation results.\nFigure 3: The payoff matrix for an application like P2P file sharing or overlay routing.\n2.4 Population Dynamics\nA characteristic of P2P systems is that peers change their behavior and enter or leave the system independently and continuously.\nSeveral studies [4] [28] of repeated Prisoner's Dilemma games use an evolutionary model [19] [34] of population dynamics.\nAn evolutionary model is not suitable for P2P systems because it only specifies the global behavior and all changes occur at discrete times.\nFor example, it may specify that a population of 5 \"100% Cooperate\" players and 5 \"100% Defect\" players evolves into a population with 3 and 7 players, respectively.\nIt does not specify which specific players switched.\nFurthermore, all the switching occurs at the end of a generation instead of continuously, like in a real P2P system.\nAs a result, evolutionary population dynamics do not accurately model turnover, traitors, and strangers.\nIn our model, entities take independent and continuous actions that change the composition of the population.\nTime consists of rounds.\nIn each round, every player plays one game as a client and one game as a server.\nAt the end of a round, a player may: 1) mutate 2) learn, 3) turnover, or 4) stay the same.\nIf a player mutates, she switches to a randomly picked strategy.\nIf she learns, she switches to a strategy that she believes will produce a higher score (described in more detail below).\nIf she maintains her identity after switching strategies, then she is referred to as a traitor.\nIf a player suffers turnover, she leaves the system and is replaced with a newcomer who uses the same strategy as the exiting player.\nTo learn, a player collects local information about the performance of different strategies.\nThis information consists of both her personal observations of strategy performance and the observations of those players she interacts with.\nThis models users communicating out-of-band about how strategies perform.\nLet s be the running average of the performance of a player's current strategy per round and age be the number of rounds she has been using the strategy.\nA strategy's rating is\nWe use the age and compute the running average before the ratio to prevent young samples (which are more likely to be outliers) from skewing the rating.\nAt the end of a round, a player switches to highest rated strategy with a probability proportional to the difference in score between her current strategy and the highest rated strategy.\n3.\nRECIPROCATIVE DECISION FUNCTION\nIn this section, we present the new decision function, Reciprocative, that is the basis for our incentive techniques.\nA decision function maps from a history of a player's actions to a decision whether to cooperate with or defect on that player.\nA strategy consists of a decision function, private or shared history, a server selection mechanism, and a stranger policy.\nOur approach to incentives is to design strategies which maximize both individual and social benefit.\nStrategic users will choose to use such strategies and thereby drive the system to high levels of cooperation.\nTwo examples of simple decision functions are \"100% Cooperate\" and \"100% Defect\".\n\"100% Cooperate\" models a naive user who does not yet realize that she is being exploited.\n\"100% Defect\" models a greedy user who is intent on exploiting the system.\nIn the absence of incentive techniques, \"100% Defect\" users will quickly dominate the \"100% Cooperate\" users and destroy cooperation in the system.\nOur requirements for a decision function are that (1) it can use shared and subjective history, (2) it can deal with untraceable defections, and (3) it is robust against different patterns of defection.\nPrevious decision functions such as Tit-for-Tat [4] and Image [28] (see Section 5) do not satisfy these criteria.\nFor example, Tit-for-Tat and Image base their decisions on both cooperations and defections, therefore cannot deal with untraceable defections.\nIn this section and the remaining sections we demonstrate how the Reciprocativebased strategies satisfy all of the requirements stated above.\nThe probability that a Reciprocative player cooperates with a peer is a function of its normalized generosity.\nGenerosity measures the benefit an entity has provided relative to the benefit it has consumed.\nThis is important because entities which consume more services than they provide, even if they provide many services, will cause cooperation to collapse.\nFor some entity i, let pi and ci be the services i has provided and consumed, respectively.\nEntity i's generosity is simply the ratio of the service it provides to the service it consumes:\nOne possibility is to cooperate with a probability equal to the generosity.\nAlthough this is effective in some cases, in other cases, a Reciprocative player may consume more than she provides (e.g., when initially using the \"Stranger Defect\" policy in 4.3).\nThis will cause Reciprocative players to defect on each other.\nTo prevent this situation, a Reciprocative player uses its own generosity as a measuring stick to judge its peer's generosity.\nNormalized generosity measures entity i's generosity relative to entity j's generosity.\nMore concretely, entity i's normalized generosity as perceived by entity j is\nIn the remainder of this section, we describe our simulation framework, and use it to demonstrate the benefits of the baseline Reciprocative decision function.\nTable 1: Default simulation parameters.\n3.1 Simulation Framework\nOur simulator implements the model described in Section 2.\nWe use the asymmetric file sharing payoff matrix (Figure 3) with untraceable defections because it models transactions in many P2P systems like file-sharing and packet forwarding in ad hoc and overlay networks.\nOur simulation study is composed of different scenarios reflecting the challenges of various non-cooperative behaviors.\nTable 1 presents the nominal parameter values used in our simulation.\nThe \"Ratio using\" rows refer to the initial ratio of the total population using a particular strategy.\nIn each scenario we vary the value range of a specific parameter to reflect a particular situation or attack.\nWe then vary the exact properties of the Reciprocative strategy to defend against that situation or attack.\n3.2 Baseline Results\nFigure 4: The evolution of strategy populations over time.\n\"Time\" the number of elapsed rounds.\n\"Population\" is the number of players using a strategy.\nIn this section, we present the dynamics of the game for the basic scenario presented in Table 1 to familiarize the reader and set a baseline for more complicated scenarios.\nFigures 4 (a) (60 players) and (b) (120 players) show players switching to higher scoring strategies over time in two separate runs of the simulator.\nEach point in the graph represents the number of players using a particular strategy at one point in time.\nFigures 5 (a) and (b) show the corresponding mean overall score per round.\nThis measures the degree of cooperation in the system: 6 is the maximum possible (achieved when everybody cooperates) and 0 is the minimum (achieved when everybody defects).\nFrom the file sharing payoff matrix, a net of 6 means everyone is able to download a file and a 0 means that no one\nFigure 5: The mean overall per round score over time.\nis able to do so.\nWe use this metric in all later results to evaluate our incentive techniques.\nFigure 5 (a) shows that the Reciprocative strategy using private history causes a system of 60 players to converge to a cooperation level of 3.7, but drops to 0.5 for 120 players.\nOne would expect the 60 player system to reach the optimal level of cooperation (6) because all the defectors are eliminated from the system.\nIt does not because of asymmetry of interest.\nFor example, suppose player B is using Reciprocative with private history.\nPlayer A may happen to ask for service from player B twice in succession without providing service to player B in the interim.\nPlayer B does not know of the service player A has provided to others, so player B will reject service to player A, even though player A is cooperative.\nWe discuss solutions to asymmetry of interest and the failure of Reciprocative in the 120 player system in Section 4.1.\n4.\nRECIPROCATIVE-BASED INCENTIVE TECHNIQUES\nIn this section we present our incentives techniques and evaluate their behavior by simulation.\nTo make the exposition clear we group our techniques by the challenges they address: large populations and high turnover (Section 4.1), collusions (Section 4.2), zero-cost identities (Section 4.3), and traitors (Section 4.4).\n4.1 Large Populations and High Turnover\nThe large populations and high turnover of P2P systems makes it less likely that repeat interactions will occur with a familiar entity.\nUnder these conditions, basing decisions only on private history (records about interactions the peer has been directly involved in) is not effective.\nIn addition, private history does not deal well with asymmetry of interest.\nFor example, if player B has cooperated with others but not with player A himself in the past, player A has no indication of player B's generosity, thus may unduly defect on him.\nWe propose two mechanisms to alleviate the problem of few repeat transactions: server-selection and shared history.\n4.1.1 Server Selection\nA natural way to increase the probability of interacting with familiar peers is by discriminating server selection.\nHowever, the asymmetry of transactions challenges selection mechanisms.\nUnlike in the prisoner's dilemma payoff matrix, where players can benefit one another within a single transaction, transactions in GPD are asymmetric.\nAs a result, a player who selects her donor for the second time without contributing to her in the interim may face a defection.\nIn addition, due to untraceability of defections, it is impossible to maintain blacklists to avoid interactions with known defectors.\nIn order to deal with asymmetric transactions, every player holds (fixed size) lists of both past donors and past recipients, and selects a server from one of these lists at random with equal probabilities.\nThis way, users approach their past recipients and give them a chance to reciprocate.\nIn scenarios with selective users we omit the complete availability assumption to prevent players from being clustered into a lot of very small groups; thus, we assume that every player can perform the requested service with probability P (for the results presented in this section, P = .3).\nIn addition, in order to avoid bias in favor of the selective players, all players (including the non-discriminative ones) select servers for games.\nFigure 6 demonstrates the effectiveness of the proposed selection mechanism in scenarios with large population sizes.\nWe fix the initial ratio of Reciprocative in the population (33%) while varying the population size (between 24 to 1000) (Notice that while in Figures 4 (a) and (b), the data points demonstrates the evolution of the system over time, each data point in this figure is the result of an entire simulation for a specific scenario).\nThe figure shows that the Reciprocative decision function using private history in conjunction with selective behavior can scale to large populations.\nIn Figure 7 we fix the population size and vary the turnover rate.\nIt demonstrates that while selective behavior is effective for low turnover rates, as turnover gets higher, selective behavior does not scale.\nThis occurs because selection is only effective as long as players from the past stay alive for long enough such that they can be selected for future games.\n4.1.2 Shared history\nIn order to mitigate asymmetry of interest and scale to higher turnover rate, there is a need in shared history.\nShared history means that every peer keeps records about all of the interactions that occur in the system, regardless of whether he was directly involved in them or not.\nIt allows players to leverage off of the experiences of others in cases of few repeat transactions.\nIt only requires that someone has interacted with a particular player for the entire population to observe it, thus scales better to large populations and high turnovers, and also tolerates asymmetry of interest.\nSome examples of shared history schemes are [20] [23] [28].\nFigure 7 shows the effectiveness of shared history under high turnover rates.\nIn this figure, we fix the population size and vary the turnover rate.\nWhile selective players with private history can only tolerate a moderate turnover, shared history scales to turnovers of up to approximately 0.1.\nThis means that 10% of the players leave the system at the end of each round.\nIn Figure 6 we fix the turnover and vary the population size.\nIt shows that shared history causes the system to converge to optimal cooperation and performance, regardless of the size of the population.\nThese results show that shared history addresses all three challenges of large populations, high turnover, and asymmetry of transactions.\nNevertheless, shared history has two disadvantages.\nFirst,\nFigure 7: Performance of selection mechanism under turnover.\nThe x-axis is the turnover rate.\nThe y-axis is the mean overall per round score.\nwhile a decentralized implementation of private history is straightforward, implementation of shared-history requires communication overhead or centralization.\nA decentralized shared history can be implemented, for example, on top of a DHT, using a peer-to-peer storage system [36] or by disseminating information to other entities in a similar way to routing protocols.\nSecond, and more fundamental, shared history is vulnerable to collusion.\nIn the next section we propose a mechanism that addresses this problem.\n4.2 Collusion and Other Shared History Attacks\n4.2.1 Collusion\nWhile shared history is scalable, it is vulnerable to collusion.\nCollusion can be either positive (e.g. defecting entities claim that other defecting entities cooperated with them) or negative (e.g. entities claim that other cooperative entities defected on them).\nCollusion subverts any strategy in which everyone in the system agrees on the reputation of a player (objective reputation).\nAn example of objective reputation is to use the Reciprocative decision function with shared history to count the total number of cooperations a player has given to and received from all entities in the system; another example is the Image strategy [28].\nThe effect of collusion is magnified in systems with zero-cost identities, where users can create fake identities that report false statements.\nInstead, to deal with collusion, entities can compute reputation subjectively, where player A weighs player B's opinions based on how much player A trusts player B.\nOur subjective algorithm is based on maxflow [24] [32].\nMaxflow is a graph theoretic problem, which given a directed graph with weighted edges asks what is the greatest rate at which \"material\" can be shipped from the source to the target without violating any capacity constraints.\nFor example, in figure 8 each edge is labeled with the amount of traffic that can travel on it.\nThe maxflow algorithm computes the maximum amount of traffic that can go from the source (s) to the target (t) without violating the constraints.\nIn this example, even though there is a loop of high capacity edges, the maxflow between the source and the target is only 2 (the numbers in brackets represent the actual flow on each edge in the solution).\nFigure 8: Each edge in the graph is labeled with its capacity and the actual flow it carries in brackets.\nThe maxflow between the source and the target in the graph is 2.\nFigure 9: This graph illustrates the robustness of maxflow in the presence of colluders who report bogus high reputation values.\nWe apply the maxflow algorithm by constructing a graph whose vertices are entities and the edges are the services that entities have received from each other.\nThis information can be stored using the same methods as the shared history.\nA maxflow is the greatest level of reputation the source can give to the sink without violating \"reputation capacity\" constraints.\nAs a result, nodes who dishonestly report high reputation values will not be able to subvert the reputation system.\nFigure 9 illustrates a scenario in which all the colluders (labeled with C) report high reputation values for each other.\nWhen node A computes the subjective reputation of B using the maxflow algorithm, it will not be affected by the local false reputation values, rather the maxflow in this case will be 0.\nThis is because no service has been received from any of the colluders.\nFigure 6: Private vs. Shared History as a function of population size.\nIn our algorithm, the benefit that entity i has received (indirectly) from entity j is the maxflow from j to i. Conversely, the benefit that entity i has provided indirectly to j is the maxflow from i to j.\nThe subjective reputation of entity j as perceived by i is:\nFigure 10: Subjective shared history compared to objective shared history and private history in the presence of colluders.\nThe cost of maxflow is its long running time.\nThe standard preflowpush maxflow algorithm has a worst case running time of O (V 3).\nInstead, we use Algorithm 1 which has a constant mean running time, but sometimes returns no flow even though one exists.\nThe essential idea is to bound the mean number of nodes examined during the maxflow computation.\nThis bounds the overhead, but also bounds the effectiveness.\nDespite this, the results below show that a maxflow-based Reciprocative decision function scales to higher populations than one using private history.\nFigure 10 compares the effectiveness of subjective reputation to objective reputation in the presence of colluders.\nIn these scenarios, defectors collude by claiming that other colluders that they encounter gave them 100 cooperations for that encounter.\nAlso, the parameters for Algorithm 1 are set as follows: increment = 100, a = 0.9.\nAs in previous sections, Reciprocative with private history results in cooperation up to a point, beyond which it fails.\nThe difference here is that objective shared history fails for all population sizes.\nThis is because the Reciprocative players cooperate with the colluders because of their high reputations.\nHowever, subjective history can reach high levels of cooperation regardless of colluders.\nThis is because there are no high weight paths in the cooperation graph from colluders to any non-colluders, so the maxflow from a colluder to any non-colluder is 0.\nTherefore, a subjective Reciprocative player will conclude that that colluder has not provided any service to her and will reject service to the colluder.\nThus, the maxflow algorithm enables Reciprocative to maintain the scalability of shared history without being vulnerable to collusion or requiring centralized trust (e.g., trusted peers).\nSince we bound the running time of the maxflow algorithm, cooperation decreases as the population size increases, but the key point is that the subjective Reciprocative decision function scales to higher populations than one using private history.\nThis advantage only increases over time as CPU power increases and more cycles can be devoted to running the maxflow algorithm (by increasing the increment parameter).\nDespite the robustness of the maxflow algorithm to the simple form of collusion described previously, it still has vulnerabilities to more sophisticated attacks.\nOne is for an entity (the \"mole\") to provide service and then lie positively about other colluders.\nThe other colluders can then exploit their reputation to receive service.\nHowever, the effectiveness of this attack relies on the amount of service that the mole provides.\nSince the mole is paying all of the cost of providing service and receiving none of the benefit, she has a strong incentive to stop colluding and try another strategy.\nThis forces the colluders to use mechanisms to maintain cooperation within their group, which may drive the cost of collusion to exceed the benefit.\n4.2.2 False reports\nAnother attack is for a defector to lie about receiving or providing service to another entity.\nThere are four possibile actions that can be lied about: providing service, not providing service, receiving service, and not receiving service.\nFalsely claiming to receive service is the simple collusion attack described above.\nFalsely claiming not to have provided service provides no benefit to the attacker.\nFalsely claiming to have provided service or not to have received it allows an attacker to boost her own reputation and/or lower the reputation of another entity.\nAn entity may want to lower another entity's reputation in order to discourage others from selecting it and exclusively use its service.\nThese false claims are clearly identifiable in the shared history as inconsistencies where one entity claims a transaction occurred and another claims it did not.\nTo limit this attack, we modify the maxflow algorithm so that an entity always believes the entity that is closer to him in the flow graph.\nIf both entities are equally distant, then the disputed edge in the flow is not critical to the evaluation and is ignored.\nThis modification prevents those cases where the attacker is making false claims about an entity that is closer than her to the evaluating entity, which prevents her from boosting her own reputation.\nThe remaining possibilities are for the attacker to falsely claim to have provided service to or not to have received it from a victim entity that is farther from the evalulator than her.\nIn these cases, an attacker can only lower the reputation of the victim.\nThe effectiveness of doing this is limited by the number of services provided and received by the attacker, which makes executing this attack expensive.\n4.3 Zero-Cost Identities\nHistory assumes that entities maintain persistent identities.\nHowever, in most P2P systems, identities are zero-cost.\nThis is desirable for network growth as it encourages newcomers to join the system.\nHowever, this also allows misbehaving users to escape the consequences of their actions by switching to new identities (i.e., whitewashing).\nWhitewashers can cause the system to collapse if they are not punished appropriately.\nUnfortunately, a player cannot tell if a stranger is a whitewasher or a legitimate newcomer.\nAlways cooperating with strangers encourages newcomers to join, but at the same time encourages whitewashing behavior.\nAlways defecting on strangers prevents whitewashing, but discourages newcomers from joining and may also initiate unfavorable cycles of defection.\nThis tension suggests that any stranger policy that has a fixed probability of cooperating with strangers will fail by either being too stingy when most strangers are newcomers or too generous when most strangers are whitewashers.\nOur solution is the \"Stranger Adaptive\" stranger policy.\nThe idea is to be generous to strangers when they are being generous and stingy when they are stingy.\nLet ps and cs be the number of services that strangers have provided and consumed, respectively.\nThe probability that a player using \"Stranger Adaptive\" helps a stranger is ps/cs.\nHowever, we do not wish to keep these counts permanently (for reasons described in Section 4.4).\nAlso, players may not know when strangers defect because defections are untraceable (as described in Section 2).\nConsequently, instead of keeping ps and cs, we assume that k = ps + cs, where k is a constant and we keep the running ratio r = ps/cs.\nWhen we need to increment ps or cs, we generate the current values of ps and cs from k and r:\nWe then compute the new r as follows:\nThis method allows us to keep a running ratio that reflects the recent generosity of strangers without knowing when strangers have defected.\nFigure 11: Different stranger policies for Reciprocative with shared history.\nThe x-axis is the turnover rate on a log scale.\nThe y-axis is the mean overall per round score.\nFigures 11 and 12 compare the effectiveness of the Reciprocative strategy using different policies toward strangers.\nFigure 11\nFigure 12: Different stranger policies for Reciprocative with private history.\nThe x-axis is the turnover rate on a log scale.\nThe y-axis is the mean overall per round score.\ncompares different stranger policies for Reciprocative with shared history, while Figure 12 is with private history.\nIn both figures, the players using the \"100% Defect\" strategy change their identity (whitewash) after every transaction and are indistinguishable from legitimate newcomers.\nThe Reciprocative players using the \"Stranger Cooperate\" policy completely fail to achieve cooperation.\nThis stranger policy allows whitewashers to maximize their payoff and consequently provides a high incentive for users to switch to whitewashing.\nIn contrast, Figure 11 shows that the \"Stranger Defect\" policy is effective with shared history.\nThis is because whitewashers always appear to be strangers and therefore the Reciprocative players will always defect on them.\nThis is consistent with previous work [13] showing that punishing strangers deals with whitewashers.\nHowever, Figure 12 shows that \"Stranger Defect\" is not effective with private history.\nThis is because Reciprocative requires some initial cooperation to bootstrap.\nIn the shared history case, a Reciprocative player can observe that another player has already cooperated with others.\nWith private history, the Reciprocative player only knows about the other players' actions toward her.\nTherefore, the initial defection dictated by the \"Stranger Defect\" policy will lead to later defections, which will prevent Reciprocative players from ever cooperating with each other.\nIn other simulations not shown here, the \"Stranger Defect\" stranger policy fails even with shared history when there are no initial \"100% Cooperate\" players.\nFigure 11 shows that with shared history, the \"Stranger Adaptive\" policy performs as well as \"Stranger Defect\" policy until the turnover rate is very high (10% of the population turning over after every transaction).\nIn these scenarios, \"Stranger Adaptive\" is using k = 10 and each player keeps a private r.\nMore importantly, it is significantly better than \"Stranger Defect\" policy with private history because it can bootstrap cooperation.\nAlthough the \"Stranger Defect\" policy is marginally more effective than \"Stranger Adaptive\" at very high rates of turnover, P2P systems are unlikely to operate there because other services (e.g., routing) also cannot tolerate very high turnover.\nWe conclude that of the stranger policies that we have explored, \"Stranger Adaptive\" is the most effective.\nBy using \"Stranger Adaptive\", P2P systems with zero-cost identities and a sufficiently low turnover can sustain cooperation without a centralized allocation of identities.\n4.4 Traitors\nTraitors are players who acquire high reputation scores by cooperating for a while, and then traitorously turn into defectors before leaving the system.\nThey model both users who turn deliberately to gain a higher score and cooperators whose identities have been stolen and exploited by defectors.\nA strategy that maintains longterm history without discriminating between old and recent actions becomes highly vulnerable to exploitation by these traitors.\nThe top two graphs in Figure 13 demonstrate the effect of traitors on cooperation in a system where players keep long-term history (never clear history).\nIn these simulations, we run for 2000 rounds and allow cooperative players to keep their identities when switching to the 100% Defector strategy.\nWe use the default values for the other parameters.\nWithout traitors, the cooperative strategies thrive.\nWith traitors, the cooperative strategies thrive until a cooperator turns traitor after 600 rounds.\nAs this \"cooperator\" exploits her reputation to achieve a high score, other cooperative players notice this and follow suit via learning.\nCooperation eventually collapses.\nOn the other hand, if we maintain short-term history and/or discounting ancient history vis-a-vis recent history, traitors can be quickly detected, and the overall cooperation level stays high, as shown in the bottom two graphs in Figure 13.\nFigure 13: Keeping long-term vs. short-term history both with and without traitors.\n5.\nRELATED WORK\nPrevious work has examined the incentive problem as applied to societies in general and more recently to Internet applications and peer-to-peer systems in particular.\nA well-known phenomenon in this context is the \"tragedy of the commons\" [18] where resources are under-provisioned due to selfish users who free-ride on the system's resources, and is especially common in large networks [29] [3].\nThe problem has been extensively studied adopting a game theoretic approach.\nThe prisoners' dilemma model provides a natural framework to study the effectiveness of different strategies in establishing cooperation among players.\nIn a simulation environment with many repeated games, persistent identities, and no collusion, Axelrod [4] shows that the Tit-for-Tat strategy dominates.\nOur model assumes growth follows local learning rather than evolutionary dynamics [14], and also allows for more kinds of attacks.\nNowak and Sigmund [28] introduce the Image strategy and demonstrate its ability to establish cooperation among players despite few repeat transactions by the employment of shared history.\nPlayers using Image cooperate with players whose global count of cooperations minus defections exceeds some threshold.\nAs a result, an Image player is either vulnerable to partial defectors (if the threshold is set too low) or does not cooperate with other Image players (if the threshold is set too high).\nIn recent years, researchers have used economic \"mechanism design\" theory to tackle the cooperation problem in Internet applications.\nMechanism design is the inverse of game theory.\nIt asks how to design a game in which the behavior of strategic players results in the socially desired outcome.\nDistributed Algorithmic Mechanism Design seeks solutions within this framework that are both fully distributed and computationally tractable [12].\n[10] and [11] are examples of applying DAMD to BGP routing and multicast cost sharing.\nMore recently, DAMD has been also studied in dynamic environments [38].\nIn this context, demonstrating the superiority of a cooperative strategy (as in the case of our work) is consistent with the objective of incentivizing the desired behavior among selfish players.\nThe unique challenges imposed by peer-to-peer systems inspired additional body of work [5] [37], mainly in the context of packet forwarding in wireless ad-hoc routing [8] [27] [30] [35], and file sharing [15] [31].\nFriedman and Resnick [13] consider the problem of zero-cost identities in online environments and find that in such systems punishing all newcomers is inevitable.\nUsing a theoretical model, they demonstrate that such a system can converge to cooperation only for sufficiently low turnover rates, which our results confirm.\n[6] and [9] show that whitewashing and collusion can have dire consequences for peer-to-peer systems and are difficult to prevent in a fully decentralized system.\nSome commercial file sharing clients [1] [2] provide incentive mechanisms which are enforced by making it difficult for the user to modify the source code.\nThese mechanisms can be circumvented by a skilled user or by a competing company releasing a compatible client without the incentive restrictions.\nAlso, these mechanisms are still vulnerable to zero-cost identities and collusion.\nBitTorrent [7] uses Tit-for-Tat as a method for resource allocation, where a user's upload rate dictates his download rate.\n6.\nCONCLUSIONS\nIn this paper we take a game theoretic approach to the problem of cooperation in peer-to-peer networks.\nAddressing the challenges imposed by P2P systems, including large populations, high turnover, asymmetry of interest and zero-cost identities, we propose a family of scalable and robust incentive techniques, based upon the Reciprocative decision function, to support cooperative behavior and improve overall system performance.\nWe find that the adoption of shared history and discriminating server selection techniques can mitigate the challenge of few repeat transactions that arises due to large population size, high turnover and asymmetry of interest.\nFurthermore, cooperation can be established even in the presence of zero-cost identities through the use of an adaptive policy towards strangers.\nFinally, colluders and traitors can be kept in check via subjective reputations and short-term history, respectively."},"lvl-3":{"kind":"string","value":"Robust Incentive Techniques for Peer-to-Peer Networks\nLack of cooperation (free riding) is one of the key problems that confronts today's P2P systems.\nWhat makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion, zero-cost identities, and traitors.\nTo tackle these challenges we model the P2P system using the Generalized Prisoner's Dilemma (GPD), and propose the Reciprocative decision function as the basis of a family of incentives techniques.\nThese techniques are fully distributed and include: discriminating server selection, maxflowbased subjective reputation, and adaptive stranger policies.\nThrough simulation, we show that these techniques can drive a system of strategic users to nearly optimal levels of cooperation.\n1.\nINTRODUCTION\nMany peer-to-peer (P2P) systems rely on cooperation among selfinterested users.\nFor example, in a file-sharing system, overall download latency and failure rate increase when users do not share their resources [3].\nIn a wireless ad-hoc network, overall packet latency and loss rate increase when nodes refuse to forward packets on behalf of others [26].\nFurther examples are file preservation [25], discussion boards [17], online auctions [16], and overlay routing [6].\nIn many of these systems, users have natural disincentives to cooperate because cooperation consumes their own resources and may degrade their own performance.\nAs a result, each user's attempt to maximize her own utility effectively lowers the overall\nFigure 1: Example of asymmetry of interest.\nA wants service from B, B wants service form C, and C wants service from A.\nutility of the system.\nAvoiding this \"tragedy of the commons\" [18] requires incentives for cooperation.\nWe adopt a game-theoretic approach in addressing this problem.\nIn particular, we use a prisoners' dilemma model to capture the essential tension between individual and social utility, asymmetric payoff matrices to allow asymmetric transactions between peers, and a learning-based [14] population dynamic model to specify the behavior of individual peers, which can be changed continuously.\nWhile social dilemmas have been studied extensively, P2P applications impose a unique set of challenges, including:\n• Large populations and high turnover: A file sharing system such as Gnutella and KaZaa can exceed 100, 000 simultaneous users, and nodes can have an average life-time of the order of minutes [33].\n• Asymmetry of interest: Asymmetric transactions of P2P systems create the possibility for asymmetry of interest.\nIn the example in Figure 1, A wants service from B, B wants service from C, and C wants service from A. • Zero-cost identity: Many P2P systems allow peers to continuously switch identities (i.e., whitewash).\nStrategies that work well in traditional prisoners' dilemma games such as Tit-for-Tat [4] will not fare well in the P2P context.\nTherefore, we propose a family of scalable and robust incentive techniques, based upon a novel Reciprocative decision function, to address these challenges and provide different tradeoffs:\n• Discriminating Server Selection: Cooperation requires familiarity between entities either directly or indirectly.\nHowever, the large populations and high turnover of P2P systems makes it less likely that repeat interactions will occur with a familiar entity.\nWe show that by having each peer keep a\nprivate history of the actions of other peers toward her, and using discriminating server selection, the Reciprocative decision function can scale to large populations and moderate levels of turnover.\n.\nShared History: Scaling to higher turnover and mitigating asymmetry of interest requires shared history.\nConsider the example in Figure 1.\nIf everyone provides service, then the system operates optimally.\nHowever, if everyone keeps only private history, no one will provide service because B does not know that A has served C, etc. .\nWe show that with shared history, B knows that A served C and consequently will serve A.\nThis results in a higher level of cooperation than with private history.\nThe cost of shared history is a distributed infrastructure (e.g., distributed hash table-based storage) to store the history.\n.\nMaxflow-based Subjective Reputation: Shared history creates the possibility for collusion.\nIn the example in Figure 1, C can falsely claim that A served him, thus deceiving B into providing service.\nWe show that a maxflow-based algorithm that computes reputation subjectively promotes cooperation despite collusion among 1/3 of the population.\nThe basic idea is that B would only believe C if C had already provided service to B.\nThe cost of the maxflow algorithm is its O (V 3) running time, where V is the number of nodes in the system.\nTo eliminate this cost, we have developed a constant mean running time variation, which trades effectiveness for complexity of computation.\nWe show that the maxflow-based algorithm scales better than private history in the presence of colluders without the centralized trust required in previous work [9] [20].\n.\nAdaptive Stranger Policy: Zero-cost identities allows noncooperating peers to escape the consequences of not cooperating and eventually destroy cooperation in the system if not stopped.\nWe show that if Reciprocative peers treat strangers (peers with no history) using a policy that adapts to the behavior of previous strangers, peers have little incentive to whitewash and whitewashing can be nearly eliminated from the system.\nThe adaptive stranger policy does this without requiring centralized allocation of identities, an entry fee for newcomers, or rate-limiting [13] [9] [25].\n.\nShort-term History: History also creates the possibility that a previously well-behaved peer with a good reputation will turn traitor and use his good reputation to exploit other peers.\nThe peer could be making a strategic decision or someone may have hijacked her identity (e.g., by compromising her host).\nLong-term history exacerbates this problem by allowing peers with many previous transactions to exploit that history for many new transactions.\nWe show that short-term history prevents traitors from disrupting cooperation.\nThe rest of the paper is organized as follows.\nWe describe the model in Section 2 and the reciprocative decision function in Section 3.\nWe then proceed to the incentive techniques in Section 4.\nIn Section 4.1, we describe the challenges of large populations and high turnover and show the effectiveness of discriminating server selection and shared history.\nIn Section 4.2, we describe collusion and demonstrate how subjective reputation mitigates it.\nIn Section 4.3, we present the problem of zero-cost identities and show how an adaptive stranger policy promotes persistent identities.\nIn Section 4.4, we show how traitors disrupt cooperation and how short-term history deals with them.\nWe discuss related work in Section 5 and conclude in Section 6.\n2.\nMODEL AND ASSUMPTIONS\n2.1 Assumptions\n2.2 Model\n2.3 Generalized Prisoner's Dilemma\n2.4 Population Dynamics\n3.\nRECIPROCATIVE DECISION FUNCTION\n3.1 Simulation Framework\n3.2 Baseline Results\n4.\nRECIPROCATIVE-BASED INCENTIVE TECHNIQUES\n4.1 Large Populations and High Turnover\n4.1.1 Server Selection\n4.1.2 Shared history\n4.2 Collusion and Other Shared History Attacks\n4.2.1 Collusion\n4.2.2 False reports\n4.3 Zero-Cost Identities\n4.4 Traitors\n5.\nRELATED WORK\nPrevious work has examined the incentive problem as applied to societies in general and more recently to Internet applications and peer-to-peer systems in particular.\nA well-known phenomenon in this context is the \"tragedy of the commons\" [18] where resources are under-provisioned due to selfish users who free-ride on the system's resources, and is especially common in large networks [29] [3].\nThe problem has been extensively studied adopting a game theoretic approach.\nThe prisoners' dilemma model provides a natural framework to study the effectiveness of different strategies in establishing cooperation among players.\nIn a simulation environment with many repeated games, persistent identities, and no collusion, Axelrod [4] shows that the Tit-for-Tat strategy dominates.\nOur model assumes growth follows local learning rather than evolutionary dynamics [14], and also allows for more kinds of attacks.\nNowak and Sigmund [28] introduce the Image strategy and demonstrate its ability to establish cooperation among players despite few repeat transactions by the employment of shared history.\nPlayers using Image cooperate with players whose global count of cooperations minus defections exceeds some threshold.\nAs a result, an Image player is either vulnerable to partial defectors (if the threshold is set too low) or does not cooperate with other Image players (if the threshold is set too high).\nIn recent years, researchers have used economic \"mechanism design\" theory to tackle the cooperation problem in Internet applications.\nMechanism design is the inverse of game theory.\nIt asks how to design a game in which the behavior of strategic players results in the socially desired outcome.\nDistributed Algorithmic Mechanism Design seeks solutions within this framework that are both fully distributed and computationally tractable [12].\n[10] and [11] are examples of applying DAMD to BGP routing and multicast cost sharing.\nMore recently, DAMD has been also studied in dynamic environments [38].\nIn this context, demonstrating the superiority of a cooperative strategy (as in the case of our work) is consistent with the objective of incentivizing the desired behavior among selfish players.\nThe unique challenges imposed by peer-to-peer systems inspired additional body of work [5] [37], mainly in the context of packet forwarding in wireless ad-hoc routing [8] [27] [30] [35], and file sharing [15] [31].\nFriedman and Resnick [13] consider the problem of zero-cost identities in online environments and find that in such systems punishing all newcomers is inevitable.\nUsing a theoretical model, they demonstrate that such a system can converge to cooperation only for sufficiently low turnover rates, which our results confirm.\n[6] and [9] show that whitewashing and collusion can have dire consequences for peer-to-peer systems and are difficult to prevent in a fully decentralized system.\nSome commercial file sharing clients [1] [2] provide incentive mechanisms which are enforced by making it difficult for the user to modify the source code.\nThese mechanisms can be circumvented by a skilled user or by a competing company releasing a compatible client without the incentive restrictions.\nAlso, these mechanisms are still vulnerable to zero-cost identities and collusion.\nBitTorrent [7] uses Tit-for-Tat as a method for resource allocation, where a user's upload rate dictates his download rate.\n6.\nCONCLUSIONS\nIn this paper we take a game theoretic approach to the problem of cooperation in peer-to-peer networks.\nAddressing the challenges imposed by P2P systems, including large populations, high turnover, asymmetry of interest and zero-cost identities, we propose a family of scalable and robust incentive techniques, based upon the Reciprocative decision function, to support cooperative behavior and improve overall system performance.\nWe find that the adoption of shared history and discriminating server selection techniques can mitigate the challenge of few repeat transactions that arises due to large population size, high turnover and asymmetry of interest.\nFurthermore, cooperation can be established even in the presence of zero-cost identities through the use of an adaptive policy towards strangers.\nFinally, colluders and traitors can be kept in check via subjective reputations and short-term history, respectively."},"lvl-4":{"kind":"string","value":"Robust Incentive Techniques for Peer-to-Peer Networks\nLack of cooperation (free riding) is one of the key problems that confronts today's P2P systems.\nWhat makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion, zero-cost identities, and traitors.\nTo tackle these challenges we model the P2P system using the Generalized Prisoner's Dilemma (GPD), and propose the Reciprocative decision function as the basis of a family of incentives techniques.\nThese techniques are fully distributed and include: discriminating server selection, maxflowbased subjective reputation, and adaptive stranger policies.\nThrough simulation, we show that these techniques can drive a system of strategic users to nearly optimal levels of cooperation.\n1.\nINTRODUCTION\nMany peer-to-peer (P2P) systems rely on cooperation among selfinterested users.\nFor example, in a file-sharing system, overall download latency and failure rate increase when users do not share their resources [3].\nIn many of these systems, users have natural disincentives to cooperate because cooperation consumes their own resources and may degrade their own performance.\nAs a result, each user's attempt to maximize her own utility effectively lowers the overall\nFigure 1: Example of asymmetry of interest.\nutility of the system.\nAvoiding this \"tragedy of the commons\" [18] requires incentives for cooperation.\nWe adopt a game-theoretic approach in addressing this problem.\nWhile social dilemmas have been studied extensively, P2P applications impose a unique set of challenges, including:\n• Large populations and high turnover: A file sharing system such as Gnutella and KaZaa can exceed 100, 000 simultaneous users, and nodes can have an average life-time of the order of minutes [33].\n• Asymmetry of interest: Asymmetric transactions of P2P systems create the possibility for asymmetry of interest.\nIn the example in Figure 1, A wants service from B, B wants service from C, and C wants service from A. • Zero-cost identity: Many P2P systems allow peers to continuously switch identities (i.e., whitewash).\nStrategies that work well in traditional prisoners' dilemma games such as Tit-for-Tat [4] will not fare well in the P2P context.\nTherefore, we propose a family of scalable and robust incentive techniques, based upon a novel Reciprocative decision function, to address these challenges and provide different tradeoffs:\n• Discriminating Server Selection: Cooperation requires familiarity between entities either directly or indirectly.\nHowever, the large populations and high turnover of P2P systems makes it less likely that repeat interactions will occur with a familiar entity.\nWe show that by having each peer keep a\nprivate history of the actions of other peers toward her, and using discriminating server selection, the Reciprocative decision function can scale to large populations and moderate levels of turnover.\n.\nShared History: Scaling to higher turnover and mitigating asymmetry of interest requires shared history.\nConsider the example in Figure 1.\nIf everyone provides service, then the system operates optimally.\nHowever, if everyone keeps only private history, no one will provide service because B does not know that A has served C, etc. .\nWe show that with shared history, B knows that A served C and consequently will serve A.\nThis results in a higher level of cooperation than with private history.\nThe cost of shared history is a distributed infrastructure (e.g., distributed hash table-based storage) to store the history.\n.\nMaxflow-based Subjective Reputation: Shared history creates the possibility for collusion.\nIn the example in Figure 1, C can falsely claim that A served him, thus deceiving B into providing service.\nWe show that a maxflow-based algorithm that computes reputation subjectively promotes cooperation despite collusion among 1/3 of the population.\nThe cost of the maxflow algorithm is its O (V 3) running time, where V is the number of nodes in the system.\nWe show that the maxflow-based algorithm scales better than private history in the presence of colluders without the centralized trust required in previous work [9] [20].\n.\nAdaptive Stranger Policy: Zero-cost identities allows noncooperating peers to escape the consequences of not cooperating and eventually destroy cooperation in the system if not stopped.\nWe show that if Reciprocative peers treat strangers (peers with no history) using a policy that adapts to the behavior of previous strangers, peers have little incentive to whitewash and whitewashing can be nearly eliminated from the system.\nThe adaptive stranger policy does this without requiring centralized allocation of identities, an entry fee for newcomers, or rate-limiting [13] [9] [25].\n.\nShort-term History: History also creates the possibility that a previously well-behaved peer with a good reputation will turn traitor and use his good reputation to exploit other peers.\nThe peer could be making a strategic decision or someone may have hijacked her identity (e.g., by compromising her host).\nLong-term history exacerbates this problem by allowing peers with many previous transactions to exploit that history for many new transactions.\nWe show that short-term history prevents traitors from disrupting cooperation.\nWe describe the model in Section 2 and the reciprocative decision function in Section 3.\nWe then proceed to the incentive techniques in Section 4.\nIn Section 4.1, we describe the challenges of large populations and high turnover and show the effectiveness of discriminating server selection and shared history.\nIn Section 4.2, we describe collusion and demonstrate how subjective reputation mitigates it.\nIn Section 4.3, we present the problem of zero-cost identities and show how an adaptive stranger policy promotes persistent identities.\nIn Section 4.4, we show how traitors disrupt cooperation and how short-term history deals with them.\nWe discuss related work in Section 5 and conclude in Section 6.\n5.\nRELATED WORK\nPrevious work has examined the incentive problem as applied to societies in general and more recently to Internet applications and peer-to-peer systems in particular.\nThe problem has been extensively studied adopting a game theoretic approach.\nThe prisoners' dilemma model provides a natural framework to study the effectiveness of different strategies in establishing cooperation among players.\nIn a simulation environment with many repeated games, persistent identities, and no collusion, Axelrod [4] shows that the Tit-for-Tat strategy dominates.\nNowak and Sigmund [28] introduce the Image strategy and demonstrate its ability to establish cooperation among players despite few repeat transactions by the employment of shared history.\nPlayers using Image cooperate with players whose global count of cooperations minus defections exceeds some threshold.\nIn recent years, researchers have used economic \"mechanism design\" theory to tackle the cooperation problem in Internet applications.\nMechanism design is the inverse of game theory.\nIt asks how to design a game in which the behavior of strategic players results in the socially desired outcome.\n[10] and [11] are examples of applying DAMD to BGP routing and multicast cost sharing.\nMore recently, DAMD has been also studied in dynamic environments [38].\nFriedman and Resnick [13] consider the problem of zero-cost identities in online environments and find that in such systems punishing all newcomers is inevitable.\nUsing a theoretical model, they demonstrate that such a system can converge to cooperation only for sufficiently low turnover rates, which our results confirm.\n[6] and [9] show that whitewashing and collusion can have dire consequences for peer-to-peer systems and are difficult to prevent in a fully decentralized system.\nSome commercial file sharing clients [1] [2] provide incentive mechanisms which are enforced by making it difficult for the user to modify the source code.\nThese mechanisms can be circumvented by a skilled user or by a competing company releasing a compatible client without the incentive restrictions.\nAlso, these mechanisms are still vulnerable to zero-cost identities and collusion.\nBitTorrent [7] uses Tit-for-Tat as a method for resource allocation, where a user's upload rate dictates his download rate.\n6.\nCONCLUSIONS\nIn this paper we take a game theoretic approach to the problem of cooperation in peer-to-peer networks.\nAddressing the challenges imposed by P2P systems, including large populations, high turnover, asymmetry of interest and zero-cost identities, we propose a family of scalable and robust incentive techniques, based upon the Reciprocative decision function, to support cooperative behavior and improve overall system performance.\nWe find that the adoption of shared history and discriminating server selection techniques can mitigate the challenge of few repeat transactions that arises due to large population size, high turnover and asymmetry of interest.\nFurthermore, cooperation can be established even in the presence of zero-cost identities through the use of an adaptive policy towards strangers.\nFinally, colluders and traitors can be kept in check via subjective reputations and short-term history, respectively."}}},{"rowIdx":99,"cells":{"id":{"kind":"string","value":"I-38"},"title":{"kind":"string","value":"Bidding Algorithms for a Distributed Combinatorial Auction"},"abstract":{"kind":"string","value":"Distributed allocation and multiagent coordination problems can be solved through combinatorial auctions. However, most of the existing winner determination algorithms for combinatorial auctions are centralized. The PAUSE auction is one of a few efforts to release the auctioneer from having to do all the work (it might even be possible to get rid of the auctioneer). It is an increasing price combinatorial auction that naturally distributes the problem of winner determination amongst the bidders in such a way that they have an incentive to perform the calculation. It can be used when we wish to distribute the computational load among the bidders or when the bidders do not wish to reveal their true valuations unless necessary. PAUSE establishes the rules the bidders must obey. However, it does not tell us how the bidders should calculate their bids. We have developed a couple of bidding algorithms for the bidders in a PAUSE auction. Our algorithms always return the set of bids that maximizes the bidder's utility. Since the problem is NP-Hard, run time remains exponential on the number of items, but it is remarkably better than an exhaustive search. In this paper we present our bidding algorithms, discuss their virtues and drawbacks, and compare the solutions obtained by them to the revenue-maximizing solution found by a centralized winner determination algorithm."},"keyphrases":{"kind":"list like","value":["bid algorithm","combinatori auction","distribut alloc","coordin","paus auction","task and resourc alloc","revenu-maxim solut","combinatori optim problem","agent","progress adapt user select environ","branch and bound search","search tree","branch-on-bid tree"],"string":"[\n \"bid algorithm\",\n \"combinatori auction\",\n \"distribut alloc\",\n \"coordin\",\n \"paus auction\",\n \"task and resourc alloc\",\n \"revenu-maxim solut\",\n \"combinatori optim problem\",\n \"agent\",\n \"progress adapt user select environ\",\n \"branch and bound search\",\n \"search tree\",\n \"branch-on-bid tree\"\n]","isConversational":false},"prmu":{"kind":"list like","value":["P","P","P","P","P","M","M","M","U","U","M","M","U"],"string":"[\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"P\",\n \"M\",\n \"M\",\n \"M\",\n \"U\",\n \"U\",\n \"M\",\n \"M\",\n \"U\"\n]","isConversational":false},"lvl-1":{"kind":"string","value":"Bidding Algorithms for a Distributed Combinatorial Auction Benito Mendoza ∗ and Jos´e M. Vidal Computer Science and Engineering University of South Carolina Columbia, SC 29208 mendoza2@engr.sc.edu, vidal@sc.edu ABSTRACT Distributed allocation and multiagent coordination problems can be solved through combinatorial auctions.\nHowever, most of the existing winner determination algorithms for combinatorial auctions are centralized.\nThe PAUSE auction is one of a few efforts to release the auctioneer from having to do all the work (it might even be possible to get rid of the auctioneer).\nIt is an increasing price combinatorial auction that naturally distributes the problem of winner determination amongst the bidders in such a way that they have an incentive to perform the calculation.\nIt can be used when we wish to distribute the computational load among the bidders or when the bidders do not wish to reveal their true valuations unless necessary.\nPAUSE establishes the rules the bidders must obey.\nHowever, it does not tell us how the bidders should calculate their bids.\nWe have developed a couple of bidding algorithms for the bidders in a PAUSE auction.\nOur algorithms always return the set of bids that maximizes the bidder``s utility.\nSince the problem is NP-Hard, run time remains exponential on the number of items, but it is remarkably better than an exhaustive search.\nIn this paper we present our bidding algorithms, discuss their virtues and drawbacks, and compare the solutions obtained by them to the revenue-maximizing solution found by a centralized winner determination algorithm.\nCategories and Subject Descriptors I.2.11 [Computing Methodologies]: Distributed Artificial Intelligence-Intelligent Agents, Multiagent Systems.\nGeneral Terms Algorithms, Performance.\n1.\nINTRODUCTION Both the research and practice of combinatorial auctions have grown rapidly in the past ten years.\nIn a combinatorial auction bidders can place bids on combinations of items, called packages or bidsets, rather than just individual items.\nOnce the bidders place their bids, it is necessary to find the allocation of items to bidders that maximizes the auctioneer``s revenue.\nThis problem, known as the winner determination problem, is a combinatorial optimization problem and is NP-Hard [10].\nNevertheless, several algorithms that have a satisfactory performance for problem sizes and structures occurring in practice have been developed.\nThe practical applications of combinatorial auctions include: allocation of airport takeoff and landing time slots, procurement of freight transportation services, procurement of public transport services, and industrial procurement [2].\nBecause of their wide applicability, one cannot hope for a general-purpose winner determination algorithm that can efficiently solve every instance of the problem.\nThus, several approaches and algorithms have been proposed to address the winner determination problem.\nHowever, most of the existing winner determination algorithms for combinatorial auctions are centralized, meaning that they require all agents to send their bids to a centralized auctioneer who then determines the winners.\nExamples of these algorithms are CASS [3], Bidtree [11] and CABOB [12].\nWe believe that distributed solutions to the winner determination problem should be studied as they offer a better fit for some applications as when, for example, agents do not want to reveal their valuations to the auctioneer.\nThe PAUSE (Progressive Adaptive User Selection Environment) auction [4, 5] is one of a few efforts to distribute the problem of winner determination amongst the bidders.\nPAUSE establishes the rules the participants have to adhere to so that the work is distributed amongst them.\nHowever, it is not concerned with how the bidders determine what they should bid.\nIn this paper we present two algorithms, pausebid and cachedpausebid, which enable agents in a PAUSE auction to find the bidset that maximizes their utility.\nOur algorithms implement a myopic utility maximizing strategy and are guaranteed to find the bidset that maximizes the agent``s utility given the outstanding best bids at a given time.\npausebid performs a branch and bound search completely from scratch every time that it is called.\ncachedpausebid is a caching-based algorithm which explores fewer nodes, since it caches some solutions.\n694 978-81-904262-7-5 (RPS) c 2007 IFAAMAS 2.\nTHE PAUSE AUCTION A PAUSE auction for m items has m stages.\nStage 1 consists of having simultaneous ascending price open-cry auctions and during this stage the bidders can only place bids on individual items.\nAt the end of this state we will know what the highest bid for each individual item is and who placed that bid.\nEach successive stage k = 2, 3, ... , m consists of an ascending price auction where the bidders must submit bidsets that cover all items but each one of the bids must be for k items or less.\nThe bidders are allowed to use bids that other agents have placed in previous rounds when building their bidsets, thus allowing them to find better solutions.\nAlso, any new bidset has to have a sum of bid prices which is bigger than that of the currently winning bidset.\nAt the end of each stage k all agents know the best bid for every subset of size k or less.\nAlso, at any point in time after stage 1 has ended there is a standing bidset whose value increases monotonically as new bidsets are submitted.\nSince in the final round all agents consider all possible bidsets, we know that the final winning bidset will be one such that no agent can propose a better bidset.\nNote, however, that this bidset is not guaranteed to be the one that maximizes revenue since we are using an ascending price auction so the winning bid for each set will be only slightly bigger than the second highest bid for the particular set of items.\nThat is, the final prices will not be the same as the prices in a traditional combinatorial auction where all the bidders bid their true valuation.\nHowever, there remains the open question of whether the final distribution of items to bidders found in a PAUSE auction is the same as the revenue maximizing solution.\nOur test results provide an answer to this question.\nThe PAUSE auction makes the job of the auctioneer very easy.\nAll it has to do is to make sure that each new bidset has a revenue bigger than the current winning bidset, as well as make sure that every bid in an agent``s bidset that is not his does indeed correspond to some other agents'' previous bid.\nThe computational problem shifts from one of winner determination to one of bid generation.\nEach agent must search over the space of all bidsets which contain at least one of its bids.\nThe search is made easier by the fact that the agent needs to consider only the current best bids and only wants bidsets where its own utility is higher than in the current winning bidset.\nEach agent also has a clear incentive for performing this computation, namely, its utility only increases with each bidset it proposes (of course, it might decrease with the bidsets that others propose).\nFinally, the PAUSE auction has been shown to be envy-free in that at the conclusion of the auction no bidder would prefer to exchange his allocation with that of any other bidder [2].\nWe can even envision completely eliminating the auctioneer and, instead, have every agent perform the task of the auctioneer.\nThat is, all bids are broadcast and when an agent receives a bid from another agent it updates the set of best bids and determines if the new bid is indeed better than the current winning bid.\nThe agents would have an incentive to perform their computation as it will increase their expected utility.\nAlso, any lies about other agents'' bids are easily found out by keeping track of the bids sent out by every agent (the set of best bids).\nNamely, the only one that can increase an agent``s bid value is the agent itself.\nAnyone claiming a higher value for some other agent is lying.\nThe only thing missing is an algorithm that calculates the utility-maximizing bidset for each agent.\n3.\nPROBLEM FORMULATION A bid b is composed of three elements bitems (the set of items the bid is over), bagent (the agent that placed the bid), and bvalue (the value or price of the bid).\nThe agents maintain a set B of the current best bids, one for each set of items of size ≤ k, where k is the current stage.\nAt any point in the auction, after the first round, there will also be a set W ⊆ B of currently winning bids.\nThis is the set of bids that covers all the items and currently maximizes the revenue, where the revenue of W is given by r(W) = b∈W bvalue .\n(1) Agent i``s value function is given by vi(S) ∈ where S is a set of items.\nGiven an agent``s value function and the current winning bidset W we can calculate the agent``s utility from W as ui(W) = b∈W | bagent=i vi(bitems ) − bvalue .\n(2) That is, the agent``s utility for a bidset W is the value it receives for the items it wins in W minus the price it must pay for those items.\nIf the agent is not winning any items then its utility is zero.\nThe goal of the bidding agents in the PAUSE auction is to maximize their utility, subject to the constraint that their next set of bids must have a total revenue that is at least bigger than the current revenue, where is the smallest increment allowed in the auction.\nFormally, given that W is the current winning bidset, agent i must find a g∗ i such that r(g∗ i ) ≥ r(W) + and g∗ i = arg max g⊆2B ui(g), (3) where each g is a set of bids that covers all items and ∀b∈g (b ∈ B) or (bagent = i and bvalue > B(bitems ) and size(bitems ) ≤ k), and where B(items) is the value of the bid in B for the set items (if there is no bid for those items it returns zero).\nThat is, each bid b in g must satisfy at least one of the two following conditions.\n1) b is already in B, 2) b is a bid of size ≤ k in which the agent i bids higher than the price for the same items in B. 4.\nBIDDING ALGORITHMS According to the PAUSE auction, during the first stage we have only several English auctions, with the bidders submitting bids on individual items.\nIn this case, an agent``s dominant strategy is to bid higher than the current winning bid until it reaches its valuation for that particular item.\nOur algorithms focus on the subsequent stages: k > 1.\nWhen k > 1, agents have to find g∗ i .\nThis can be done by performing a complete search on B. However, this approach is computationally expensive since it produces a large search tree.\nOur algorithms represent alternative approaches to overcome this expensive search.\n4.1 The PAUSEBID Algorithm In the pausebid algorithm (shown in Figure 1) we implement some heuristics to prune the search tree.\nGiven that bidders want to maximize their utility and that at any given point there are likely only a few bids within B which The Sixth Intl..\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07) 695 pausebid(i, k) 1 my-bids ← ∅ 2 their-bids ← ∅ 3 for b ∈ B 4 do if bagent = i or vi(bitems ) > bvalue 5 then my-bids ← my-bids +new Bid(bitems , i, vi(bitems )) 6 else their-bids ← their-bids +b 7 for S ∈ subsets of k or fewer items such that vi(S) > 0 and ¬∃b∈Bbitems = S 8 do my-bids ← my-bids +new Bid(S, i, vi(S)) 9 bids ← my-bids + their-bids 10 g∗ ← ∅ # Global variable 11 u∗ ← ui(W)# Global variable 12 pbsearch(bids, ∅) 13 surplus ← b∈g∗ | bagent=i bvalue − B(bitems ) 14 if surplus = 0 15 then return g∗ 16 my-payment ← vi(g∗ ) − u∗ 17 for b ∈ g∗ | bagent = i 18 do if my-payment ≤ 0 19 then bvalue ← B(bitems ) 20 else bvalue ← B(bitems ) + my-payment ·bvalue −B(bitems ) surplus 21 return g∗ Figure 1: The pausebid algorithm which implements a branch and bound search.\ni is the agent and k is the current stage of the auction, for k ≥ 2.\nthe agent can dominate, we start by defining my-bids to be the list of bids for which the agent``s valuation is higher than the current best bid, as given in B.\nWe set the value of these bids to be the agent``s true valuation (but we won``t necessarily be bidding true valuation, as we explain later).\nSimilarly, we set their-bids to be the rest of the bids from B. Finally, the agent``s search list is simply the concatenation of my-bids and their-bids.\nNote that the agent``s own bids are placed first on the search list as this will enable us to do more pruning (pausebid lines 3 to 9).\nThe agent can now perform a branch and bound search on the branch-on-bids tree produced by these bids.\nThis branch and bound search is implemented by pbsearch (Figure 2).\nOur algorithm not only implements the standard bound but it also implements other pruning techniques in order to further reduce the size of the search tree.\nThe bound we use is the maximum utility that the agent can expect to receive from a given set of bids.\nWe call it u∗ .\nInitially, u∗ is set to ui(W) (pausebid line 11) since that is the utility the agent currently receives and any solution he proposes should give him more utility.\nIf pbsearch ever comes across a partial solution where the maximum utility the agent can expect to receive is less than u∗ then that subtree is pruned (pbsearch line 21).\nNote that we can determine the maximum utility only after the algorithm has searched over all of the agent``s own bids (which are first on the list) because after that we know that the solution will not include any more bids where the agent is the winner thus the agent``s utility will no longer increase.\nFor example, pbsearch(bids, g) 1 if bids = ∅ then return 2 b ← first(bids) 3 bids ← bids −b 4 g ← g + b 5 ¯Ig ← items not in g 6 if g does not contain a bid from i 7 then return 8 if g includes all items 9 then min-payment ← max(0, r(W) + − (r(g) − ri(g)), b∈g | bagent=i B(bitems )) 10 max-utility ← vi(g) − min-payment 11 if r(g) > r(W) and max-utility ≥ u∗ 12 then g∗ ← g 13 u∗ ← max-utility 14 pbsearch(bids, g − b) # b is Out 15 else max-revenue ← r(g) + max(h(¯Ig), hi(¯Ig)) 16 if max-revenue ≤ r(W) 17 then pbsearch(bids, g − b) # b is Out 18 elseif bagent = i 19 then min-payment ← (r(W) + ) −(r(g) − ri(g)) − h(¯Ig) 20 max-utility ← vi(g) − min-payment 21 if max-utility > u∗ 22 then pbsearch({x ∈ bids | xitems ∩ bitems = ∅}, g) # b is In 23 pbsearch(bids, g − b) # b is Out 24 else 25 pbsearch({x ∈ bids | xitems ∩ bitems = ∅}, g) # b is In 26 pbsearch(bids, g − b) # b is Out 27 return Figure 2: The pbsearch recursive procedure where bids is the set of available bids and g is the current partial solution.\nif an agent has only one bid in my-bids then the maximum utility he can expect is equal to his value for the items in that bid minus the minimum possible payment we can make for those items and still come up with a set of bids that has revenue greater than r(W).\nThe calculation of the minimum payment is shown in line 19 for the partial solution case and line 9 for the case where we have a complete solution in pbsearch.\nNote that in order to calculate the min-payment for the partial solution case we need an upper bound on the payments that we must make for each item.\nThis upper bound is provided by h(S) = s∈S max b∈B | s∈bitems bvalue size(bitems) .\n(4) This function produces a bound identical to the one used by the Bidtree algorithm-it merely assigns to each individual item in S a value equal to the maximum bid in B divided by the number of items in that bid.\nTo prune the branches that cannot lead to a solution with revenue greater than the current W, the algorithm considers both the values of the bids in B and the valuations of the 696 The Sixth Intl..\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07) agent.\nSimilarly to (4) we define hi(S, k) = s∈S max S | size(S )≤k and s∈S and vi(S )>0 vi(S ) size(S ) (5) which assigns to each individual item s in S the maximum value produced by the valuation of S divided by the size of S , where S is a set for which the agent has a valuation greater than zero, contains s, and its size is less or equal than k.\nThe algorithm uses the heuristics h and hi (lines 15 and 19 of pbsearch), to prune the just mentioned branches in the same way an A∗ algorithm uses its heuristic.\nA final pruning technique implemented by the algorithm is ignoring any branches where the agent has no bids in the current answer g and no more of the agent``s bids are in the list (pbsearch lines 6 and 7).\nThe resulting g∗ found by pbsearch is thus the set of bids that has revenue bigger than r(W) and maximizes agent i``s utility.\nHowever, agent i``s bids in g∗ are still set to his own valuation and not to the lowest possible price.\nLines 17 to 20 in pausebid are responsible for setting the agent``s payments so that it can achieve its maximum utility u∗ .\nIf the agent has only one bid in g∗ then it is simply a matter of reducing the payment of that bid by u∗ from the current maximum of the agent``s true valuation.\nHowever, if the agent has more than one bid then we face the problem of how to distribute the agent``s payments among these bids.\nThere are many ways of distributing the payments and there does not appear to be a dominant strategy for performing this distribution.\nWe have chosen to distribute the payments in proportion to the agent``s true valuation for each set of items.\npausebid assumes that the set of best bids B and the current best winning bidset W remains constant during its execution, and it returns the agent``s myopic utility-maximizing bidset (if there is one) using a branch and bound search.\nHowever it repeats the whole search at every stage.\nWe can minimize this problem by caching the result of previous searches.\n4.2 The CACHEDPAUSEBID Algorithm The cachedpausebid algorithm (shown in Figure 3) is our second approach to solve the bidding problem in the PAUSE auction.\nIt is based in a cache table called C-Table where we store some solutions to avoid doing a complete search every time.\nThe problem is the same; the agent i has to find g∗ i .\nWe note that g∗ i is a bidset that contains at least one bid of the agent i. Let S be a set of items for which the agent i has a valuation such that vi(S) ≥ B(S) > 0, let gS i be a bidset over S such that r(gS i ) ≥ r(W) + and gS i = arg max g⊆2B ui(g), (6) where each g is a set of bids that covers all items and ∀b∈g (b ∈ B) or (bagent = i and bvalue > B(bitems )) and (∃b∈gbitems = S and bagent = i).\nThat is, gS i is i``s best bidset for all items which includes a bid from i for all S items.\nIn the PAUSE auction we cannot bid for sets of items with size greater than k. So, if we have for each set of items S for which vi(S) > 0 and size(S) ≤ k its corresponding gS i then g∗ i is the gS i that maximizes the agent``s utility.\nThat is g∗ i = arg max {S | vi(S)>0∧size(S)≤k} ui(gS i ).\n(7) Each agent i implements a hash table C-Table such that C-Table[S] = gS for all S which vi(S) ≥ B(S) > 0.\nWe can cachedpausebid(i, k, k-changed) 1 for each S in C-Table 2 do if vi(S) < B(S) 3 then remove S from C-Table 4 else if k-changed and size(S) = k 5 then B ← B + new Bid(i, S, vi(S)) 6 g∗ ← ∅ 7 u∗ ← ui(W) 8 for each S with size(S) ≤ k in C-Table 9 do ¯S ← Items − S 10 gS ← C-Table[S] # Global variable 11 min-payment ← max(r(W) + , b∈gS B(bitems )) 12 uS ← r(gS ) − min-payment # Global variable 13 if (k-changed and size(S) = k) or (∃b∈B bitems ⊆ ¯S and bagent = i) 14 then B ← {b ∈ B |bitems ⊆ ¯S} 15 bids ← B +{b ∈ B|bitems ⊆ ¯S and b /∈ B } 16 for b ∈ bids 17 do if vi(bitems ) > bvalue 18 then bagent ← i 19 bvalue ← vi(bitems ) 20 if k-changed and size(S) = k 21 then n ← size(bids) 22 uS ← 0 23 else n ← size(B ) 24 g ← ∅ + new Bid(S, i, vi(S)) 25 cpbsearch(bids, g, n) 26 C-Table[S] ← gS 27 if uS > u∗ and r(gS ) ≥ r(W) + 28 then surplus ← b∈gS | bagent=i bvalue − B(bitems ) 29 if surplus > 0 30 then my-payment ← vi(gS ) − ui(gS ) 31 for b ∈ gS | bagent = i 32 do if my-payment ≤ 0 33 then bvalue ← B(bitems ) 34 else bvalue ← B(bitems )+ my-payment ·bvalue −B(bitems ) surplus 35 u∗ ← ui(gS ) 36 g∗ ← gS 37 else if uS ≤ 0 and vi(S) < B(S) 38 then remove S from C-Table 39 return g∗ Figure 3: The cachedpausebid algorithm that implements a caching based search to find a bidset that maximizes the utility for the agent i. k is the current stage of the auction (for k ≥ 2), and k-changed is a boolean that is true right after the auction moved to the next stage.\nThe Sixth Intl..\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07) 697 cpbsearch(bids, g, n) 1 if bids = ∅ or n ≤ 0 then return 2 b ← first(bids) 3 bids ← bids −b 4 g ← g + b 5 ¯Ig ← items not in g 6 if g includes all items 7 then min-payment ← max(0, r(W) + − (r(g) − ri(g)), b∈g | bagent=i B(bitems )) 8 max-utility ← vi(g) − min-payment 9 if r(g) > r(W) and max-utility ≥ uS 10 then gS ← g 11 uS ← max-utility 12 cpbsearch(bids, g − b, n − 1) # b is Out 13 else max-revenue ← r(g) + max(h(¯Ig), hi(¯Ig)) 14 if max-revenue ≤ r(W) 15 then cpbsearch(bids, g − b, n − 1) # b is Out 16 elseif bagent = i 17 then min-payment ← (r(W) + ) −(r(g) − ri(g)) − h(¯Ig) 18 max-utility ← vi(g) − min-payment 19 if max-utility > uS 20 then cpbsearch({x ∈ bids | xitems ∩ bitems = ∅}, g, n + 1) # b is In 21 cpbsearch(bids, g − b, n − 1) # b is Out 22 else 23 cpbsearch({x ∈ bids | xitems ∩ bitems = ∅}, g, n + 1) # b is In 24 cpbsearch(bids, g − b, n − 1) # b is Out 25 return Figure 4: The cpbsearch recursive procedure where bids is the set of available bids, g is the current partial solution and n is a value that indicates how deep in the list bids the algorithm has to search.\nthen find g∗ by searching for the gS , stored in C-Table[S], that maximizes the agent``s utility, considering only the set of items S with size(S) ≤ k.\nThe problem remains in maintaining the C-Table updated and avoiding to search every gS every time.\ncachedpausebid deals with this and other details.\nLet B be the set of bids that contains the new best bids, that is, B contains the bids recently added to B and the bids that have changed price (always higher), bidder, or both and were already in B. Let ¯S = Items − S be the complement of S (the set of items not included in S).\ncachedpausebid takes three parameters: i the agent, k the current stage of the auction, and k-changed a boolean that is true right after the auction moved to the next stage.\nInitially C-Table has one row or entry for each set S for which vi(S) > 0.\nWe start by eliminating the entries corresponding to each set S for which vi(S) < B(S) from C-Table (line 3).\nThen, in the case that k-changed is true, for each set S with size(S) = k, we add to B a bid for that set with value equal to vi(S) and bidder agent i (line 5); this a bid that the agent is now allowed to consider.\nWe then search for g∗ amongst the gS stored in C-Table, for this we only need to consider the sets with size(S) ≤ k (line 8).\nBut how do we know that the gS in C-Table[S] is still the best solution for S?\nThere are only two cases when we are not sure about that and we need to do a search to update C-Table[S].\nThese cases are: i) When k-changed is true and size(S) ≤ k, since there was no gS stored in C-Table for this S. ii) When there exists at least one bid in B for the set of items ¯S or a subset of it submitted by an agent different than i, since it is probable that this new bid can produce a solution better than the one stored in C-Table[S].\nWe handle the two cases mentioned above in lines 13 to 26 of cachedpausebid.\nIn both of these cases, since gS must contain a bid for S we need to find a bidset that cover the missing items, that is ¯S. Thus, our search space consists of all the bids on B for the set of items ¯S or for a subset of it.\nWe build the list bids that contains only those bids.\nHowever, we put the bids from B at the beginning of bids (line 14) since they are the ones that have changed.\nThen, we replace the bids in bids that have a price lower than the valuation the agent i has for those same items with a bid from agent i for those items and value equal to the agent``s valuation (lines 16-19).\nThe recursive procedure cpbsearch, called in line 25 of cachedpausebid and shown in Figure 4, is the one that finds the new gS .\ncpbsearch is a slightly modified version of our branch and bound search implemented in pbsearch.\nThe first modification is that it has a third parameter n that indicates how deep on the list bids we want to search, since it stops searching when n less or equal to zero and not only when the list bids is empty (line 1).\nEach time that there is a recursive call of cpbsearch n is decreased by one when a bid from bids is discarded or out (lines 12, 15, 21, and 24) and n remains the same otherwise (lines 20 and 23).\nWe set the value of n before calling cpbsearch, to be the size of the list bids (cachedpausebid line 21) in case i), since we want cpbsearch to search over all bids; and we set n to be the number of bids from B included in bids (cachedpausebid line 23) in case ii), since we know that only the those first n bids in bids changed and can affect our current gS .\nAnother difference with pbsearch is that the bound in cpbsearch is uS which we set to be 0 (cachedpausebid line 22) when in case i) and r(gS )−min-payment (cachedpausebid line 12) when in case ii).\nWe call cpbsearch with g already containing a bid for S.\nAfter cpbsearch is executed we are sure that we have the right gS , so we store it in the corresponding C-Table[S] (cachedpausebid line 26).\nWhen we reach line 27 in cachedpausebid, we are sure that we have the right gS .\nHowever, agent i``s bids in gS are still set to his own valuation and not to the lowest possible price.\nIf uS is greater than the current u∗ , lines 31 to 34 in cachedpausebid are responsible for setting the agent``s payments so that it can achieve its maximum utility uS .\nAs in pausebid, we have chosen to distribute the payments in proportion to the agent``s true valuation for each set of items.\nIn the case that uS less than or equal to zero and the valuation that the agent i has for the set of items S is lower than the current value of the bid in B for the same set of items, we remove the corresponding C-Table[S] since we know that is not worthwhile to keep it in the cache table (cachedpausebid line 38).\nThe cachedpausebid function is called when k > 1 and returns the agent``s myopic utility-maximizing bidset, if there is one.\nIt assumes that W and B remains constant during its execution.\n698 The Sixth Intl..\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07) generatevalues(i, items) 1 for x ∈ items 2 do vi(x) = expd(.01) 3 for n ← 1 ... (num-bids − items) 4 do s1, s2 ←Two random sets of items with values.\n5 vi(s1 ∪ s2) = vi(s1) + vi(s2) + expd(.01) Figure 5: Algorithm for the generation of random value functions.\nexpd(x) returns a random number taken from an exponential distribution with mean 1/x.\n0 20 40 60 80 100 2 3 4 5 6 7 8 9 10 Number of Items CachedPauseBid 3 3 3 3 3 3 3 3 3 3 PauseBid + + + + + + + + + + Figure 6: Average percentage of convergence (y-axis), which is the percentage of times that our algorithms converge to the revenue-maximizing solution, as function of the number of items in the auction.\n5.\nTEST AND COMPARISON We have implemented both algorithms and performed a series of experiments in order to determine how their solution compares to the revenue-maximizing solution and how their times compare with each other.\nIn order to do our tests we had to generate value functions for the agents1 .\nThe algorithm we used is shown in Figure 5.\nThe type of valuations it generates correspond to domains where a set of agents must perform a set of tasks but there are cost savings for particular agents if they can bundle together certain subsets of tasks.\nFor example, imagine a set of robots which must pick up and deliver items to different locations.\nSince each robot is at a different location and has different abilities, each one will have different preferences over how to bundle.\nTheir costs for the item bundles are subadditive, which means that their preferences are superadditive.\nThe first experiment we performed simply ensured the proper 1 Note that we could not use CATS [6] because it generates sets of bids for an indeterminate number of agents.\nIt is as if you were told the set of bids placed in a combinatorial auction but not who placed each bid or even how many people placed bids, and then asked to determine the value function of every participant in the auction.\n0 20 40 60 80 100 2 3 4 5 6 7 8 9 10 Number of Items CachedPauseBid 3 3 3 3 3 3 3 3 3 3 PauseBid + + + + + + + + + + Figure 7: Average percentage of revenue from our algorithms relative to maximum revenue (y-axis) as function of the number of items in the auction.\nfunctioning of our algorithms.\nWe then compared the solutions found by both of them to the revenue-maximizing solution as found by CASS when given a set of bids that corresponds to the agents'' true valuation.\nThat is, for each agent i and each set of items S for which vi(S) > 0 we generated a bid.\nThis set of bids was fed to CASS which implements a centralized winner determination algorithm to find the solution which maximizes revenue.\nNote, however, that the revenue from the PAUSE auction on all the auctions is always smaller than the revenue of the revenue-maximizing solution when the agents bid their true valuations.\nSince PAUSE uses English auctions the final prices (roughly) represent the second-highest valuation, plus , for that set of items.\nWe fixed the number of agents to be 5 and we experimented with different number of items, namely from 2 to 10.\nWe ran both algorithms 100 times for each combination.\nWhen we compared the solutions of our algorithms to the revenue-maximizing solution, we realized that they do not always find the same distribution of items as the revenue-maximizing solution (as shown in Figure 6).\nThe cases where our algorithms failed to arrive at the distribution of the revenue-maximizing solution are those where there was a large gap between the first and second valuation for a set (or sets) of items.\nIf the revenue-maximizing solution contains the bid (or bids) using these higher valuation then it is impossible for the PAUSE auction to find this solution because that bid (those bids) is never placed.\nFor example, if agent i has vi(1) = 1000 and the second highest valuation for (1) is only 10 then i only needs to place a bid of 11 in order to win that item.\nIf the revenue-maximizing solution requires that 1 be sold for 1000 then that solution will never be found because that bid will never be placed.\nWe also found that average percentage of times that our algorithms converges to the revenue-maximizing solution decreases as the number of items increases.\nFor 2 items is almost 100% but decreases a little bit less than 1 percent as the items increase, so that this average percentage of convergence is around 90% for 10 items.\nIn a few instances our algorithms find different solutions this is due to the different The Sixth Intl..\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07) 699 1 10 100 1000 10000 2 3 4 5 6 7 8 9 10 Number of Items CachedPauseBid 3 3 3 3 3 3 3 3 3 PauseBid + + + + + + + + + + Figure 8: Average number of expanded nodes (y-axis) as function of items in the auction.\nordering of the bids in the bids list which makes them search in different order.\nWe know that the revenue generated by the PAUSE auction is generally lower than the revenue of the revenuemaximizing solution, but how much lower?\nTo answer this question we calculated percentage representing the proportion of the revenue given by our algorithms relative to the revenue given by CASS.\nWe found that the percentage of revenue of our algorithms increases in average 2.7% as the number of items increases, as shown in Figure 7.\nHowever, we found that cachedpausebid generates a higher revenue than pausebid (4.3% higher in average) except for auctions with 2 items where both have about the same percentage.\nAgain, this difference is produced by the order of the search.\nIn the case of 2 items both algorithms produce in average a revenue proportion of 67.4%, while in the other extreme (10 items), cachedpausebid produced in average a revenue proportion of 91.5% while pausebid produced in average a revenue proportion of 87.7%.\nThe scalability of our algorithms can be determined by counting the number of nodes expanded in the search tree.\nFor this we count the number of times that pbsearch gets invoked for each time that pausebid is called and the number of times that fastpausebidsearch gets invoked for each time that cachedpausebid, respectively for each of our algorithms.\nAs expected since this is an NP-Hard problem, the number of expanded nodes does grow exponentially with the number of items (as shown in Figure 8).\nHowever, we found that cachedpausebid outperforms pausebid, since it expands in average less than half the number of nodes.\nFor example, the average number of nodes expanded when 2 items is zero for cachedpausebid while for pausebid is 2; and in the other extreme (10 items) cachedpausebid expands in average only 633 nodes while pausebid expands in average 1672 nodes, a difference of more than 1000 nodes.\nAlthough the number of nodes expanded by our algorithms increases as function of the number of items, the actual number of nodes is a much smaller than the worst-case scenario of nn where n is the number of items.\nFor example, for 10 items we expand slightly more than 103 nodes for the case of pausebid and less than that for the case of cachedpause0.1 1 10 100 1000 2 3 4 5 6 7 8 9 10 Number of Items CachedPauseBid 3 3 3 3 3 3 3 3 3 3 PauseBid + + + + + + + + + + Figure 9: Average time in seconds that takes to finish an auction (y-axis) as function of the number of items in the auction.\nbid which are much smaller numbers than 1010 .\nNotice also that our value generation algorithm (Figure 5) generates a number of bids that is exponential on the number of items, as might be expected in many situations.\nAs such, these results do not support the conclusion that time grows exponentially with the number of items when the number of bids is independent of the number of items.\nWe expect that both algorithms will grow exponentially as a function the number of bids, but stay roughly constant as the number of items grows.\nWe wanted to make sure that less expanded nodes does indeed correspond to faster execution, especially since our algorithms execute different operations.\nWe thus ran the same experiment with all the agents in the same machine, an Intel Centrino 2.0 GHz laptop PC with 1 GB of RAM and a 7200 RMP 60 GB hard drive, and calculated the average time that takes to finish an auction for each algorithm.\nAs shown in Figure 9, cachedpausebid is faster than pausebid, the difference in execution speed is even more clear as the number of items increases.\n6.\nRELATED WORK A lot of research has been done on various aspects of combinatorial auctions.\nWe recommend [2] for a good review.\nHowever, the study of distributed winner determination algorithms for combinatorial auctions is still relatively new.\nOne approach is given by the algorithms for distributing the winner determination problem in combinatorial auctions presented in [7], but these algorithms assume the computational entities are the items being sold and thus end up with a different type of distribution.\nThe VSA algorithm [3] is another way of performing distributed winner determination in combinatorial auction but it assumes the bids themselves perform the computation.\nThis algorithm also fails to converge to a solution for most cases.\nIn [9] the authors present a distributed mechanism for calculating VCG payments in a mechanism design problem.\nTheir mechanism roughly amounts to having each agent calculate the payments for two other agents and give these to a secure 700 The Sixth Intl..\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07) central server which then checks to make sure results from all pairs agree, otherwise a re-calculation is ordered.\nThis general idea, which they call the redundancy principle, could also be applied to our problem but it requires the existence of a secure center agent that everyone trusts.\nAnother interesting approach is given in [8] where the bidding agents prioritize their bids, thus reducing the set of bids that the centralized winner determination algorithm must consider, making that problem easier.\nFinally, in the computation procuring clock auction [1] the agents are given an everincreasing percentage of the surplus achieved by their proposed solution over the current best.\nAs such, it assumes the agents are impartial computational entities, not the set of possible buyers as assumed by the PAUSE auction.\n7.\nCONCLUSIONS We believe that distributed solutions to the winner determination problem should be studied as they offer a better fit for some applications as when, for example, agents do not want to reveal their valuations to the auctioneer or when we wish to distribute the computational load among the bidders.\nThe PAUSE auction is one of a few approaches to decentralize the winner determination problem in combinatorial auctions.\nWith this auction, we can even envision completely eliminating the auctioneer and, instead, have every agent performe the task of the auctioneer.\nHowever, while PAUSE establishes the rules the bidders must obey, it does not tell us how the bidders should calculate their bids.\nWe have presented two algorithms, pausebid and cachedpausebid, that bidder agents can use to engage in a PAUSE auction.\nBoth algorithms implement a myopic utility maximizing strategy that is guaranteed to find the bidset that maximizes the agent``s utility given the set of outstanding best bids at any given time, without considering possible future bids.\nBoth algorithms find, most of the time, the same distribution of items as the revenue-maximizing solution.\nThe cases where our algorithms failed to arrive at that distribution are those where there was a large gap between the first and second valuation for a set (or sets) of items.\nAs it is an NP-Hard problem, the running time of our algorithms remains exponential but it is significantly better than a full search.\npausebid performs a branch and bound search completely from scratch each time it is invoked.\ncachedpausebid caches partial solutions and performs a branch and bound search only on the few portions affected by the changes on the bids between consecutive times.\ncachedpausebid has a better performance since it explores fewer nodes (less than half) and it is faster.\nAs expected the revenue generated by a PAUSE auction is lower than the revenue of a revenue-maximizing solution found by a centralized winner determination algorithm, however we found that cachedpausebid generates in average 4.7% higher revenue than pausebid.\nWe also found that the revenue generated by our algorithms increases as function of the number of items in the auction.\nOur algorithms have shown that it is feasible to implement the complex coordination constraints supported by combinatorial auctions without having to resort to a centralized winner determination algorithm.\nMoreover, because of the design of the PAUSE auction, the agents in the auction also have an incentive to perform the required computation.\nOur bidding algorithms can be used by any multiagent system that would use combinatorial auctions for coordination but would rather not implement a centralized auctioneer.\n8.\nREFERENCES [1] P. J. Brewer.\nDecentralized computation procurement and computational robustness in a smart market.\nEconomic Theory, 13(1):41-92, January 1999.\n[2] P. Cramton, Y. Shoham, and R. Steinberg, editors.\nCombinatorial Auctions.\nMIT Press, 2006.\n[3] Y. Fujishima, K. Leyton-Brown, and Y. Shoham.\nTaming the computational complexity of combinatorial auctions: Optimal and approximate approaches.\nIn Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 548-553.\nMorgan Kaufmann Publishers Inc., 1999.\n[4] F. Kelly and R. Stenberg.\nA combinatorial auction with multiple winners for universal service.\nManagement Science, 46(4):586-596, 2000.\n[5] A. Land, S. Powell, and R. Steinberg.\nPAUSE: A computationally tractable combinatorial auction.\nIn Cramton et al. [2], chapter 6, pages 139-157.\n[6] K. Leyton-Brown, M. Pearson, and Y. Shoham.\nTowards a universal test suite for combinatorial auction algorithms.\nIn Proceedings of the 2nd ACM conference on Electronic commerce, pages 66-76.\nACM Press, 2000.\nhttp://cats.stanford.edu.\n[7] M. V. Narumanchi and J. M. Vidal.\nAlgorithms for distributed winner determination in combinatorial auctions.\nIn LNAI volume of AMEC/TADA.\nSpringer, 2006.\n[8] S. Park and M. H. Rothkopf.\nAuctions with endogenously determined allowable combinations.\nTechnical report, Rutgets Center for Operations Research, January 2001.\nRRR 3-2001.\n[9] D. C. Parkes and J. Shneidman.\nDistributed implementations of vickrey-clarke-groves auctions.\nIn Proceedings of the Third International Joint Conference on Autonomous Agents and MultiAgent Systems, pages 261-268.\nACM, 2004.\n[10] M. H. Rothkopf, A. Pekec, and R. M. Harstad.\nComputationally manageable combinational auctions.\nManagement Science, 44(8):1131-1147, 1998.\n[11] T. Sandholm.\nAn algorithm for winner determination in combinatorial auctions.\nArtificial Intelligence, 135(1-2):1-54, February 2002.\n[12] T. Sandholm, S. Suri, A. Gilpin, and D. Levine.\nCABOB: a fast optimal algorithm for winner determination in combinatorial auctions.\nManagement Science, 51(3):374-391, 2005.\nThe Sixth Intl..\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07) 701"},"lvl-2":{"kind":"string","value":"Bidding Algorithms for a Distributed Combinatorial Auction\nABSTRACT\nDistributed allocation and multiagent coordination problems can be solved through combinatorial auctions.\nHowever, most of the existing winner determination algorithms for combinatorial auctions are centralized.\nThe PAUSE auction is one of a few efforts to release the auctioneer from having to do all the work (it might even be possible to get rid of the auctioneer).\nIt is an increasing price combinatorial auction that naturally distributes the problem of winner determination amongst the bidders in such a way that they have an incentive to perform the calculation.\nIt can be used when we wish to distribute the computational load among the bidders or when the bidders do not wish to reveal their true valuations unless necessary.\nPAUSE establishes the rules the bidders must obey.\nHowever, it does not tell us how the bidders should calculate their bids.\nWe have developed a couple of bidding algorithms for the bidders in a PAUSE auction.\nOur algorithms always return the set of bids that maximizes the bidder's utility.\nSince the problem is NP-Hard, run time remains exponential on the number of items, but it is remarkably better than an exhaustive search.\nIn this paper we present our bidding algorithms, discuss their virtues and drawbacks, and compare the solutions obtained by them to the revenue-maximizing solution found by a centralized winner determination algorithm.\n1.\nINTRODUCTION\nBoth the research and practice of combinatorial auctions have grown rapidly in the past ten years.\nIn a combinatorial auction bidders can place bids on combinations of items, called packages or bidsets, rather than just individual items.\nOnce the bidders place their bids, it is necessary to find the allocation of items to bidders that maximizes the auctioneer's revenue.\nThis problem, known as the winner determination problem, is a combinatorial optimization problem and is NP-Hard [10].\nNevertheless, several algorithms that have a satisfactory performance for problem sizes and structures occurring in practice have been developed.\nThe practical applications of combinatorial auctions include: allocation of airport takeoff and landing time slots, procurement of freight transportation services, procurement of public transport services, and industrial procurement [2].\nBecause of their wide applicability, one cannot hope for a general-purpose winner determination algorithm that can efficiently solve every instance of the problem.\nThus, several approaches and algorithms have been proposed to address the winner determination problem.\nHowever, most of the existing winner determination algorithms for combinatorial auctions are centralized, meaning that they require all agents to send their bids to a centralized auctioneer who then determines the winners.\nExamples of these algorithms are CASS [3], Bidtree [11] and CABOB [12].\nWe believe that distributed solutions to the winner determination problem should be studied as they offer a better fit for some applications as when, for example, agents do not want to reveal their valuations to the auctioneer.\nThe PAUSE (Progressive Adaptive User Selection Environment) auction [4, 5] is one of a few efforts to distribute the problem of winner determination amongst the bidders.\nPAUSE establishes the rules the participants have to adhere to so that the work is distributed amongst them.\nHowever, it is not concerned with how the bidders determine what they should bid.\nIn this paper we present two algorithms, PAUSEBID and CACHEDPAUSEBID, which enable agents in a PAUSE auction to find the bidset that maximizes their utility.\nOur algorithms implement a myopic utility maximizing strategy and are guaranteed to find the bidset that maximizes the agent's utility given the outstanding best bids at a given time.\nPAUSEBID performs a branch and bound search completely from scratch every time that it is called.\nCACHEDPAUSEBID is a caching-based algorithm which explores fewer nodes, since it caches some solutions.\n2.\nTHE PAUSE AUCTION\nA PAUSE auction for m items has m stages.\nStage 1 consists of having simultaneous ascending price open-cry auctions and during this stage the bidders can only place bids on individual items.\nAt the end of this state we will know what the highest bid for each individual item is and who placed that bid.\nEach successive stage k = 2, 3,..., m consists of an ascending price auction where the bidders must submit bidsets that cover all items but each one of the bids must be for k items or less.\nThe bidders are allowed to use bids that other agents have placed in previous rounds when building their bidsets, thus allowing them to find better solutions.\nAlso, any new bidset has to have a sum of bid prices which is bigger than that of the currently winning bidset.\nAt the end of each stage k all agents know the best bid for every subset of size k or less.\nAlso, at any point in time after stage 1 has ended there is a standing bidset whose value increases monotonically as new bidsets are submitted.\nSince in the final round all agents consider all possible bidsets, we know that the final winning bidset will be one such that no agent can propose a better bidset.\nNote, however, that this bidset is not guaranteed to be the one that maximizes revenue since we are using an ascending price auction so the winning bid for each set will be only slightly bigger than the second highest bid for the particular set of items.\nThat is, the final prices will not be the same as the prices in a traditional combinatorial auction where all the bidders bid their true valuation.\nHowever, there remains the open question of whether the final distribution of items to bidders found in a PAUSE auction is the same as the revenue maximizing solution.\nOur test results provide an answer to this question.\nThe PAUSE auction makes the job of the auctioneer very easy.\nAll it has to do is to make sure that each new bidset has a revenue bigger than the current winning bidset, as well as make sure that every bid in an agent's bidset that is not his does indeed correspond to some other agents' previous bid.\nThe computational problem shifts from one of winner determination to one of bid generation.\nEach agent must search over the space of all bidsets which contain at least one of its bids.\nThe search is made easier by the fact that the agent needs to consider only the current best bids and only wants bidsets where its own utility is higher than in the current winning bidset.\nEach agent also has a clear incentive for performing this computation, namely, its utility only increases with each bidset it proposes (of course, it might decrease with the bidsets that others propose).\nFinally, the PAUSE auction has been shown to be envy-free in that at the conclusion of the auction no bidder would prefer to exchange his allocation with that of any other bidder [2].\nWe can even envision completely eliminating the auctioneer and, instead, have every agent perform the task of the auctioneer.\nThat is, all bids are broadcast and when an agent receives a bid from another agent it updates the set of best bids and determines if the new bid is indeed better than the current winning bid.\nThe agents would have an incentive to perform their computation as it will increase their expected utility.\nAlso, any lies about other agents' bids are easily found out by keeping track of the bids sent out by every agent (the set of best bids).\nNamely, the only one that can increase an agent's bid value is the agent itself.\nAnyone claiming a higher value for some other agent is lying.\nThe only thing missing is an algorithm that calculates the utility-maximizing bidset for each agent.\n3.\nPROBLEM FORMULATION\nA bid b is composed of three elements bitems (the set of items the bid is over), bagent (the agent that placed the bid), and bvalue (the value or price of the bid).\nThe agents maintain a set B of the current best bids, one for each set of items of size <k, where k is the current stage.\nAt any point in the auction, after the first round, there will also be a set W C B of currently winning bids.\nThis is the set of bids that covers all the items and currently maximizes the revenue, where the revenue of W is given by\nAgent i's value function is given by vi (S) E R where S is a set of items.\nGiven an agent's value function and the current winning bidset W we can calculate the agent's utility from W as\nThat is, the agent's utility for a bidset W is the value it receives for the items it wins in W minus the price it must pay for those items.\nIf the agent is not winning any items then its utility is zero.\nThe goal of the bidding agents in the PAUSE auction is to maximize their utility, subject to the constraint that their next set of bids must have a total revenue that is at least E bigger than the current revenue, where E is the smallest increment allowed in the auction.\nFormally, given that W is the current winning bidset, agent i must find a g ∗ i such that r (g ∗ i)> r (W) + E and\nwhere each g is a set of bids that covers all items and db ∈ g (b E B) or (bagent = i and bvalue> B (bitems) and size (bitems) <k), and where B (items) is the value of the bid in B for the set items (if there is no bid for those items it returns zero).\nThat is, each bid b in g must satisfy at least one of the two following conditions.\n1) b is already in B, 2) b is a bid of size <k in which the agent i bids higher than the price for the same items in B.\n4.\nBIDDING ALGORITHMS\nAccording to the PAUSE auction, during the first stage we have only several English auctions, with the bidders submitting bids on individual items.\nIn this case, an agent's dominant strategy is to bid E higher than the current winning bid until it reaches its valuation for that particular item.\nOur algorithms focus on the subsequent stages: k> 1.\nWhen k> 1, agents have to find g ∗ i.\nThis can be done by performing a complete search on B. However, this approach is computationally expensive since it produces a large search tree.\nOur algorithms represent alternative approaches to overcome this expensive search.\n4.1 The PAUSEBID Algorithm\nIn the PAUSEBID algorithm (shown in Figure 1) we implement some heuristics to prune the search tree.\nGiven that bidders want to maximize their utility and that at any given point there are likely only a few bids within B which\nFigure 1: The PAUSEBID algorithm which implements a branch and bound search.\ni is the agent and k is the current stage of the auction, for k> 2.\nthe agent can dominate, we start by defining my-bids to be the list of bids for which the agent's valuation is higher than the current best bid, as given in B.\nWe set the value of these bids to be the agent's true valuation (but we won't necessarily be bidding true valuation, as we explain later).\nSimilarly, we set their-bids to be the rest of the bids from B. Finally, the agent's search list is simply the concatenation of my-bids and their-bids.\nNote that the agent's own bids are placed first on the search list as this will enable us to do more pruning (PAUSEBID lines 3 to 9).\nThe agent can now perform a branch and bound search on the branch-on-bids tree produced by these bids.\nThis branch and bound search is implemented by PBSEARCH (Figure 2).\nOur algorithm not only implements the standard bound but it also implements other pruning techniques in order to further reduce the size of the search tree.\nThe bound we use is the maximum utility that the agent can expect to receive from a given set of bids.\nWe call it u ∗.\nInitially, u ∗ is set to ui (W) (PAUSEBID line 11) since that is the utility the agent currently receives and any solution he proposes should give him more utility.\nIf PBSEARCH ever comes across a partial solution where the maximum utility the agent can expect to receive is less than u ∗ then that subtree is pruned (PBSEARCH line 21).\nNote that we can determine the maximum utility only after the algorithm has searched over all of the agent's own bids (which are first on the list) because after that we know that the solution will not include any more bids where the agent is the winner thus the agent's utility will no longer increase.\nFor example,\nFigure 2: The PBSEARCH recursive procedure where bids is the set of available bids and g is the current partial solution.\nif an agent has only one bid in my-bids then the maximum utility he can expect is equal to his value for the items in that bid minus the minimum possible payment we can make for those items and still come up with a set of bids that has revenue greater than r (W).\nThe calculation of the minimum payment is shown in line 19 for the partial solution case and line 9 for the case where we have a complete solution in PBSEARCH.\nNote that in order to calculate the min-payment for the partial solution case we need an upper bound on the payments that we must make for each item.\nThis upper bound is provided by\nThis function produces a bound identical to the one used by the Bidtree algorithm--it merely assigns to each individual item in S a value equal to the maximum bid in B divided by the number of items in that bid.\nTo prune the branches that cannot lead to a solution with revenue greater than the current W, the algorithm considers both the values of the bids in B and the valuations of the\n696 The Sixth Intl. .\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07)\nwhich assigns to each individual item s in S the maximum value produced by the valuation of S ~ divided by the size of S ~, where S ~ is a set for which the agent has a valuation greater than zero, contains s, and its size is less or equal than k.\nThe algorithm uses the heuristics h and hi (lines 15 and 19 of PBSEARCH), to prune the just mentioned branches in the same way an A ∗ algorithm uses its heuristic.\nA final pruning technique implemented by the algorithm is ignoring any branches where the agent has no bids in the current answer g and no more of the agent's bids are in the list (PBSEARCH lines 6 and 7).\nThe resulting g ∗ found by PBSEARCH is thus the set of bids that has revenue bigger than r (W) and maximizes agent i's utility.\nHowever, agent i's bids in g ∗ are still set to his own valuation and not to the lowest possible price.\nLines 17 to 20 in PAUSEBID are responsible for setting the agent's payments so that it can achieve its maximum utility u ∗.\nIf the agent has only one bid in g ∗ then it is simply a matter of reducing the payment of that bid by u ∗ from the current maximum of the agent's true valuation.\nHowever, if the agent has more than one bid then we face the problem of how to distribute the agent's payments among these bids.\nThere are many ways of distributing the payments and there does not appear to be a dominant strategy for performing this distribution.\nWe have chosen to distribute the payments in proportion to the agent's true valuation for each set of items.\nPAUSEBID assumes that the set of best bids B and the current best winning bidset W remains constant during its execution, and it returns the agent's myopic utility-maximizing bidset (if there is one) using a branch and bound search.\nHowever it repeats the whole search at every stage.\nWe can minimize this problem by caching the result of previous searches.\n4.2 The CACHEDPAUSEBID Algorithm\nThe CACHEDPAUSEBID algorithm (shown in Figure 3) is our second approach to solve the bidding problem in the PAUSE auction.\nIt is based in a cache table called C-Table where we store some solutions to avoid doing a complete search every time.\nThe problem is the same; the agent i has to find g ∗ i.\nWe note that g ∗ i is a bidset that contains at least one bid of the agent i. Let S be a set of items for which the agent i has a valuation such that vi (S) ≥ B (S)> 0, let gSi be a bidset over S such that r (gSi) ≥ r (W) + e and\nwhere each g is a set of bids that covers all items and ∀ b ∈ g (b ∈ B) or (bagent = i and bvalue> B (bitems)) and (∃ b ∈ gbitems = S and bagent = i).\nThat is, gSi is i's best bidset for all items which includes a bid from i for all S items.\nIn the PAUSE auction we cannot bid for sets of items with size greater than k. So, if we have for each set of items S for which vi (S)> 0 and size (S) ≤ k its corresponding gSi then g ∗ i is the gSi that maximizes the agent's utility.\nThat is\nFigure 3: The CACHEDPAUSEBID algorithm that imple\nments a caching based search to find a bidset that maximizes the utility for the agent i. k is the current stage of the auction (for k ≥ 2), and k-changed is a boolean that is true right after the auction moved to the next stage.\nFigure 4: The CPBSEARCH recursive procedure where bids is the set of available bids, g is the current partial solution and n is a value that indicates how deep in the list bids the algorithm has to search.\nthen find g' by searching for the gS, stored in C-Table [S], that maximizes the agent's utility, considering only the set of items S with size (S) <k.\nThe problem remains in maintaining the C-Table updated and avoiding to search every gS every time.\nCACHEDPAUSEBID deals with this and other details.\nLet B' be the set of bids that contains the new best bids, that is, B' contains the bids recently added to B and the bids that have changed price (always higher), bidder, or both and were already in B. Let S ¯ = Items--S be the complement of S (the set of items not included in S).\nCACHEDPAUSEBID takes three parameters: i the agent, k the current stage of the auction, and k-changed a boolean that is true right after the auction moved to the next stage.\nInitially C-Table has one row or entry for each set S for which vi (S)> 0.\nWe start by eliminating the entries corresponding to each set S for which vi (S) <B (S) from C-Table (line 3).\nThen, in the case that k-changed is true, for each set S with size (S) = k, we add to B' a bid for that set with value equal to vi (S) and bidder agent i (line 5); this a bid that the agent is now allowed to consider.\nWe then search for g' amongst the gS stored in C-Table, for this we only need to consider the sets with size (S) <k (line 8).\nBut how do we know that the gS in C-Table [S] is still the best solution for S?\nThere are only two cases when we are not sure about that and we need to do a search to update C-Table [S].\nThese cases are: i) When k-changed is true and size (S) <k, since there was no gS stored in C-Table for this S. ii) When there exists at least one bid in B' for the set of items submitted by an agent different than i, since it is probable that this new bid can produce a solution better than the one stored in C-Table [S].\nWe handle the two cases mentioned above in lines 13 to 26 of CACHEDPAUSEBID.\nIn both of these cases, since gS must contain a bid for S we need to find a bidset that cover the missing items, that is ¯ S. Thus, our search space consists of all the bids on B for the set of items S ¯ or for a subset of it.\nWe build the list bids that contains only those bids.\nHowever, we put the bids from B' at the beginning of bids (line 14) since they are the ones that have changed.\nThen, we replace the bids in bids that have a price lower than the valuation the agent i has for those same items with a bid from agent i for those items and value equal to the agent's valuation (lines 16--19).\nThe recursive procedure CPBSEARCH, called in line 25 of CACHEDPAUSEBID and shown in Figure 4, is the one that finds the new gS.\nCPBSEARCH is a slightly modified version of our branch and bound search implemented in PBSEARCH.\nThe first modification is that it has a third parameter n that indicates how deep on the list bids we want to search, since it stops searching when n less or equal to zero and not only when the list bids is empty (line 1).\nEach time that there is a recursive call of CPBSEARCH n is decreased by one when a bid from bids is discarded or out (lines 12, 15, 21, and 24) and n remains the same otherwise (lines 20 and 23).\nWe set the value of n before calling CPBSEARCH, to be the size of the list bids (CACHEDPAUSEBID line 21) in case i), since we want CPBSEARCH to search over all bids; and we set n to be the number of bids from B' included in bids (CACHEDPAUSEBID line 23) in case ii), since we know that only the those first n bids in bids changed and can affect our current gS.\nAnother difference with PBSEARCH is that the bound in CPBSEARCH is uS which we set to be 0 (CACHEDPAUSEBID line 22) when in case i) and r (gS)--min-payment (CACHEDPAUSEBID line 12) when in case ii).\nWe call CPBSEARCH with g already containing a bid for S.\nAfter CPBSEARCH is executed we are sure that we have the right gS, so we store it in the corresponding C-Table [S] (CACHEDPAUSEBID line 26).\nWhen we reach line 27 in CACHEDPAUSEBID, we are sure that we have the right gS.\nHowever, agent i's bids in gS are still set to his own valuation and not to the lowest possible price.\nIf uS is greater than the current u', lines 31 to 34 in CACHEDPAUSEBID are responsible for setting the agent's payments so that it can achieve its maximum utility uS.\nAs in PAUSEBID, we have chosen to distribute the payments in proportion to the agent's true valuation for each set of items.\nIn the case that uS less than or equal to zero and the valuation that the agent i has for the set of items S is lower than the current value of the bid in B for the same set of items, we remove the corresponding C-Table [S] since we know that is not worthwhile to keep it in the cache table (CACHEDPAUSEBID line 38).\nThe CACHEDPAUSEBID function is called when k> 1 and returns the agent's myopic utility-maximizing bidset, if there is one.\nIt assumes that W and B remains constant during its execution.\nFigure 5: Algorithm for the generation of random value functions.\nEXPD (x) returns a random number taken from an exponential distribution with mean 1/x.\nFigure 6: Average percentage of convergence (y-axis), which is the percentage of times that our algorithms converge to the revenue-maximizing solution, as function of the number of items in the auction.\n5.\nTEST AND COMPARISON\nWe have implemented both algorithms and performed a series of experiments in order to determine how their solution compares to the revenue-maximizing solution and how their times compare with each other.\nIn order to do our tests we had to generate value functions for the agents1.\nThe algorithm we used is shown in Figure 5.\nThe type of valuations it generates correspond to domains where a set of agents must perform a set of tasks but there are cost savings for particular agents if they can bundle together certain subsets of tasks.\nFor example, imagine a set of robots which must pick up and deliver items to different locations.\nSince each robot is at a different location and has different abilities, each one will have different preferences over how to bundle.\nTheir costs for the item bundles are subadditive, which means that their preferences are superadditive.\nThe first experiment we performed simply ensured the proper 1Note that we could not use CATS [6] because it generates sets of bids for an indeterminate number of agents.\nIt is as if you were told the set of bids placed in a combinatorial auction but not who placed each bid or even how many people placed bids, and then asked to determine the value function of every participant in the auction.\nFigure 7: Average percentage of revenue from our algorithms relative to maximum revenue (y-axis) as function of the number of items in the auction.\nfunctioning of our algorithms.\nWe then compared the solutions found by both of them to the revenue-maximizing solution as found by CASS when given a set of bids that corresponds to the agents' true valuation.\nThat is, for each agent i and each set of items S for which vi (S)> 0 we generated a bid.\nThis set of bids was fed to CASS which implements a centralized winner determination algorithm to find the solution which maximizes revenue.\nNote, however, that the revenue from the PAUSE auction on all the auctions is always smaller than the revenue of the revenue-maximizing solution when the agents bid their true valuations.\nSince PAUSE uses English auctions the final prices (roughly) represent the second-highest valuation, plus E, for that set of items.\nWe fixed the number of agents to be 5 and we experimented with different number of items, namely from 2 to 10.\nWe ran both algorithms 100 times for each combination.\nWhen we compared the solutions of our algorithms to the revenue-maximizing solution, we realized that they do not always find the same distribution of items as the revenue-maximizing solution (as shown in Figure 6).\nThe cases where our algorithms failed to arrive at the distribution of the revenue-maximizing solution are those where there was a large gap between the first and second valuation for a set (or sets) of items.\nIf the revenue-maximizing solution contains the bid (or bids) using these higher valuation then it is impossible for the PAUSE auction to find this solution because that bid (those bids) is never placed.\nFor example, if agent i has vi (1) = 1000 and the second highest valuation for (1) is only 10 then i only needs to place a bid of 11 in order to win that item.\nIf the revenue-maximizing solution requires that 1 be sold for 1000 then that solution will never be found because that bid will never be placed.\nWe also found that average percentage of times that our algorithms converges to the revenue-maximizing solution decreases as the number of items increases.\nFor 2 items is almost 100% but decreases a little bit less than 1 percent as the items increase, so that this average percentage of convergence is around 90% for 10 items.\nIn a few instances our algorithms find different solutions this is due to the different\nFigure 8: Average number of expanded nodes (y-axis) as function of items in the auction.\nordering of the bids in the bids list which makes them search in different order.\nWe know that the revenue generated by the PAUSE auction is generally lower than the revenue of the revenuemaximizing solution, but how much lower?\nTo answer this question we calculated percentage representing the proportion of the revenue given by our algorithms relative to the revenue given by CASS.\nWe found that the percentage of revenue of our algorithms increases in average 2.7% as the number of items increases, as shown in Figure 7.\nHowever, we found that CACHEDPAUSEBID generates a higher revenue than PAUSEBID (4.3% higher in average) except for auctions with 2 items where both have about the same percentage.\nAgain, this difference is produced by the order of the search.\nIn the case of 2 items both algorithms produce in average a revenue proportion of 67.4%, while in the other extreme (10 items), CACHEDPAUSEBID produced in average a revenue proportion of 91.5% while PAUSEBID produced in average a revenue proportion of 87.7%.\nThe scalability of our algorithms can be determined by counting the number of nodes expanded in the search tree.\nFor this we count the number of times that PBSEARCH gets invoked for each time that PAUSEBID is called and the number of times that FASTPAUSEBIDSEARCH gets invoked for each time that CACHEDPAUSEBID, respectively for each of our algorithms.\nAs expected since this is an NP-Hard problem, the number of expanded nodes does grow exponentially with the number of items (as shown in Figure 8).\nHowever, we found that CACHEDPAUSEBID outperforms PAUSEBID, since it expands in average less than half the number of nodes.\nFor example, the average number of nodes expanded when 2 items is zero for CACHEDPAUSEBID while for PAUSEBID is 2; and in the other extreme (10 items) CACHEDPAUSEBID expands in average only 633 nodes while PAUSEBID expands in average 1672 nodes, a difference of more than 1000 nodes.\nAlthough the number of nodes expanded by our algorithms increases as function of the number of items, the actual number of nodes is a much smaller than the worst-case scenario of nn where n is the number of items.\nFor example, for 10 items we expand slightly more than 103 nodes for the case of PAUSEBID and less than that for the case of CACHEDPAUSE2 3 4 5 6 7 8 9 10 Number of Items\nFigure 9: Average time in seconds that takes to finish an auction (y-axis) as function of the number of items in the auction.\nBID which are much smaller numbers than 1010.\nNotice also that our value generation algorithm (Figure 5) generates a number of bids that is exponential on the number of items, as might be expected in many situations.\nAs such, these results do not support the conclusion that time grows exponentially with the number of items when the number of bids is independent of the number of items.\nWe expect that both algorithms will grow exponentially as a function the number of bids, but stay roughly constant as the number of items grows.\nWe wanted to make sure that less expanded nodes does indeed correspond to faster execution, especially since our algorithms execute different operations.\nWe thus ran the same experiment with all the agents in the same machine, an Intel Centrino 2.0 GHz laptop PC with 1 GB of RAM and a 7200 RMP 60 GB hard drive, and calculated the average time that takes to finish an auction for each algorithm.\nAs shown in Figure 9, CACHEDPAUSEBID is faster than PAUSEBID, the difference in execution speed is even more clear as the number of items increases.\n6.\nRELATED WORK\nA lot of research has been done on various aspects of combinatorial auctions.\nWe recommend [2] for a good review.\nHowever, the study of distributed winner determination algorithms for combinatorial auctions is still relatively new.\nOne approach is given by the algorithms for distributing the winner determination problem in combinatorial auctions presented in [7], but these algorithms assume the computational entities are the items being sold and thus end up with a different type of distribution.\nThe VSA algorithm [3] is another way of performing distributed winner determination in combinatorial auction but it assumes the bids themselves perform the computation.\nThis algorithm also fails to converge to a solution for most cases.\nIn [9] the authors present a distributed mechanism for calculating VCG payments in a mechanism design problem.\nTheir mechanism roughly amounts to having each agent calculate the payments for two other agents and give these to a secure\n700 The Sixth Intl. .\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07)\ncentral server which then checks to make sure results from all pairs agree, otherwise a re-calculation is ordered.\nThis general idea, which they call the redundancy principle, could also be applied to our problem but it requires the existence of a secure center agent that everyone trusts.\nAnother interesting approach is given in [8] where the bidding agents prioritize their bids, thus reducing the set of bids that the centralized winner determination algorithm must consider, making that problem easier.\nFinally, in the computation procuring clock auction [1] the agents are given an everincreasing percentage of the surplus achieved by their proposed solution over the current best.\nAs such, it assumes the agents are impartial computational entities, not the set of possible buyers as assumed by the PAUSE auction.\n7.\nCONCLUSIONS\nWe believe that distributed solutions to the winner determination problem should be studied as they offer a better fit for some applications as when, for example, agents do not want to reveal their valuations to the auctioneer or when we wish to distribute the computational load among the bidders.\nThe PAUSE auction is one of a few approaches to decentralize the winner determination problem in combinatorial auctions.\nWith this auction, we can even envision completely eliminating the auctioneer and, instead, have every agent performe the task of the auctioneer.\nHowever, while PAUSE establishes the rules the bidders must obey, it does not tell us how the bidders should calculate their bids.\nWe have presented two algorithms, PAUSEBID and CACHEDPAUSEBID, that bidder agents can use to engage in a PAUSE auction.\nBoth algorithms implement a myopic utility maximizing strategy that is guaranteed to find the bidset that maximizes the agent's utility given the set of outstanding best bids at any given time, without considering possible future bids.\nBoth algorithms find, most of the time, the same distribution of items as the revenue-maximizing solution.\nThe cases where our algorithms failed to arrive at that distribution are those where there was a large gap between the first and second valuation for a set (or sets) of items.\nAs it is an NP-Hard problem, the running time of our algorithms remains exponential but it is significantly better than a full search.\nPAUSEBID performs a branch and bound search completely from scratch each time it is invoked.\nCACHEDPAUSEBID caches partial solutions and performs a branch and bound search only on the few portions affected by the changes on the bids between consecutive times.\nCACHEDPAUSEBID has a better performance since it explores fewer nodes (less than half) and it is faster.\nAs expected the revenue generated by a PAUSE auction is lower than the revenue of a revenue-maximizing solution found by a centralized winner determination algorithm, however we found that CACHEDPAUSEBID generates in average 4.7% higher revenue than PAUSEBID.\nWe also found that the revenue generated by our algorithms increases as function of the number of items in the auction.\nOur algorithms have shown that it is feasible to implement the complex coordination constraints supported by combinatorial auctions without having to resort to a centralized winner determination algorithm.\nMoreover, because of the design of the PAUSE auction, the agents in the auction also have an incentive to perform the required computation.\nOur bidding algorithms can be used by any multiagent system that would use combinatorial auctions for coordination but would rather not implement a centralized auctioneer."},"lvl-3":{"kind":"string","value":"Bidding Algorithms for a Distributed Combinatorial Auction\nABSTRACT\nDistributed allocation and multiagent coordination problems can be solved through combinatorial auctions.\nHowever, most of the existing winner determination algorithms for combinatorial auctions are centralized.\nThe PAUSE auction is one of a few efforts to release the auctioneer from having to do all the work (it might even be possible to get rid of the auctioneer).\nIt is an increasing price combinatorial auction that naturally distributes the problem of winner determination amongst the bidders in such a way that they have an incentive to perform the calculation.\nIt can be used when we wish to distribute the computational load among the bidders or when the bidders do not wish to reveal their true valuations unless necessary.\nPAUSE establishes the rules the bidders must obey.\nHowever, it does not tell us how the bidders should calculate their bids.\nWe have developed a couple of bidding algorithms for the bidders in a PAUSE auction.\nOur algorithms always return the set of bids that maximizes the bidder's utility.\nSince the problem is NP-Hard, run time remains exponential on the number of items, but it is remarkably better than an exhaustive search.\nIn this paper we present our bidding algorithms, discuss their virtues and drawbacks, and compare the solutions obtained by them to the revenue-maximizing solution found by a centralized winner determination algorithm.\n1.\nINTRODUCTION\nBoth the research and practice of combinatorial auctions have grown rapidly in the past ten years.\nIn a combinatorial auction bidders can place bids on combinations of items, called packages or bidsets, rather than just individual items.\nOnce the bidders place their bids, it is necessary to find the allocation of items to bidders that maximizes the auctioneer's revenue.\nThis problem, known as the winner determination problem, is a combinatorial optimization problem and is NP-Hard [10].\nNevertheless, several algorithms that have a satisfactory performance for problem sizes and structures occurring in practice have been developed.\nThe practical applications of combinatorial auctions include: allocation of airport takeoff and landing time slots, procurement of freight transportation services, procurement of public transport services, and industrial procurement [2].\nBecause of their wide applicability, one cannot hope for a general-purpose winner determination algorithm that can efficiently solve every instance of the problem.\nThus, several approaches and algorithms have been proposed to address the winner determination problem.\nHowever, most of the existing winner determination algorithms for combinatorial auctions are centralized, meaning that they require all agents to send their bids to a centralized auctioneer who then determines the winners.\nExamples of these algorithms are CASS [3], Bidtree [11] and CABOB [12].\nWe believe that distributed solutions to the winner determination problem should be studied as they offer a better fit for some applications as when, for example, agents do not want to reveal their valuations to the auctioneer.\nThe PAUSE (Progressive Adaptive User Selection Environment) auction [4, 5] is one of a few efforts to distribute the problem of winner determination amongst the bidders.\nPAUSE establishes the rules the participants have to adhere to so that the work is distributed amongst them.\nHowever, it is not concerned with how the bidders determine what they should bid.\nIn this paper we present two algorithms, PAUSEBID and CACHEDPAUSEBID, which enable agents in a PAUSE auction to find the bidset that maximizes their utility.\nOur algorithms implement a myopic utility maximizing strategy and are guaranteed to find the bidset that maximizes the agent's utility given the outstanding best bids at a given time.\nPAUSEBID performs a branch and bound search completely from scratch every time that it is called.\nCACHEDPAUSEBID is a caching-based algorithm which explores fewer nodes, since it caches some solutions.\n2.\nTHE PAUSE AUCTION\n3.\nPROBLEM FORMULATION\n4.\nBIDDING ALGORITHMS\n4.1 The PAUSEBID Algorithm\n696 The Sixth Intl. .\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07)\n4.2 The CACHEDPAUSEBID Algorithm\n5.\nTEST AND COMPARISON\n6.\nRELATED WORK\nA lot of research has been done on various aspects of combinatorial auctions.\nWe recommend [2] for a good review.\nHowever, the study of distributed winner determination algorithms for combinatorial auctions is still relatively new.\nOne approach is given by the algorithms for distributing the winner determination problem in combinatorial auctions presented in [7], but these algorithms assume the computational entities are the items being sold and thus end up with a different type of distribution.\nThe VSA algorithm [3] is another way of performing distributed winner determination in combinatorial auction but it assumes the bids themselves perform the computation.\nThis algorithm also fails to converge to a solution for most cases.\nIn [9] the authors present a distributed mechanism for calculating VCG payments in a mechanism design problem.\nTheir mechanism roughly amounts to having each agent calculate the payments for two other agents and give these to a secure\n700 The Sixth Intl. .\nJoint Conf.\non Autonomous Agents and Multi-Agent Systems (AAMAS 07)\ncentral server which then checks to make sure results from all pairs agree, otherwise a re-calculation is ordered.\nThis general idea, which they call the redundancy principle, could also be applied to our problem but it requires the existence of a secure center agent that everyone trusts.\nAnother interesting approach is given in [8] where the bidding agents prioritize their bids, thus reducing the set of bids that the centralized winner determination algorithm must consider, making that problem easier.\nFinally, in the computation procuring clock auction [1] the agents are given an everincreasing percentage of the surplus achieved by their proposed solution over the current best.\nAs such, it assumes the agents are impartial computational entities, not the set of possible buyers as assumed by the PAUSE auction.\n7.\nCONCLUSIONS\nWe believe that distributed solutions to the winner determination problem should be studied as they offer a better fit for some applications as when, for example, agents do not want to reveal their valuations to the auctioneer or when we wish to distribute the computational load among the bidders.\nThe PAUSE auction is one of a few approaches to decentralize the winner determination problem in combinatorial auctions.\nWith this auction, we can even envision completely eliminating the auctioneer and, instead, have every agent performe the task of the auctioneer.\nHowever, while PAUSE establishes the rules the bidders must obey, it does not tell us how the bidders should calculate their bids.\nWe have presented two algorithms, PAUSEBID and CACHEDPAUSEBID, that bidder agents can use to engage in a PAUSE auction.\nBoth algorithms implement a myopic utility maximizing strategy that is guaranteed to find the bidset that maximizes the agent's utility given the set of outstanding best bids at any given time, without considering possible future bids.\nBoth algorithms find, most of the time, the same distribution of items as the revenue-maximizing solution.\nThe cases where our algorithms failed to arrive at that distribution are those where there was a large gap between the first and second valuation for a set (or sets) of items.\nAs it is an NP-Hard problem, the running time of our algorithms remains exponential but it is significantly better than a full search.\nPAUSEBID performs a branch and bound search completely from scratch each time it is invoked.\nCACHEDPAUSEBID caches partial solutions and performs a branch and bound search only on the few portions affected by the changes on the bids between consecutive times.\nCACHEDPAUSEBID has a better performance since it explores fewer nodes (less than half) and it is faster.\nAs expected the revenue generated by a PAUSE auction is lower than the revenue of a revenue-maximizing solution found by a centralized winner determination algorithm, however we found that CACHEDPAUSEBID generates in average 4.7% higher revenue than PAUSEBID.\nWe also found that the revenue generated by our algorithms increases as function of the number of items in the auction.\nOur algorithms have shown that it is feasible to implement the complex coordination constraints supported by combinatorial auctions without having to resort to a centralized winner determination algorithm.\nMoreover, because of the design of the PAUSE auction, the agents in the auction also have an incentive to perform the required computation.\nOur bidding algorithms can be used by any multiagent system that would use combinatorial auctions for coordination but would rather not implement a centralized auctioneer."},"lvl-4":{"kind":"string","value":"Bidding Algorithms for a Distributed Combinatorial Auction\nABSTRACT\nDistributed allocation and multiagent coordination problems can be solved through combinatorial auctions.\nHowever, most of the existing winner determination algorithms for combinatorial auctions are centralized.\nThe PAUSE auction is one of a few efforts to release the auctioneer from having to do all the work (it might even be possible to get rid of the auctioneer).\nIt is an increasing price combinatorial auction that naturally distributes the problem of winner determination amongst the bidders in such a way that they have an incentive to perform the calculation.\nIt can be used when we wish to distribute the computational load among the bidders or when the bidders do not wish to reveal their true valuations unless necessary.\nPAUSE establishes the rules the bidders must obey.\nHowever, it does not tell us how the bidders should calculate their bids.\nWe have developed a couple of bidding algorithms for the bidders in a PAUSE auction.\nOur algorithms always return the set of bids that maximizes the bidder's utility.\nSince the problem is NP-Hard, run time remains exponential on the number of items, but it is remarkably better than an exhaustive search.\nIn this paper we present our bidding algorithms, discuss their virtues and drawbacks, and compare the solutions obtained by them to the revenue-maximizing solution found by a centralized winner determination algorithm.\n1.\nINTRODUCTION\nBoth the research and practice of combinatorial auctions have grown rapidly in the past ten years.\nIn a combinatorial auction bidders can place bids on combinations of items, called packages or bidsets, rather than just individual items.\nOnce the bidders place their bids, it is necessary to find the allocation of items to bidders that maximizes the auctioneer's revenue.\nThis problem, known as the winner determination problem, is a combinatorial optimization problem and is NP-Hard [10].\nNevertheless, several algorithms that have a satisfactory performance for problem sizes and structures occurring in practice have been developed.\nBecause of their wide applicability, one cannot hope for a general-purpose winner determination algorithm that can efficiently solve every instance of the problem.\nThus, several approaches and algorithms have been proposed to address the winner determination problem.\nHowever, most of the existing winner determination algorithms for combinatorial auctions are centralized, meaning that they require all agents to send their bids to a centralized auctioneer who then determines the winners.\nExamples of these algorithms are CASS [3], Bidtree [11] and CABOB [12].\nWe believe that distributed solutions to the winner determination problem should be studied as they offer a better fit for some applications as when, for example, agents do not want to reveal their valuations to the auctioneer.\nThe PAUSE (Progressive Adaptive User Selection Environment) auction [4, 5] is one of a few efforts to distribute the problem of winner determination amongst the bidders.\nPAUSE establishes the rules the participants have to adhere to so that the work is distributed amongst them.\nHowever, it is not concerned with how the bidders determine what they should bid.\nIn this paper we present two algorithms, PAUSEBID and CACHEDPAUSEBID, which enable agents in a PAUSE auction to find the bidset that maximizes their utility.\nOur algorithms implement a myopic utility maximizing strategy and are guaranteed to find the bidset that maximizes the agent's utility given the outstanding best bids at a given time.\nPAUSEBID performs a branch and bound search completely from scratch every time that it is called.\nCACHEDPAUSEBID is a caching-based algorithm which explores fewer nodes, since it caches some solutions.\n6.\nRELATED WORK\nA lot of research has been done on various aspects of combinatorial auctions.\nHowever, the study of distributed winner determination algorithms for combinatorial auctions is still relatively new.\nOne approach is given by the algorithms for distributing the winner determination problem in combinatorial auctions presented in [7], but these algorithms assume the computational entities are the items being sold and thus end up with a different type of distribution.\nThe VSA algorithm [3] is another way of performing distributed winner determination in combinatorial auction but it assumes the bids themselves perform the computation.\nThis algorithm also fails to converge to a solution for most cases.\nIn [9] the authors present a distributed mechanism for calculating VCG payments in a mechanism design problem.\nTheir mechanism roughly amounts to having each agent calculate the payments for two other agents and give these to a secure\n700 The Sixth Intl. .\nJoint Conf.\nAnother interesting approach is given in [8] where the bidding agents prioritize their bids, thus reducing the set of bids that the centralized winner determination algorithm must consider, making that problem easier.\nFinally, in the computation procuring clock auction [1] the agents are given an everincreasing percentage of the surplus achieved by their proposed solution over the current best.\nAs such, it assumes the agents are impartial computational entities, not the set of possible buyers as assumed by the PAUSE auction.\n7.\nCONCLUSIONS\nThe PAUSE auction is one of a few approaches to decentralize the winner determination problem in combinatorial auctions.\nWith this auction, we can even envision completely eliminating the auctioneer and, instead, have every agent performe the task of the auctioneer.\nHowever, while PAUSE establishes the rules the bidders must obey, it does not tell us how the bidders should calculate their bids.\nWe have presented two algorithms, PAUSEBID and CACHEDPAUSEBID, that bidder agents can use to engage in a PAUSE auction.\nBoth algorithms implement a myopic utility maximizing strategy that is guaranteed to find the bidset that maximizes the agent's utility given the set of outstanding best bids at any given time, without considering possible future bids.\nBoth algorithms find, most of the time, the same distribution of items as the revenue-maximizing solution.\nAs it is an NP-Hard problem, the running time of our algorithms remains exponential but it is significantly better than a full search.\nPAUSEBID performs a branch and bound search completely from scratch each time it is invoked.\nCACHEDPAUSEBID caches partial solutions and performs a branch and bound search only on the few portions affected by the changes on the bids between consecutive times.\nAs expected the revenue generated by a PAUSE auction is lower than the revenue of a revenue-maximizing solution found by a centralized winner determination algorithm, however we found that CACHEDPAUSEBID generates in average 4.7% higher revenue than PAUSEBID.\nWe also found that the revenue generated by our algorithms increases as function of the number of items in the auction.\nOur algorithms have shown that it is feasible to implement the complex coordination constraints supported by combinatorial auctions without having to resort to a centralized winner determination algorithm.\nMoreover, because of the design of the PAUSE auction, the agents in the auction also have an incentive to perform the required computation.\nOur bidding algorithms can be used by any multiagent system that would use combinatorial auctions for coordination but would rather not implement a centralized auctioneer."}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":0,"numItemsPerPage":100,"numTotalItems":144,"offset":0,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NjUyNjUyMCwic3ViIjoiL2RhdGFzZXRzL3RhbG4tbHMybi9zZW1ldmFsLTIwMTAtcHJlIiwiZXhwIjoxNzc2NTMwMTIwLCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.CM99R6JzlUOVeYKveiwgYYHgdsxWmMxotwqSB7osAfdxKaRYdNpbe9f4L1FAE_9niMKZWCayF9Rpf5pQa7L2BA","displayUrls":true,"splitSizeSummaries":[{"config":"raw","split":"train","numRows":144,"numBytesParquet":7870349},{"config":"raw","split":"test","numRows":100,"numBytesParquet":5561526}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
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J-39 | The Sequential Auction Problem on eBay: An Empirical Analysis and a Solution | Bidders on eBay have no dominant bidding strategy when faced with multiple auctions each offering an item of interest. As seen through an analysis of 1,956 auctions on eBay for a Dell E193FP LCD monitor, some bidders win auctions at prices higher than those of other available auctions, while others never win an auction... | [
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] | The Sequential Auction Problem on eBay: An Empirical Analysis and a Solution ∗ Adam I. Juda Division of Engineering and Applied Sciences Harvard University, Harvard Business School ajuda@hbs.edu David C. Parkes Division of Engineering and Applied Sciences Harvard University parkes@eecs.harvard.edu ABSTRACT Bidders on e... | The Sequential Auction Problem on eBay: An Empirical Analysis and a Solution *
ABSTRACT
Bidders on eBay have no dominant bidding strategy when faced with multiple auctions each offering an item of interest.
As seen through an analysis of 1,956 auctions on eBay for a Dell E193FP LCD monitor, some bidders win auctions at... | The Sequential Auction Problem on eBay: An Empirical Analysis and a Solution *
ABSTRACT
Bidders on eBay have no dominant bidding strategy when faced with multiple auctions each offering an item of interest.
As seen through an analysis of 1,956 auctions on eBay for a Dell E193FP LCD monitor, some bidders win auctions at... | The Sequential Auction Problem on eBay: An Empirical Analysis and a Solution *
ABSTRACT
Bidders on eBay have no dominant bidding strategy when faced with multiple auctions each offering an item of interest.
As seen through an analysis of 1,956 auctions on eBay for a Dell E193FP LCD monitor, some bidders win auctions at... |
I-54 | Approximate and Online Multi-Issue Negotiation | This paper analyzes bilateral multi-issue negotiation between self-interested autonomous agents. The agents have time constraints in the form of both deadlines and discount factors. There are m > 1 issues for negotiation where each issue is viewed as a pie of size one. The issues are indivisible (i.e., individual issue... | [
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] | Approximate and Online Multi-Issue Negotiation Shaheen S. Fatima Department of Computer Science University of Liverpool Liverpool L69 3BX, UK.
shaheen@csc.liv.ac.uk Michael Wooldridge Department of Computer Science University of Liverpool Liverpool L69 3BX, UK.
mjw@csc.liv.ac.uk Nicholas R. Jennings School of Electroni... | Approximate and Online Multi-Issue Negotiation
ABSTRACT
This paper analyzes bilateral multi-issue negotiation between selfinterested autonomous agents.
The agents have time constraints in the form of both deadlines and discount factors.
There are m> 1 issues for negotiation where each issue is viewed as a pie of size o... | Approximate and Online Multi-Issue Negotiation
ABSTRACT
This paper analyzes bilateral multi-issue negotiation between selfinterested autonomous agents.
The agents have time constraints in the form of both deadlines and discount factors.
There are m> 1 issues for negotiation where each issue is viewed as a pie of size o... | Approximate and Online Multi-Issue Negotiation
ABSTRACT
This paper analyzes bilateral multi-issue negotiation between selfinterested autonomous agents.
The agents have time constraints in the form of both deadlines and discount factors.
There are m> 1 issues for negotiation where each issue is viewed as a pie of size o... |
I-68 | On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints | Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve. In this paper, we improve a state-of-the-art heuristic solution method for DEC-MDPs, called OC-DEC-MDP, that has recently been shown to scal... | [
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] | On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints Janusz Marecki and Milind Tambe Computer Science Department University of Southern California 941 W 37th Place, Los Angeles, CA 90089 {marecki, tambe}@usc.
edu ABSTRACT Decentralized Markov Decision Processes (DEC-... | On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints
ABSTRACT
Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve.
In this paper, we improve... | On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints
ABSTRACT
Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve.
In this paper, we improve... | On Opportunistic Techniques for Solving Decentralized Markov Decision Processes with Temporal Constraints
ABSTRACT
Decentralized Markov Decision Processes (DEC-MDPs) are a popular model of agent-coordination problems in domains with uncertainty and time constraints but very difficult to solve.
In this paper, we improve... |
I-55 | Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory | It is well established by conflict theorists and others that successful negotiation should incorporate creating value as well as claiming value. Joint improvements that bring benefits to all parties can be realised by (i) identifying attributes that are not of direct conflict between the parties, (ii) tradeoffs on attr... | [
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] | Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory Quoc Bao Vo School of Computer Science and IT RMIT University, Australia vqbao@cs.rmit.edu.au Lin Padgham School of Computer Science and IT RMIT University, Australia linpa@cs.rmit.edu.au ABSTRACT It is well established b... | Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory
ABSTRACT
It is well established by conflict theorists and others that successful negotiation should incorporate "creating value" as well as "claiming value."
Joint improvements that bring benefits to all parties can be re... | Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory
ABSTRACT
It is well established by conflict theorists and others that successful negotiation should incorporate "creating value" as well as "claiming value."
Joint improvements that bring benefits to all parties can be re... | Searching for Joint Gains in Automated Negotiations Based on Multi-criteria Decision Making Theory
ABSTRACT
It is well established by conflict theorists and others that successful negotiation should incorporate "creating value" as well as "claiming value."
Joint improvements that bring benefits to all parties can be re... |
J-38 | Multi-Attribute Coalitional Games | We study coalitional games where the value of cooperation among the agents are solely determined by the attributes the agents possess, with no assumption as to how these attributes jointly determine this value. This framework allows us to model diverse economic interactions by picking the right attributes. We study the... | [
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] | Multi-Attribute Coalitional Games∗ Samuel Ieong † Computer Science Department Stanford University Stanford, CA 94305 sieong@cs.stanford.edu Yoav Shoham Computer Science Department Stanford University Stanford, CA 94305 shoham@cs.stanford.edu ABSTRACT We study coalitional games where the value of cooperation among the a... | Multi-Attribute Coalitional Games * t
ABSTRACT
We study coalitional games where the value of cooperation among the agents are solely determined by the attributes the agents possess, with no assumption as to how these attributes jointly determine this value.
This framework allows us to model diverse economic interaction... | Multi-Attribute Coalitional Games * t
ABSTRACT
We study coalitional games where the value of cooperation among the agents are solely determined by the attributes the agents possess, with no assumption as to how these attributes jointly determine this value.
This framework allows us to model diverse economic interaction... | Multi-Attribute Coalitional Games * t
ABSTRACT
We study coalitional games where the value of cooperation among the agents are solely determined by the attributes the agents possess, with no assumption as to how these attributes jointly determine this value.
This framework allows us to model diverse economic interaction... |
I-57 | Rumours and Reputation: Evaluating Multi-Dimensional Trust within a Decentralised Reputation System | In this paper we develop a novel probabilistic model of computational trust that explicitly deals with correlated multi-dimensional contracts. Our starting point is to consider an agent attempting to estimate the utility of a contract, and we show that this leads to a model of computational trust whereby an agent must ... | [
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] | Rumours and Reputation: Evaluating Multi-Dimensional Trust within a Decentralised Reputation System Steven Reece1 , Alex Rogers2 , Stephen Roberts1 and Nicholas R. Jennings2 1 Department of Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK.
{reece,sjrob}@robots.
ox.ac.uk 2 Electronics and Computer Science,... | Rumours and Reputation: Evaluating Multi-Dimensional Trust within a Decentralised Reputation System
ABSTRACT
In this paper we develop a novel probabilistic model of computational trust that explicitly deals with correlated multi-dimensional contracts.
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ABSTRACT
In this paper we develop a novel probabilistic model of computational trust that explicitly deals with correlated multi-dimensional contracts.
Our starting point is to consider an agent attempting to estimate th... | Rumours and Reputation: Evaluating Multi-Dimensional Trust within a Decentralised Reputation System
ABSTRACT
In this paper we develop a novel probabilistic model of computational trust that explicitly deals with correlated multi-dimensional contracts.
Our starting point is to consider an agent attempting to estimate th... |
I-43 | Dynamics Based Control with an Application to Area-Sweeping Problems | In this paper we introduce Dynamics Based Control (DBC), an approach to planning and control of an agent in stochastic environments. Unlike existing approaches, which seek to optimize expected rewards (e.g., in Partially Observable Markov Decision Problems (POMDPs)), DBC optimizes system behavior towards specified syst... | [
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] | Dynamics Based Control with an Application to Area-Sweeping Problems Zinovi Rabinovich Engineering and Computer Science Hebrew University of Jerusalem Jerusalem, Israel nomad@cs.huji.ac.il Jeffrey S. Rosenschein Engineering and Computer Science Hebrew University of Jerusalem Jerusalem, Israel jeff@cs.huji.ac.il Gal A. ... | Dynamics Based Control with an Application to Area-Sweeping Problems
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In this paper we introduce Dynamics Based Control (DBC), an approach to planning and control of an agent in stochastic environments.
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ABSTRACT
In this paper we introduce Dynamics Based Control (DBC), an approach to planning and control of an agent in stochastic environments.
Unlike existing approaches, which seek to optimize expected rewards (e.g., in Partially Observable Markov Dec... | Dynamics Based Control with an Application to Area-Sweeping Problems
ABSTRACT
In this paper we introduce Dynamics Based Control (DBC), an approach to planning and control of an agent in stochastic environments.
Unlike existing approaches, which seek to optimize expected rewards (e.g., in Partially Observable Markov Dec... |
I-42 | A Complete Distributed Constraint Optimization Method For Non-Traditional Pseudotree Arrangements | Distributed Constraint Optimization (DCOP) is a general framework that can model complex problems in multi-agent systems. Several current algorithms that solve general DCOP instances, including ADOPT and DPOP, arrange agents into a traditional pseudotree structure. We introduce an extension to the DPOP algorithm that h... | [
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] | A Complete Distributed Constraint Optimization Method For Non-Traditional Pseudotree Arrangements∗ James Atlas Computer and Information Sciences University of Delaware Newark, DE 19716 atlas@cis.udel.edu Keith Decker Computer and Information Sciences University of Delaware Newark, DE 19716 decker@cis.udel.edu ABSTRACT ... | A Complete Distributed Constraint Optimization Method For Non-Traditional Pseudotree Arrangements *
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Distributed Constraint Optimization (DCOP) is a general framework that can model complex problems in multi-agent systems.
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Distributed Constraint Optimization (DCOP) is a general framework that can model complex problems in multi-agent systems.
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ABSTRACT
Distributed Constraint Optimization (DCOP) is a general framework that can model complex problems in multi-agent systems.
Several current algorithms that solve general DCOP instances, including ADOPT and DPOP, a... |
I-56 | Unifying Distributed Constraint Algorithms in a BDI Negotiation Framework | This paper presents a novel, unified distributed constraint satisfaction framework based on automated negotiation. The Distributed Constraint Satisfaction Problem (DCSP) is one that entails several agents to search for an agreement, which is a consistent combination of actions that satisfies their mutual constraints in... | [
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] | Unifying Distributed Constraint Algorithms in a BDI Negotiation Framework Bao Chau Le Dinh and Kiam Tian Seow School of Computer Engineering Nanyang Technological University Republic of Singapore {ledi0002,asktseow}@ntu.
edu.sg ABSTRACT This paper presents a novel, unified distributed constraint satisfaction framework ... | Unifying Distributed Constraint Algorithms in a BDI Negotiation Framework
ABSTRACT
This paper presents a novel, unified distributed constraint satisfaction framework based on automated negotiation.
The Distributed Constraint Satisfaction Problem (DCSP) is one that entails several agents to search for an agreement, whic... | Unifying Distributed Constraint Algorithms in a BDI Negotiation Framework
ABSTRACT
This paper presents a novel, unified distributed constraint satisfaction framework based on automated negotiation.
The Distributed Constraint Satisfaction Problem (DCSP) is one that entails several agents to search for an agreement, whic... | Unifying Distributed Constraint Algorithms in a BDI Negotiation Framework
ABSTRACT
This paper presents a novel, unified distributed constraint satisfaction framework based on automated negotiation.
The Distributed Constraint Satisfaction Problem (DCSP) is one that entails several agents to search for an agreement, whic... |
I-52 | A Unified and General Framework for Argumentation-based Negotiation | This paper proposes a unified and general framework for argumentation-based negotiation, in which the role of argumentation is formally analyzed. The framework makes it possible to study the outcomes of an argumentation-based negotiation. It shows what an agreement is, how it is related to the theories of the agents, w... | [
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] | A Unified and General Framework for Argumentation-based Negotiation Leila Amgoud IRIT - CNRS 118, route de Narbonne 31062, Toulouse, France amgoud@irit.fr Yannis Dimopoulos University of Cyprus 75 Kallipoleos Str.
PO Box 20537, Cyprus yannis@cs.ucy.ac.cy Pavlos Moraitis Paris-Descartes University 45 rue des Saints-Père... | A Unified and General Framework for Argumentation-based Negotiation
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This paper proposes a unified and general framework for argumentation-based negotiation, in which the role of argumentation is formally analyzed.
The framework makes it possible to study the outcomes of an argumentation-based negotiation.
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This paper proposes a unified and general framework for argumentation-based negotiation, in which the role of argumentation is formally analyzed.
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It s... | A Unified and General Framework for Argumentation-based Negotiation
ABSTRACT
This paper proposes a unified and general framework for argumentation-based negotiation, in which the role of argumentation is formally analyzed.
The framework makes it possible to study the outcomes of an argumentation-based negotiation.
It s... |
I-46 | Modular Interpreted Systems | We propose a new class of representations that can be used for modeling (and model checking) temporal, strategic and epistemic properties of agents and their teams. Our representations borrow the main ideas from interpreted systems of Halpern, Fagin et al.; however, they are also modular and compact in the way concurre... | [
"modular interpret system",
"model check",
"model check",
"open comput system",
"tempor and strateg logic",
"model methodolog",
"multi-agent system",
"higher level represent languag",
"branch time",
"comput tree logic ctl",
"altern-time tempor logic",
"kripk structur",
"synchron concurr prog... | [
"P",
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] | Modular Interpreted Systems Wojciech Jamroga Department of Informatics Clausthal University of Technology, Germany wjamroga@in.tu-clausthal.de Thomas Ågotnes Department of Computer Engineering Bergen University College, Norway tag@hib.no ABSTRACT We propose a new class of representations that can be used for modeling (... | Modular Interpreted Systems
ABSTRACT
We propose a new class of representations that can be used for modeling (and model checking) temporal, strategic and epistemic properties of agents and their teams.
Our representations borrow the main ideas from interpreted systems of Halpern, Fagin et al.; however, they are also mo... | Modular Interpreted Systems
ABSTRACT
We propose a new class of representations that can be used for modeling (and model checking) temporal, strategic and epistemic properties of agents and their teams.
Our representations borrow the main ideas from interpreted systems of Halpern, Fagin et al.; however, they are also mo... | Modular Interpreted Systems
ABSTRACT
We propose a new class of representations that can be used for modeling (and model checking) temporal, strategic and epistemic properties of agents and their teams.
Our representations borrow the main ideas from interpreted systems of Halpern, Fagin et al.; however, they are also mo... |
I-47 | Operational Semantics of Multiagent Interactions | The social stance advocated by institutional frameworks and most multi-agent system methodologies has resulted in a wide spectrum of organizational and communicative abstractions which have found currency in several programming frameworks and software platforms. Still, these tools and frameworks are designed to support... | [
"oper semant",
"multiag interact",
"institut framework",
"organiz and commun abstract",
"pre-defin abstract",
"softwar architectur",
"formal execut semant",
"social interact",
"organiz program languag",
"multi-agent interact connector-base model",
"softwar connector",
"structur oper semant"
] | [
"P",
"P",
"P",
"P",
"P",
"P",
"P",
"P",
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"M",
"R"
] | Operational Semantics of Multiagent Interactions Juan M. Serrano University Rey Juan Carlos C/Tulipan S/N Madrid, Spain juanmanuel.serrano@urjc.es Sergio Saugar University Rey Juan Carlos C/Tulipan S/N Madrid, Spain sergio.saugar@urjc.es ABSTRACT The social stance advocated by institutional frameworks and most multi-ag... | Operational Semantics of Multiagent Interactions
ABSTRACT
The social stance advocated by institutional frameworks and most multi-agent system methodologies has resulted in a wide spectrum of organizational and communicative abstractions which have found currency in several programming frameworks and software platforms.... | Operational Semantics of Multiagent Interactions
ABSTRACT
The social stance advocated by institutional frameworks and most multi-agent system methodologies has resulted in a wide spectrum of organizational and communicative abstractions which have found currency in several programming frameworks and software platforms.... | Operational Semantics of Multiagent Interactions
ABSTRACT
The social stance advocated by institutional frameworks and most multi-agent system methodologies has resulted in a wide spectrum of organizational and communicative abstractions which have found currency in several programming frameworks and software platforms.... |
I-53 | A Randomized Method for the Shapley Value for the Voting Game | The Shapley value is one of the key solution concepts for coalition games. Its main advantage is that it provides a unique and fair solution, but its main problem is that, for many coalition games, the Shapley value cannot be determined in polynomial time. In particular, the problem of finding this value for the voting... | [
"uniqu and fair solut",
"polynomi time",
"approxim",
"coalit format",
"interact",
"multi-agent system",
"cooper game theori",
"shaplei valu",
"reach consensu mean",
"randomis method",
"gener function",
"game-theori"
] | [
"P",
"P",
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"U",
"M",
"M",
"U",
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"M",
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] | A Randomized Method for the Shapley Value for the Voting Game Shaheen S. Fatima Department of Computer Science, University of Liverpool Liverpool L69 3BX, UK.
shaheen@csc.liv.ac.uk Michael Wooldridge Department of Computer Science, University of Liverpool Liverpool L69 3BX, UK.
mjw@csc.liv.ac.uk Nicholas R. Jennings Sc... | A Randomized Method for the Shapley Value for the Voting Game
ABSTRACT
The Shapley value is one of the key solution concepts for coalition games.
Its main advantage is that it provides a unique and fair solution, but its main problem is that, for many coalition games, the Shapley value cannot be determined in polynomia... | A Randomized Method for the Shapley Value for the Voting Game
ABSTRACT
The Shapley value is one of the key solution concepts for coalition games.
Its main advantage is that it provides a unique and fair solution, but its main problem is that, for many coalition games, the Shapley value cannot be determined in polynomia... | A Randomized Method for the Shapley Value for the Voting Game
ABSTRACT
The Shapley value is one of the key solution concepts for coalition games.
Its main advantage is that it provides a unique and fair solution, but its main problem is that, for many coalition games, the Shapley value cannot be determined in polynomia... |
I-45 | Implementing Commitment-Based Interactions | Although agent interaction plays a vital role in MAS, and message-centric approaches to agent interaction have their drawbacks, present agent-oriented programming languages do not provide support for implementing agent interaction that is flexible and robust. Instead, messages are provided as a primitive building block... | [
"agent interact",
"commit machin framework",
"commit machin",
"social commit",
"bdi",
"race condit",
"commit-base interact",
"messagecentr approach",
"agent-orient program languag",
"bdi-style plan",
"interact goal",
"turn track",
"belief manag method",
"herm design",
"netbil interact",
... | [
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] | Implementing Commitment-Based Interactions∗ Michael Winikoff School of Computer Science and IT RMIT University Melbourne, Australia michael.winikoff@rmit.edu.au ABSTRACT Although agent interaction plays a vital role in MAS, and messagecentric approaches to agent interaction have their drawbacks, present agent-oriented ... | Implementing Commitment-Based Interactions *
ABSTRACT
Although agent interaction plays a vital role in MAS, and messagecentric approaches to agent interaction have their drawbacks, present agent-oriented programming languages do not provide support for implementing agent interaction that is flexible and robust.
Instead... | Implementing Commitment-Based Interactions *
ABSTRACT
Although agent interaction plays a vital role in MAS, and messagecentric approaches to agent interaction have their drawbacks, present agent-oriented programming languages do not provide support for implementing agent interaction that is flexible and robust.
Instead... | Implementing Commitment-Based Interactions *
ABSTRACT
Although agent interaction plays a vital role in MAS, and messagecentric approaches to agent interaction have their drawbacks, present agent-oriented programming languages do not provide support for implementing agent interaction that is flexible and robust.
Instead... |
I-51 | Learning and Joint Deliberation through Argumentation in Multi-Agent Systems | In this paper we will present an argumentation framework for learning agents (AMAL) designed for two purposes: (1) for joint deliberation, and (2) for learning from communication. The AMAL framework is completely based on learning from examples: the argument preference relation, the argument generation policy, and the ... | [
"joint deliber",
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"learn agent",
"learn from commun",
"multi-agent system",
"argument protocol",
"collabor",
"group",
"predict accuraci",
"case-base polici",
"multi-agent learn",
"case-base reason"
] | [
"P",
"P",
"P",
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"U",
"U",
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] | Learning and Joint Deliberation through Argumentation in Multi-Agent Systems Santi Ontañón CCL, Cognitive Computing Lab Georgia Institute of Technology Atlanta, GA 303322/0280 santi@cc.gatech.edu Enric Plaza IIIA, Artificial Intelligence Research Institute CSIC, Spanish Council for Scientific Research Campus UAB, 08193... | Learning and Joint Deliberation through Argumentation in Multi-Agent Systems
ABSTRACT
In this paper we will present an argumentation framework for learning agents (AMAL) designed for two purposes: (1) for joint deliberation, and (2) for learning from communication.
The AMAL framework is completely based on learning fro... | Learning and Joint Deliberation through Argumentation in Multi-Agent Systems
ABSTRACT
In this paper we will present an argumentation framework for learning agents (AMAL) designed for two purposes: (1) for joint deliberation, and (2) for learning from communication.
The AMAL framework is completely based on learning fro... | Learning and Joint Deliberation through Argumentation in Multi-Agent Systems
ABSTRACT
In this paper we will present an argumentation framework for learning agents (AMAL) designed for two purposes: (1) for joint deliberation, and (2) for learning from communication.
The AMAL framework is completely based on learning fro... |
I-50 | Agents, Beliefs, and Plausible Behavior in a Temporal Setting | Logics of knowledge and belief are often too static and inflexible to be used on real-world problems. In particular, they usually offer no concept for expressing that some course of events is more likely to happen than another. We address this problem and extend CTLK (computation tree logic with knowledge) with a notio... | [
"belief",
"plausibl",
"logic",
"comput tree logic",
"reason",
"plausibl updat oper",
"plausibl notion",
"belief notion",
"multi-agent system",
"indistinguish",
"framework",
"semant",
"tempor logic"
] | [
"P",
"P",
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"P",
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"R",
"R",
"U",
"U",
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] | Agents, Beliefs, and Plausible Behavior in a Temporal Setting Nils Bulling and Wojciech Jamroga Department of Informatics, Clausthal University of Technology, Germany {bulling,wjamroga}@in.
tu-clausthal.
de ABSTRACT Logics of knowledge and belief are often too static and inflexible to be used on real-world problems.
In... | Agents, Beliefs, and Plausible Behavior in a Temporal Setting
ABSTRACT
Logics of knowledge and belief are often too static and inflexible to be used on real-world problems.
In particular, they usually offer no concept for expressing that some course of events is more likely to happen than another.
We address this probl... | Agents, Beliefs, and Plausible Behavior in a Temporal Setting
ABSTRACT
Logics of knowledge and belief are often too static and inflexible to be used on real-world problems.
In particular, they usually offer no concept for expressing that some course of events is more likely to happen than another.
We address this probl... | Agents, Beliefs, and Plausible Behavior in a Temporal Setting
ABSTRACT
Logics of knowledge and belief are often too static and inflexible to be used on real-world problems.
In particular, they usually offer no concept for expressing that some course of events is more likely to happen than another.
We address this probl... |
H-38 | DiffusionRank: A Possible Penicillin for Web Spamming | While the PageRank algorithm has proven to be very effective for ranking Web pages, the rank scores of Web pages can be manipulated. To handle the manipulation problem and to cast a new insight on the Web structure, we propose a ranking algorithm called DiffusionRank. DiffusionRank is motivated by the heat diffusion ph... | [
"diffusionrank",
"rank",
"web spam",
"pagerank",
"web graph",
"group-to-group relat",
"link commun",
"random graph",
"keyword stuf",
"link stuf",
"machin learn",
"link analysi",
"seed select algorithm",
"gaussian kernel smooth",
"equal vote abil"
] | [
"P",
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] | DiffusionRank: A Possible Penicillin for Web Spamming Haixuan Yang, Irwin King, and Michael R. Lyu Dept. of Computer Science and Engineering The Chinese University of Hong Kong Shatin, NT, Hong Kong {hxyang,king,lyu}@cse.
cuhk.edu.hk ABSTRACT While the PageRank algorithm has proven to be very effective for ranking Web ... | DiffusionRank: A Possible Penicillin for Web Spamming
ABSTRACT
While the PageRank algorithm has proven to be very effective for ranking Web pages, the rank scores of Web pages can be manipulated.
To handle the manipulation problem and to cast a new insight on the Web structure, we propose a ranking algorithm called Dif... | DiffusionRank: A Possible Penicillin for Web Spamming
ABSTRACT
While the PageRank algorithm has proven to be very effective for ranking Web pages, the rank scores of Web pages can be manipulated.
To handle the manipulation problem and to cast a new insight on the Web structure, we propose a ranking algorithm called Dif... | DiffusionRank: A Possible Penicillin for Web Spamming
ABSTRACT
While the PageRank algorithm has proven to be very effective for ranking Web pages, the rank scores of Web pages can be manipulated.
To handle the manipulation problem and to cast a new insight on the Web structure, we propose a ranking algorithm called Dif... |
H-77 | Automatic Extraction of Titles from General Documents using Machine Learning | In this paper, we propose a machine learning approach to title extraction from general documents. By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapters, technical papers, brochures, reports, and letters. Previously, methods have been pr... | [
"machin learn",
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"automat titl extract",
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"languag independ",
"metada of document",
"linguist featur",
"comparison between model",
"model gener",
"extract titl us",
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"metada extract"
] | [
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] | Automatic Extraction of Titles from General Documents using Machine Learning Yunhua Hu1 Computer Science Department Xi``an Jiaotong University No 28, Xianning West Road Xi'an, China, 710049 yunhuahu@mail.xjtu.edu.cn Hang Li, Yunbo Cao Microsoft Research Asia 5F Sigma Center, No. 49 Zhichun Road, Haidian, Beijing, China... | Automatic Extraction of Titles from General Documents using Machine Learning
ABSTRACT
In this paper, we propose a machine learning approach to title extraction from general documents.
By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapter... | Automatic Extraction of Titles from General Documents using Machine Learning
ABSTRACT
In this paper, we propose a machine learning approach to title extraction from general documents.
By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapter... | Automatic Extraction of Titles from General Documents using Machine Learning
ABSTRACT
In this paper, we propose a machine learning approach to title extraction from general documents.
By general documents, we mean documents that can belong to any one of a number of specific genres, including presentations, book chapter... |
H-63 | Location based Indexing Scheme for DAYS | Data dissemination through wireless channels for broadcasting information to consumers is becoming quite common. Many dissemination schemes have been proposed but most of them push data to wireless channels for general consumption. Push based broadcast [1] is essentially asymmetric, i.e., the volume of data being highe... | [
"index scheme",
"index scheme",
"index",
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"locat depend data",
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"tree structur",
"dai",
"pull base data access",
"wireless broadcast data map",
"wireless data broadcast",
"dat... | [
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] | Location based Indexing Scheme for DAYS Debopam Acharya and Vijay Kumar 1 Computer Science and Informatics University of Missouri-Kansas City Kansas City, MO 64110 dargc(kumarv)@umkc.
edu ABSTRACT Data dissemination through wireless channels for broadcasting information to consumers is becoming quite common.
Many disse... | Location based Indexing Scheme for DAYS
ABSTRACT
Data dissemination through wireless channels for broadcasting information to consumers is becoming quite common.
Many dissemination schemes have been proposed but most of them push data to wireless channels for general consumption.
Push based broadcast [1] is essentially... | Location based Indexing Scheme for DAYS
ABSTRACT
Data dissemination through wireless channels for broadcasting information to consumers is becoming quite common.
Many dissemination schemes have been proposed but most of them push data to wireless channels for general consumption.
Push based broadcast [1] is essentially... | Location based Indexing Scheme for DAYS
ABSTRACT
Data dissemination through wireless channels for broadcasting information to consumers is becoming quite common.
Many dissemination schemes have been proposed but most of them push data to wireless channels for general consumption.
Push based broadcast [1] is essentially... |
H-88 | Controlling Overlap in Content-Oriented XML Retrieval | The direct application of standard ranking techniques to retrieve individual elements from a collection of XML documents often produces a result set in which the top ranks are dominated by a large number of elements taken from a small number of highly relevant documents. This paper presents and evaluates an algorithm t... | [
"xml",
"rank",
"inex",
"inform retriev",
"xml ir",
"baselin retriev",
"sog quantiz",
"xml cumul gain metric",
"cumul gain",
"ideal gain vector",
"xcg metric reward retriev",
"multitext system",
"re-rank algorithm",
"term frequenc vector",
"prioriti queue",
"time complex",
"extend tre... | [
"P",
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] | Controlling Overlap in Content-Oriented XML Retrieval Charles L. A. Clarke School of Computer Science, University of Waterloo, Canada claclark@plg.uwaterloo.ca ABSTRACT The direct application of standard ranking techniques to retrieve individual elements from a collection of XML documents often produces a result set in... | Controlling Overlap in Content-Oriented XML Retrieval
ABSTRACT
The direct application of standard ranking techniques to retrieve individual elements from a collection of XML documents often produces a result set in which the top ranks are dominated by a large number of elements taken from a small number of highly relev... | Controlling Overlap in Content-Oriented XML Retrieval
ABSTRACT
The direct application of standard ranking techniques to retrieve individual elements from a collection of XML documents often produces a result set in which the top ranks are dominated by a large number of elements taken from a small number of highly relev... | Controlling Overlap in Content-Oriented XML Retrieval
ABSTRACT
The direct application of standard ranking techniques to retrieve individual elements from a collection of XML documents often produces a result set in which the top ranks are dominated by a large number of elements taken from a small number of highly relev... |
J-72 | Applying Learning Algorithms to Preference Elicitation | We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory. We show that learning algorithms can be used as a basis for preference elicitation algorithms. The resulting elicitation algorithms perform a polynomial nu... | [
"learn",
"learn algorithm",
"learn",
"prefer elicit",
"parallel",
"prefer elicit problem",
"combinatori auction",
"learn theori",
"prefer elicit algorithm",
"elicit algorithm",
"polynomi",
"result algorithm",
"polynomi commun",
"polynomi commun",
"convers procedur",
"combinatori auctio... | [
"P",
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"P",
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] | Applying Learning Algorithms to Preference Elicitation Sebastien M. Lahaie Division of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 slahaie@eecs.harvard.edu David C. Parkes Division of Engineering and Applied Sciences Harvard University Cambridge, MA 02138 parkes@eecs.harvard.edu ABSTRACT We ... | Applying Learning Algorithms to Preference Elicitation
ABSTRACT
We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory.
We show that learning algorithms can be used as a basis for preference elicitation algorith... | Applying Learning Algorithms to Preference Elicitation
ABSTRACT
We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory.
We show that learning algorithms can be used as a basis for preference elicitation algorith... | Applying Learning Algorithms to Preference Elicitation
ABSTRACT
We consider the parallels between the preference elicitation problem in combinatorial auctions and the problem of learning an unknown function from learning theory.
We show that learning algorithms can be used as a basis for preference elicitation algorith... |
C-56 | A Hierarchical Process Execution Support for Grid Computing | Grid is an emerging infrastructure used to share resources among virtual organizations in a seamless manner and to provide breakthrough computing power at low cost. Nowadays there are dozens of academic and commercial products that allow execution of isolated tasks on grids, but few products support the enactment of lo... | [
"hierarch process execut",
"process execut",
"grid comput",
"distribut system",
"distribut applic",
"distribut process",
"distribut schedul",
"distribut execut",
"distribut comput",
"parallel comput",
"parallel execut",
"process descript",
"grid architectur",
"schedul algorithm",
"proces... | [
"P",
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"M"
] | A Hierarchical Process Execution Support for Grid Computing Fábio R. L. Cicerre Institute of Computing State University of Campinas Campinas, Brazil fcicerre@ic.unicamp.br Edmundo R. M. Madeira Institute of Computing State University of Campinas Campinas, Brazil edmundo@ic.unicamp.br Luiz E. Buzato Institute of Computi... | A Hierarchical Process Execution Support for Grid Computing
ABSTRACT
Grid is an emerging infrastructure used to share resources among virtual organizations in a seamless manner and to provide breakthrough computing power at low cost.
Nowadays there are dozens of academic and commercial products that allow execution of ... | A Hierarchical Process Execution Support for Grid Computing
ABSTRACT
Grid is an emerging infrastructure used to share resources among virtual organizations in a seamless manner and to provide breakthrough computing power at low cost.
Nowadays there are dozens of academic and commercial products that allow execution of ... | A Hierarchical Process Execution Support for Grid Computing
ABSTRACT
Grid is an emerging infrastructure used to share resources among virtual organizations in a seamless manner and to provide breakthrough computing power at low cost.
Nowadays there are dozens of academic and commercial products that allow execution of ... |
C-42 | Demonstration of Grid-Enabled Ensemble Kalman Filter Data Assimilation Methodology for Reservoir Characterization | Ensemble Kalman filter data assimilation methodology is a popular approach for hydrocarbon reservoir simulations in energy exploration. In this approach, an ensemble of geological models and production data of oil fields is used to forecast the dynamic response of oil wells. The Schlumberger ECLIPSE software is used fo... | [
"ensembl kalman filter",
"data assimil methodolog",
"hydrocarbon reservoir simul",
"energi explor",
"tigr grid comput environ",
"tigr",
"grid comput",
"cyberinfrastructur develop project",
"high perform comput",
"tigr grid middlewar",
"strateg applic area",
"gridwai metaschedul",
"pool licen... | [
"P",
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"U",
"M",
"R",
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"R",
"U"
] | Demonstration of Grid-Enabled Ensemble Kalman Filter Data Assimilation Methodology for Reservoir Characterization Ravi Vadapalli High Performance Computing Center Texas Tech University Lubbock, TX 79409 001-806-742-4350 Ravi.Vadapalli@ttu.edu Ajitabh Kumar Department of Petroleum Engineering Texas A&M University Colleg... | Demonstration of Grid-Enabled Ensemble Kalman Filter Data Assimilation Methodology for Reservoir Characterization
ABSTRACT
Ensemble Kalman filter data assimilation methodology is a popular approach for hydrocarbon reservoir simulations in energy exploration.
In this approach, an ensemble of geological models and produc... | Demonstration of Grid-Enabled Ensemble Kalman Filter Data Assimilation Methodology for Reservoir Characterization
ABSTRACT
Ensemble Kalman filter data assimilation methodology is a popular approach for hydrocarbon reservoir simulations in energy exploration.
In this approach, an ensemble of geological models and produc... | Demonstration of Grid-Enabled Ensemble Kalman Filter Data Assimilation Methodology for Reservoir Characterization
ABSTRACT
Ensemble Kalman filter data assimilation methodology is a popular approach for hydrocarbon reservoir simulations in energy exploration.
In this approach, an ensemble of geological models and produc... |
J-66 | Expressive Negotiation over Donations to Charities | When donating money to a (say, charitable) cause, it is possible to use the contemplated donation as negotiating material to induce other parties interested in the charity to donate more. Such negotiation is usually done in terms of matching offers, where one party promises to pay a certain amount if others pay a certa... | [
"express negoti",
"donat to chariti",
"negoti materi",
"bid languag",
"concav bid",
"linear program",
"quasilinear",
"mechan design",
"chariti support",
"express chariti donat",
"combinatori auction",
"econom effici",
"bid framework",
"donat-clear",
"threshold bid",
"payment willing fu... | [
"P",
"P",
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"R",
"U",
"R",
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"U",
"M",
"M",
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"M"
] | Expressive Negotiation over Donations to Charities∗ Vincent Conitzer Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA conitzer@cs.cmu.edu Tuomas Sandholm Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA sandholm@cs.cmu.edu ABSTRACT When donating money to a (say, charitable)... | Expressive Negotiation over Donations to Charities ∗
ABSTRACT
When donating money to a (say, charitable) cause, it is possible to use the contemplated donation as negotiating material to induce other parties interested in the charity to donate more.
Such negotiation is usually done in terms of matching offers, where on... | Expressive Negotiation over Donations to Charities ∗
ABSTRACT
When donating money to a (say, charitable) cause, it is possible to use the contemplated donation as negotiating material to induce other parties interested in the charity to donate more.
Such negotiation is usually done in terms of matching offers, where on... | Expressive Negotiation over Donations to Charities ∗
ABSTRACT
When donating money to a (say, charitable) cause, it is possible to use the contemplated donation as negotiating material to induce other parties interested in the charity to donate more.
Such negotiation is usually done in terms of matching offers, where on... |
C-81 | Adaptive Duty Cycling for Energy Harvesting Systems | Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks. In this paper, we present an adaptive duty cycling algorithm that allows energy harvesting sensor nodes to autonomously adjust their duty cycle according to the energy availability in the env... | [
"duti cycl",
"energi harvest system",
"energi harvest",
"sensor network",
"energi neutral oper",
"environment energi",
"power manag",
"harvest-awar power manag",
"perform scale",
"duti cycl rate",
"network latenc",
"sampl frequenc",
"solar panel",
"energi track",
"storag buffer",
"powe... | [
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"M"
] | Adaptive Duty Cycling for Energy Harvesting Systems Jason Hsu, Sadaf Zahedi, Aman Kansal, Mani Srivastava Electrical Engineering Department University of California Los Angeles {jasonh,kansal,szahedi,mbs} @ ee.ucla.edu Vijay Raghunathan NEC Labs America Princeton, NJ vijay@nec-labs.com ABSTRACT Harvesting energy from t... | Adaptive Duty Cycling for Energy Harvesting Systems
{jasonh, kansal, szahedi, mbs} @ ee.ucla.edu vijay@nec-labs.com
ABSTRACT
Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks.
In this paper, we present an adaptive duty cycling algorithm that ... | Adaptive Duty Cycling for Energy Harvesting Systems
{jasonh, kansal, szahedi, mbs} @ ee.ucla.edu vijay@nec-labs.com
ABSTRACT
Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks.
In this paper, we present an adaptive duty cycling algorithm that ... | Adaptive Duty Cycling for Energy Harvesting Systems
{jasonh, kansal, szahedi, mbs} @ ee.ucla.edu vijay@nec-labs.com
ABSTRACT
Harvesting energy from the environment is feasible in many applications to ameliorate the energy limitations in sensor networks.
In this paper, we present an adaptive duty cycling algorithm that ... |
I-37 | A Framework for Agent-Based Distributed Machine Learning and Data Mining | This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes among agents and (ii) online reasoning about learning success and learning progress by learning agents. We present an abstract architecture t... | [
"agent",
"distribut machin learn",
"machin learn",
"data mine",
"individu learn process",
"meta-reason",
"distribut cluster applic",
"framework and architectur",
"unsupervis cluster",
"bayesian classifi",
"consensusbas method",
"commun and coordin",
"autonom learn agent",
"histor inform",
... | [
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] | A Framework for Agent-Based Distributed Machine Learning and Data Mining Jan Tozicka Gerstner Laboratory Czech Technical University Technick``a 2, Prague, 166 27 Czech Republic tozicka@labe.felk.cvut.cz Michael Rovatsos School of Informatics The University of Edinburgh Edinburgh EH8 9LE United Kingdom mrovatso@inf.ed.a... | A Framework for Agent-Based Distributed Machine Learning and Data Mining
ABSTRACT
This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes among agents and (ii) online reasoning about learning succe... | A Framework for Agent-Based Distributed Machine Learning and Data Mining
ABSTRACT
This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes among agents and (ii) online reasoning about learning succe... | A Framework for Agent-Based Distributed Machine Learning and Data Mining
ABSTRACT
This paper proposes a framework for agent-based distributed machine learning and data mining based on (i) the exchange of meta-level descriptions of individual learning processes among agents and (ii) online reasoning about learning succe... |
C-80 | Consistency-preserving Caching of Dynamic Database Content | With the growing use of dynamic web content generated from relational databases, traditional caching solutions for throughput and latency improvements are ineffective. We describe a middleware layer called Ganesh that reduces the volume of data transmitted without semantic interpretation of queries or results. It achie... | [
"databas content",
"relat databas",
"tempor local",
"cach dynam databas content",
"hash-base techniqu",
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"proxi",
"jdbc driver",
"resultset object",
"bboard benchmark",
"read-write oper",
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"content address storag",
"relat databas system"... | [
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] | Consistency-preserving Caching of Dynamic Database Content∗ Niraj Tolia and M. Satyanarayanan Carnegie Mellon University {ntolia,satya}@cs.
cmu.edu ABSTRACT With the growing use of dynamic web content generated from relational databases, traditional caching solutions for throughput and latency improvements are ineffect... | Consistency-preserving Caching of Dynamic Database Content *
ABSTRACT
With the growing use of dynamic web content generated from relational databases, traditional caching solutions for throughput and latency improvements are ineffective.
We describe a middleware layer called Ganesh that reduces the volume of data trans... | Consistency-preserving Caching of Dynamic Database Content *
ABSTRACT
With the growing use of dynamic web content generated from relational databases, traditional caching solutions for throughput and latency improvements are ineffective.
We describe a middleware layer called Ganesh that reduces the volume of data trans... | Consistency-preserving Caching of Dynamic Database Content *
ABSTRACT
With the growing use of dynamic web content generated from relational databases, traditional caching solutions for throughput and latency improvements are ineffective.
We describe a middleware layer called Ganesh that reduces the volume of data trans... |
J-67 | Mechanism Design for Online Real-Time Scheduling | For the problem of online real-time scheduling of jobs on a single processor, previous work presents matching upper and lower bounds on the competitive ratio that can be achieved by a deterministic algorithm. However, these results only apply to the non-strategic setting in which the jobs are released directly to the a... | [
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] | Mechanism Design for Online Real-Time Scheduling Ryan Porter∗ Computer Science Department Stanford University Stanford, CA 94305 rwporter@stanford.edu ABSTRACT For the problem of online real-time scheduling of jobs on a single processor, previous work presents matching upper and lower bounds on the competitive ratio th... | Mechanism Design for Online Real-Time Scheduling
ABSTRACT
For the problem of online real-time scheduling of jobs on a single processor, previous work presents matching upper and lower bounds on the competitive ratio that can be achieved by a deterministic algorithm.
However, these results only apply to the non-strategi... | Mechanism Design for Online Real-Time Scheduling
ABSTRACT
For the problem of online real-time scheduling of jobs on a single processor, previous work presents matching upper and lower bounds on the competitive ratio that can be achieved by a deterministic algorithm.
However, these results only apply to the non-strategi... | Mechanism Design for Online Real-Time Scheduling
ABSTRACT
For the problem of online real-time scheduling of jobs on a single processor, previous work presents matching upper and lower bounds on the competitive ratio that can be achieved by a deterministic algorithm.
However, these results only apply to the non-strategi... |
J-73 | Competitive Algorithms for VWAP and Limit Order Trading | We introduce new online models for two important aspects of modern financial markets: Volume Weighted Average Price trading and limit order books. We provide an extensive study of competitive algorithms in these models and relate them to earlier online algorithms for stock trading. | [
"competit algorithm",
"vwap",
"onlin model",
"modern financi market",
"onlin algorithm",
"stock trade",
"volum weight averag price trade model",
"limit order book trade model",
"trade sequenc",
"share",
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"onlin trade",
"competit analysi"
] | [
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] | Competitive Algorithms for VWAP and Limit Order Trading Sham M. Kakade Computer and Information Science University of Pennsylvania kakade@linc.cis.upenn.edu Michael Kearns Computer and Information Science University of Pennsylvania mkearns@cis.upenn.edu Yishay Mansour Computer Science Tel Aviv University mansour@post.t... | Competitive Algorithms for VWAP and Limit Order Trading
ABSTRACT
We introduce new online models for two important aspects of modern financial markets: Volume Weighted Average Price trading and limit order books.
We provide an extensive study of competitive algorithms in these models and relate them to earlier online al... | Competitive Algorithms for VWAP and Limit Order Trading
ABSTRACT
We introduce new online models for two important aspects of modern financial markets: Volume Weighted Average Price trading and limit order books.
We provide an extensive study of competitive algorithms in these models and relate them to earlier online al... | Competitive Algorithms for VWAP and Limit Order Trading
ABSTRACT
We introduce new online models for two important aspects of modern financial markets: Volume Weighted Average Price trading and limit order books.
We provide an extensive study of competitive algorithms in these models and relate them to earlier online al... |
C-57 | Congestion Games with Load-Dependent Failures: Identical Resources | We define a new class of games, congestion games with load-dependent failures (CGLFs), which generalizes the well-known class of congestion games, by incorporating the issue of resource failures into congestion games. In a CGLF, agents share a common set of resources, where each resource has a cost and a probability of... | [
"congest game",
"load-depend failur",
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"potenti function",
"pure strategi nash equilibrium",
"nash equilibrium",
"nondecreas cost function",
"localeffect game",
"resourc cost function",
"real-valu function",
"load-depend resourc failur"... | [
"P",
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] | Congestion Games with Load-Dependent Failures: Identical Resources Michal Penn Technion - IIT Haifa, Israel mpenn@ie.technion.ac.il Maria Polukarov Technion - IIT Haifa, Israel pmasha@tx.technion.ac.il Moshe Tennenholtz Technion - IIT Haifa, Israel moshet@ie.technion.ac.il ABSTRACT We define a new class of games, conge... | Congestion Games with Load-Dependent Failures: Identical Resources
ABSTRACT
We define a new class of games, congestion games with loaddependent failures (CGLFs), which generalizes the well-known class of congestion games, by incorporating the issue of resource failures into congestion games.
In a CGLF, agents share a c... | Congestion Games with Load-Dependent Failures: Identical Resources
ABSTRACT
We define a new class of games, congestion games with loaddependent failures (CGLFs), which generalizes the well-known class of congestion games, by incorporating the issue of resource failures into congestion games.
In a CGLF, agents share a c... | Congestion Games with Load-Dependent Failures: Identical Resources
ABSTRACT
We define a new class of games, congestion games with loaddependent failures (CGLFs), which generalizes the well-known class of congestion games, by incorporating the issue of resource failures into congestion games.
In a CGLF, agents share a c... |
H-62 | Implicit User Modeling for Personalized Search | Information retrieval systems (e.g., web search engines) are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a touris... | [
"implicit user model",
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"queri expans",
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"person web search",
"interact ir",
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] | [
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] | Implicit User Modeling for Personalized Search Xuehua Shen, Bin Tan, ChengXiang Zhai Department of Computer Science University of Illinois at Urbana-Champaign ABSTRACT Information retrieval systems (e.g., web search engines) are critical for overcoming information overload.
A major deficiency of existing retrieval syst... | Implicit User Modeling for Personalized Search
ABSTRACT
Information retrieval systems (e.g., web search engines) are critical for overcoming information overload.
A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently ... | Implicit User Modeling for Personalized Search
ABSTRACT
Information retrieval systems (e.g., web search engines) are critical for overcoming information overload.
A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently ... | Implicit User Modeling for Personalized Search
ABSTRACT
Information retrieval systems (e.g., web search engines) are critical for overcoming information overload.
A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently ... |
H-48 | A New Approach for Evaluating Query Expansion: Query-Document Term Mismatch | The effectiveness of information retrieval (IR) systems is influenced by the degree of term overlap between user queries and relevant documents. Query-document term mismatch, whether partial or total, is a fact that must be dealt with by IR systems. Query Expansion (QE) is one method for dealing with term mismatch. IR ... | [
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"relev document",
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"inform search",
"document expans",
"document process"
] | [
"P",
"P",
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"P",
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] | A New Approach for Evaluating Query Expansion: Query-Document Term Mismatch Tonya Custis Thomson Corporation 610 Opperman Drive St. Paul, MN tonya.custis@thomson.com Khalid Al-Kofahi Thomson Corporation 610 Opperman Drive St. Paul, MN khalid.al-kofahi@thomson.com ABSTRACT The effectiveness of information retrieval (IR)... | A New Approach for Evaluating Query Expansion: Query-Document Term Mismatch
ABSTRACT
The effectiveness of information retrieval (IR) systems is influenced by the degree of term overlap between user queries and relevant documents.
Query-document term mismatch, whether partial or total, is a fact that must be dealt with ... | A New Approach for Evaluating Query Expansion: Query-Document Term Mismatch
ABSTRACT
The effectiveness of information retrieval (IR) systems is influenced by the degree of term overlap between user queries and relevant documents.
Query-document term mismatch, whether partial or total, is a fact that must be dealt with ... | A New Approach for Evaluating Query Expansion: Query-Document Term Mismatch
ABSTRACT
The effectiveness of information retrieval (IR) systems is influenced by the degree of term overlap between user queries and relevant documents.
Query-document term mismatch, whether partial or total, is a fact that must be dealt with ... |
H-60 | A Frequency-based and a Poisson-based Definition of the Probability of Being Informative | This paper reports on theoretical investigations about the assumptions underlying the inverse document frequency (idf). We show that an intuitive idf-based probability function for the probability of a term being informative assumes disjoint document events. By assuming documents to be independent rather than disjoint,... | [
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] | A Frequency-based and a Poisson-based Definition of the Probability of Being Informative Thomas Roelleke Department of Computer Science Queen Mary University of London thor@dcs.qmul.ac.uk ABSTRACT This paper reports on theoretical investigations about the assumptions underlying the inverse document frequency (idf ).
We... | A Frequency-based and a Poisson-based Definition of the Probability of Being Informative
ABSTRACT
This paper reports on theoretical investigations about the assumptions underlying the inverse document frequency (idf).
We show that an intuitive idf - based probability function for the probability of a term being informa... | A Frequency-based and a Poisson-based Definition of the Probability of Being Informative
ABSTRACT
This paper reports on theoretical investigations about the assumptions underlying the inverse document frequency (idf).
We show that an intuitive idf - based probability function for the probability of a term being informa... | A Frequency-based and a Poisson-based Definition of the Probability of Being Informative
ABSTRACT
This paper reports on theoretical investigations about the assumptions underlying the inverse document frequency (idf).
We show that an intuitive idf - based probability function for the probability of a term being informa... |
J-59 | Cost Sharing in a Job Scheduling Problem Using the Shapley Value | A set of jobs need to be served by a single server which can serve only one job at a time. Jobs have processing times and incur waiting costs (linear in their waiting time). The jobs share their costs through compensation using monetary transfers. We characterize the Shapley value rule for this model using fairness axi... | [
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"job schedul",
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"fair axiom",
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"cooper game theori approach",
"unit wait cost",
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] | [
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] | Cost Sharing in a Job Scheduling Problem Using the Shapley Value Debasis Mishra Center for Operations Research and Econometrics (CORE) Universit´e Catholique de Louvain Louvain la Neuve, Belgium mishra@core.ucl.ac.be Bharath Rangarajan Center for Operations Research and Econometrics (CORE) Universit´e Catholique de Lou... | Cost Sharing in a Job Scheduling Problem Using the Shapley Value
ABSTRACT
A set of jobs need to be served by a single server which can serve only one job at a time.
Jobs have processing times and incur waiting costs (linear in their waiting time).
The jobs share their costs through compensation using monetary transfers... | Cost Sharing in a Job Scheduling Problem Using the Shapley Value
ABSTRACT
A set of jobs need to be served by a single server which can serve only one job at a time.
Jobs have processing times and incur waiting costs (linear in their waiting time).
The jobs share their costs through compensation using monetary transfers... | Cost Sharing in a Job Scheduling Problem Using the Shapley Value
ABSTRACT
A set of jobs need to be served by a single server which can serve only one job at a time.
Jobs have processing times and incur waiting costs (linear in their waiting time).
The jobs share their costs through compensation using monetary transfers... |
C-69 | pTHINC: A Thin-Client Architecture for Mobile Wireless Web | Although web applications are gaining popularity on mobile wireless PDAs, web browsers on these systems can be quite slow and often lack adequate functionality to access many web sites. We have developed pTHINC, a PDA thinclient solution that leverages more powerful servers to run full-function web browsers and other a... | [
"pthinc",
"thin-client",
"mobil",
"web applic",
"mobil wireless pda",
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"function",
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"seamless mobil",
"system usabl",
"screen resolut",
"local pda web browser",
"web brows perform",
"crucial browser helper applic",
"video playback",
"full-function w... | [
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] | pTHINC: A Thin-Client Architecture for Mobile Wireless Web Joeng Kim, Ricardo A. Baratto, and Jason Nieh Department of Computer Science Columbia University, New York, NY, USA {jk2438, ricardo, nieh}@cs.
columbia.edu ABSTRACT Although web applications are gaining popularity on mobile wireless PDAs, web browsers on these... | pTHINC: A Thin-Client Architecture for Mobile Wireless Web
ABSTRACT
Although web applications are gaining popularity on mobile wireless PDAs, web browsers on these systems can be quite slow and often lack adequate functionality to access many web sites.
We have developed pTHINC, a PDA thinclient solution that leverages... | pTHINC: A Thin-Client Architecture for Mobile Wireless Web
ABSTRACT
Although web applications are gaining popularity on mobile wireless PDAs, web browsers on these systems can be quite slow and often lack adequate functionality to access many web sites.
We have developed pTHINC, a PDA thinclient solution that leverages... | pTHINC: A Thin-Client Architecture for Mobile Wireless Web
ABSTRACT
Although web applications are gaining popularity on mobile wireless PDAs, web browsers on these systems can be quite slow and often lack adequate functionality to access many web sites.
We have developed pTHINC, a PDA thinclient solution that leverages... |
J-65 | Privacy in Electronic Commerce and the Economics of Immediate Gratification | Dichotomies between privacy attitudes and behavior have been noted in the literature but not yet fully explained. We apply lessons from the research on behavioral economics to understand the individual decision making process with respect to privacy in electronic commerce. We show that it is unrealistic to expect indiv... | [
"privaci",
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"immedi gratif",
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"self-control problem",
"psycholog distort",
"anonym",
"person inform protect",
"psycholog inconsist",
"time-inconsist prefer",
"financi privaci",
"privaci sensit decis",
"privaci enhanc technolog",
... | [
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] | Privacy in Electronic Commerce and the Economics of Immediate Gratification Alessandro Acquisti H. John Heinz III School of Public Policy and Management Carnegie Mellon University acquisti@andrew.cmu.edu ABSTRACT Dichotomies between privacy attitudes and behavior have been noted in the literature but not yet fully expl... | Privacy in Electronic Commerce and the Economics of Immediate Gratification
ABSTRACT
Dichotomies between privacy attitudes and behavior have been noted in the literature but not yet fully explained.
We apply lessons from the research on behavioral economics to understand the individual decision making process with resp... | Privacy in Electronic Commerce and the Economics of Immediate Gratification
ABSTRACT
Dichotomies between privacy attitudes and behavior have been noted in the literature but not yet fully explained.
We apply lessons from the research on behavioral economics to understand the individual decision making process with resp... | Privacy in Electronic Commerce and the Economics of Immediate Gratification
ABSTRACT
Dichotomies between privacy attitudes and behavior have been noted in the literature but not yet fully explained.
We apply lessons from the research on behavioral economics to understand the individual decision making process with resp... |
C-41 | Evaluating Adaptive Resource Management for Distributed Real-Time Embedded Systems | A challenging problem faced by researchers and developers of distributed real-time and embedded (DRE) systems is devising and implementing effective adaptive resource management strategies that can meet end-to-end quality of service (QoS) requirements in varying operational conditions. This paper presents two contribut... | [
"adapt resourc manag",
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"hybrid control techniqu",
"distribut real-time embed system",
"servic end-to-end qualiti",
"real-time video distribut system",
"real-time corba specif",
"video encod/decod",
"resourc reserv mechan",
"dynam environ",
"stream servic",... | [
"P",
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] | Evaluating Adaptive Resource Management for Distributed Real-Time Embedded Systems Nishanth Shankaran, ∗ Xenofon Koutsoukos, Douglas C. Schmidt, and Aniruddha Gokhale Dept. of EECS, Vanderbilt University, Nashville ABSTRACT A challenging problem faced by researchers and developers of distributed real-time and embedded ... | Evaluating Adaptive Resource Management for Distributed Real-Time Embedded Systems
ABSTRACT
A challenging problem faced by researchers and developers of distributed real-time and embedded (DRE) systems is devising and implementing effective adaptive resource management strategies that can meet end-to-end quality of ser... | Evaluating Adaptive Resource Management for Distributed Real-Time Embedded Systems
ABSTRACT
A challenging problem faced by researchers and developers of distributed real-time and embedded (DRE) systems is devising and implementing effective adaptive resource management strategies that can meet end-to-end quality of ser... | Evaluating Adaptive Resource Management for Distributed Real-Time Embedded Systems
ABSTRACT
A challenging problem faced by researchers and developers of distributed real-time and embedded (DRE) systems is devising and implementing effective adaptive resource management strategies that can meet end-to-end quality of ser... |
C-55 | Context Awareness for Group Interaction Support | In this paper, we present an implemented system for supporting group interaction in mobile distributed computing environments. First, an introduction to context computing and a motivation for using contextual information to facilitate group interaction is given. We then present the architecture of our system, which con... | [
"context awar",
"group interact",
"locat sens",
"softwar framework",
"contextawar",
"mobil system",
"fifth contextdimens group-context",
"xml configur file",
"event-condit-action",
"sensor fusion"
] | [
"P",
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] | Context Awareness for Group Interaction Support Alois Ferscha, Clemens Holzmann, Stefan Oppl Institut für Pervasive Computing, Johannes Kepler Universität Linz Altenbergerstraße 69, A-4040 Linz {ferscha,holzmann,oppl}@soft.
uni-linz.
ac.at ABSTRACT In this paper, we present an implemented system for supporting group in... | Context Awareness for Group Interaction Support
ABSTRACT
In this paper, we present an implemented system for supporting group interaction in mobile distributed computing environments.
First, an introduction to context computing and a motivation for using contextual information to facilitate group interaction is given.
... | Context Awareness for Group Interaction Support
ABSTRACT
In this paper, we present an implemented system for supporting group interaction in mobile distributed computing environments.
First, an introduction to context computing and a motivation for using contextual information to facilitate group interaction is given.
... | Context Awareness for Group Interaction Support
ABSTRACT
In this paper, we present an implemented system for supporting group interaction in mobile distributed computing environments.
First, an introduction to context computing and a motivation for using contextual information to facilitate group interaction is given.
... |
J-71 | A Dynamic Pari-Mutuel Market for Hedging, Wagering, and Information Aggregation | I develop a new mechanism for risk allocation and information speculation called a dynamic pari-mutuel market (DPM). A DPM acts as hybrid between a pari-mutuel market and a continuous double auction (CDA), inheriting some of the advantages of both. Like a pari-mutuel market, a DPM offers infinite buy-in liquidity and z... | [
"dynam pari-mutuel market",
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"cda",
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] | A Dynamic Pari-Mutuel Market for Hedging, Wagering, and Information Aggregation David M. Pennock Yahoo! Research Labs 74 N. Pasadena Ave, 3rd Floor Pasadena, CA 91103 USA pennockd@yahoo-inc.com ABSTRACT I develop a new mechanism for risk allocation and information speculation called a dynamic pari-mutuel market (DPM).
... | A Dynamic Pari-Mutuel Market for Hedging, Wagering, and Information Aggregation
ABSTRACT
I develop a new mechanism for risk allocation and information speculation called a dynamic pari-mutuel market (DPM).
A DPM acts as hybrid between a pari-mutuel market and a continuous double auction (CDA), inheriting some of the ad... | A Dynamic Pari-Mutuel Market for Hedging, Wagering, and Information Aggregation
ABSTRACT
I develop a new mechanism for risk allocation and information speculation called a dynamic pari-mutuel market (DPM).
A DPM acts as hybrid between a pari-mutuel market and a continuous double auction (CDA), inheriting some of the ad... | A Dynamic Pari-Mutuel Market for Hedging, Wagering, and Information Aggregation
ABSTRACT
I develop a new mechanism for risk allocation and information speculation called a dynamic pari-mutuel market (DPM).
A DPM acts as hybrid between a pari-mutuel market and a continuous double auction (CDA), inheriting some of the ad... |
C-83 | Concept and Architecture of a Pervasive Document Editing and Managing System | Collaborative document processing has been addressed by many approaches so far, most of which focus on document versioning and collaborative editing. We address this issue from a different angle and describe the concept and architecture of a pervasive document editing and managing system. It exploits database technique... | [
"pervas document edit and manag system",
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"real-time transact",
"comput support collabor work",
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"collabor layout",
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] | Concept and Architecture of a Pervasive Document Editing and Managing System Stefania Leone Thomas B. Hodel Harald Gall University of Zurich, Switzerland University of Zurich, Switzerland University of Zurich, Switzerland Department of Informatics Department of Informatics Department of Informatics leone@ifi.unizh.ch h... | Concept and Architecture of a Pervasive Document Editing and Managing System
ABSTRACT
Collaborative document processing has been addressed by many approaches so far, most of which focus on document versioning and collaborative editing.
We address this issue from a different angle and describe the concept and architectu... | Concept and Architecture of a Pervasive Document Editing and Managing System
ABSTRACT
Collaborative document processing has been addressed by many approaches so far, most of which focus on document versioning and collaborative editing.
We address this issue from a different angle and describe the concept and architectu... | Concept and Architecture of a Pervasive Document Editing and Managing System
ABSTRACT
Collaborative document processing has been addressed by many approaches so far, most of which focus on document versioning and collaborative editing.
We address this issue from a different angle and describe the concept and architectu... |
C-54 | Remote Access to Large Spatial Databases | Enterprises in the public and private sectors have been making their large spatial data archives available over the Internet. However, interactive work with such large volumes of online spatial data is a challenging task. We propose two efficient approaches to remote access to large spatial data. First, we introduce a ... | [
"remot access",
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"dynam network infrastructur",
"web browser",
"internet-enabl databas manag system",
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] | Remote Access to Large Spatial Databases ∗ Egemen Tanin Frantiˇsek Brabec Hanan Samet Computer Science Department Center for Automation Research Institute for Advanced Computer Studies University of Maryland, College Park, MD 20742 {egemen,brabec,hjs}@umiacs.
umd.edu www.cs.umd.edu/{~egemen,~brabec,~hjs} ABSTRACT Enter... | Remote Access to Large Spatial Databases *
ABSTRACT
Enterprises in the public and private sectors have been making their large spatial data archives available over the Internet.
However, interactive work with such large volumes of online spatial data is a challenging task.
We propose two efficient approaches to remote ... | Remote Access to Large Spatial Databases *
ABSTRACT
Enterprises in the public and private sectors have been making their large spatial data archives available over the Internet.
However, interactive work with such large volumes of online spatial data is a challenging task.
We propose two efficient approaches to remote ... | Remote Access to Large Spatial Databases *
ABSTRACT
Enterprises in the public and private sectors have been making their large spatial data archives available over the Internet.
However, interactive work with such large volumes of online spatial data is a challenging task.
We propose two efficient approaches to remote ... |
J-70 | Self-interested Automated Mechanism Design and Implications for Optimal Combinatorial Auctions | Often, an outcome must be chosen on the basis of the preferences reported by a group of agents. The key difficulty is that the agents may report their preferences insincerely to make the chosen outcome more favorable to themselves. Mechanism design is the art of designing the rules of the game so that the agents are mo... | [
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"combinatori auction",
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"prefer aggreg",
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"individu ration",
"nonmanipul mechan",
"statist knowledg",
"classic mechan",
"payment maxim",
"fallback outcom",
"minsat",
"self-interest amd",
"complement... | [
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] | Self-interested Automated Mechanism Design and Implications for Optimal Combinatorial Auctions∗ Vincent Conitzer Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA conitzer@cs.cmu.edu Tuomas Sandholm Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA sandholm@cs.cmu.edu ABSTRAC... | Self-interested Automated Mechanism Design and Implications for Optimal Combinatorial Auctions ∗
ABSTRACT
Often, an outcome must be chosen on the basis of the preferences reported by a group of agents.
The key difficulty is that the agents may report their preferences insincerely to make the chosen outcome more favorab... | Self-interested Automated Mechanism Design and Implications for Optimal Combinatorial Auctions ∗
ABSTRACT
Often, an outcome must be chosen on the basis of the preferences reported by a group of agents.
The key difficulty is that the agents may report their preferences insincerely to make the chosen outcome more favorab... | Self-interested Automated Mechanism Design and Implications for Optimal Combinatorial Auctions ∗
ABSTRACT
Often, an outcome must be chosen on the basis of the preferences reported by a group of agents.
The key difficulty is that the agents may report their preferences insincerely to make the chosen outcome more favorab... |
C-68 | An Evaluation of Availability Latency in Carrier-based Vehicular ad-hoc Networks | On-demand delivery of audio and video clips in peer-to-peer vehicular ad-hoc networks is an emerging area of research. Our target environment uses data carriers, termed zebroids, where a mobile device carries a data item on behalf of a server to a client thereby minimizing its availability latency. In this study, we qu... | [
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... | [
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] | An Evaluation of Availability Latency in Carrier-based Vehicular ad-hoc Networks Shahram Ghandeharizadeh Dept of Computer Science Univ of Southern California Los Angeles, CA 90089, USA shahram@usc.edu Shyam Kapadia Dept of Computer Science Univ of Southern California Los Angeles, CA 90089, USA kapadia@usc.edu Bhaskar K... | An Evaluation of Availability Latency in Carrier-based Vehicular Ad-Hoc Networks Shahram
ABSTRACT
On-demand delivery of audio and video clips in peer-to-peer vehicular ad-hoc networks is an emerging area of research.
Our target environment uses data carriers, termed zebroids, where a mobile device carries a data item o... | An Evaluation of Availability Latency in Carrier-based Vehicular Ad-Hoc Networks Shahram
ABSTRACT
On-demand delivery of audio and video clips in peer-to-peer vehicular ad-hoc networks is an emerging area of research.
Our target environment uses data carriers, termed zebroids, where a mobile device carries a data item o... | An Evaluation of Availability Latency in Carrier-based Vehicular Ad-Hoc Networks Shahram
ABSTRACT
On-demand delivery of audio and video clips in peer-to-peer vehicular ad-hoc networks is an emerging area of research.
Our target environment uses data carriers, termed zebroids, where a mobile device carries a data item o... |
J-58 | Towards Truthful Mechanisms for Binary Demand Games: A General Framework | The family of Vickrey-Clarke-Groves (VCG) mechanisms is arguably the most celebrated achievement in truthful mechanism design. However, VCG mechanisms have their limitations. They only apply to optimization problems with a utilitarian (or affine) objective function, and their output should optimize the objective functi... | [
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] | Towards Truthful Mechanisms for Binary Demand Games: A General Framework Ming-Yang Kao ∗ Dept. of Computer Science Northwestern University Evanston, IL, USA kao@cs.northwestern.edu Xiang-Yang Li † Dept. of Computer Science Illinois Institute of Technology Chicago, IL, USA xli@cs.iit.edu WeiZhao Wang Dept. of Computer S... | Towards Truthful Mechanisms for Binary Demand Games: A General Framework
ABSTRACT
The family of Vickrey-Clarke-Groves (VCG) mechanisms is arguably the most celebrated achievement in truthful mechanism design.
However, VCG mechanisms have their limitations.
They only apply to optimization problems with a utilitarian (or... | Towards Truthful Mechanisms for Binary Demand Games: A General Framework
ABSTRACT
The family of Vickrey-Clarke-Groves (VCG) mechanisms is arguably the most celebrated achievement in truthful mechanism design.
However, VCG mechanisms have their limitations.
They only apply to optimization problems with a utilitarian (or... | Towards Truthful Mechanisms for Binary Demand Games: A General Framework
ABSTRACT
The family of Vickrey-Clarke-Groves (VCG) mechanisms is arguably the most celebrated achievement in truthful mechanism design.
However, VCG mechanisms have their limitations.
They only apply to optimization problems with a utilitarian (or... |
H-61 | Impedance Coupling in Content-targeted Advertising | The current boom of the Web is associated with the revenues originated from on-line advertising. While search-based advertising is dominant, the association of ads with a Web page (during user navigation) is becoming increasingly important. In this work, we study the problem of associating ads with a Web page, referred... | [
"content-target advertis",
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"match strategi",
"ad and keyword",
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"on-line advertis",
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"bayesian network model",
"expans term",
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] | Impedance Coupling in Content-targeted Advertising Berthier Ribeiro-Neto Computer Science Department Federal University of Minas Gerais Belo Horizonte, Brazil berthier@dcc.ufmg.br Marco Cristo Computer Science Department Federal University of Minas Gerais Belo Horizonte, Brazil marco@dcc.ufmg.br Paulo B. Golgher Akwan ... | Impedance Coupling in Content-targeted Advertising
ABSTRACT
The current boom of the Web is associated with the revenues originated from on-line advertising.
While search-based advertising is dominant, the association of ads with a Web page (during user navigation) is becoming increasingly important.
In this work, we st... | Impedance Coupling in Content-targeted Advertising
ABSTRACT
The current boom of the Web is associated with the revenues originated from on-line advertising.
While search-based advertising is dominant, the association of ads with a Web page (during user navigation) is becoming increasingly important.
In this work, we st... | Impedance Coupling in Content-targeted Advertising
ABSTRACT
The current boom of the Web is associated with the revenues originated from on-line advertising.
While search-based advertising is dominant, the association of ads with a Web page (during user navigation) is becoming increasingly important.
In this work, we st... |
H-49 | Performance Prediction Using Spatial Autocorrelation | Evaluation of information retrieval systems is one of the core tasks in information retrieval. Problems include the inability to exhaustively label all documents for a topic, nongeneralizability from a small number of topics, and incorporating the variability of retrieval systems. Previous work addresses the evaluation... | [
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] | [
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] | Performance Prediction Using Spatial Autocorrelation Fernando Diaz Center for Intelligent Information Retrieval Department of Computer Science University of Massachusetts Amherst, MA 01003 fdiaz@cs.umass.edu ABSTRACT Evaluation of information retrieval systems is one of the core tasks in information retrieval.
Problems... | Performance Prediction Using Spatial Autocorrelation
ABSTRACT
Evaluation of information retrieval systems is one of the core tasks in information retrieval.
Problems include the inability to exhaustively label all documents for a topic, nongeneralizability from a small number of topics, and incorporating the variabilit... | Performance Prediction Using Spatial Autocorrelation
ABSTRACT
Evaluation of information retrieval systems is one of the core tasks in information retrieval.
Problems include the inability to exhaustively label all documents for a topic, nongeneralizability from a small number of topics, and incorporating the variabilit... | Performance Prediction Using Spatial Autocorrelation
ABSTRACT
Evaluation of information retrieval systems is one of the core tasks in information retrieval.
Problems include the inability to exhaustively label all documents for a topic, nongeneralizability from a small number of topics, and incorporating the variabilit... |
C-44 | MSP: Multi-Sequence Positioning of Wireless Sensor Nodes | Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of wireless sensor nodes, a challenging task because of the severe constraints on cost, energy and effective range of sensor devices. To overcome limitations in existing solutions, w... | [
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"distribut-base locat estim",
"listen-detect-assembl-report protocol",
"margin distribut",
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] | [
"P",
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] | MSP: Multi-Sequence Positioning of Wireless Sensor Nodes∗ Ziguo Zhong Computer Science and Engineering University of Minnesota zhong@cs.umn.edu Tian He Computer Science and Engineering University of Minnesota tianhe@cs.umn.edu Abstract Wireless Sensor Networks have been proposed for use in many location-dependent appli... | MSP: Multi-Sequence Positioning of Wireless Sensor Nodes *
Abstract
Wireless Sensor Networks have been proposed for use in many location-dependent applications.
Most of these need to identify the locations of wireless sensor nodes, a challenging task because of the severe constraints on cost, energy and effective range... | MSP: Multi-Sequence Positioning of Wireless Sensor Nodes *
Abstract
Wireless Sensor Networks have been proposed for use in many location-dependent applications.
Most of these need to identify the locations of wireless sensor nodes, a challenging task because of the severe constraints on cost, energy and effective range... | MSP: Multi-Sequence Positioning of Wireless Sensor Nodes *
Abstract
Wireless Sensor Networks have been proposed for use in many location-dependent applications.
Most of these need to identify the locations of wireless sensor nodes, a challenging task because of the severe constraints on cost, energy and effective range... |
J-60 | On Decentralized Incentive Compatible Mechanisms for Partially Informed Environments | Algorithmic Mechanism Design focuses on Dominant Strategy Implementations. The main positive results are the celebrated Vickrey-Clarke-Groves (VCG) mechanisms and computationally efficient mechanisms for severely restricted players (single-parameter domains). As it turns out, many natural social goals cannot be impleme... | [
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"weakli domin strategi iter elimin",
"peer-to-peer",
"p-inform environ"
] | [
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] | On Decentralized Incentive Compatible Mechanisms for Partially Informed Environments ∗ Ahuva Mu``alem School of Engineering and Computer Science The Hebrew University of Jerusalem ahumu@cs.huji.ac.il ABSTRACT Algorithmic Mechanism Design focuses on Dominant Strategy Implementations.
The main positive results are the ce... | On Decentralized Incentive Compatible Mechanisms for Partially Informed Environments *
ABSTRACT
Algorithmic Mechanism Design focuses on Dominant Strategy Implementations.
The main positive results are the celebrated Vickrey-Clarke-Groves (VCG) mechanisms and computationally efficient mechanisms for severely restricted ... | On Decentralized Incentive Compatible Mechanisms for Partially Informed Environments *
ABSTRACT
Algorithmic Mechanism Design focuses on Dominant Strategy Implementations.
The main positive results are the celebrated Vickrey-Clarke-Groves (VCG) mechanisms and computationally efficient mechanisms for severely restricted ... | On Decentralized Incentive Compatible Mechanisms for Partially Informed Environments *
ABSTRACT
Algorithmic Mechanism Design focuses on Dominant Strategy Implementations.
The main positive results are the celebrated Vickrey-Clarke-Groves (VCG) mechanisms and computationally efficient mechanisms for severely restricted ... |
J-74 | On Cheating in Sealed-Bid Auctions | Motivated by the rise of online auctions and their relative lack of security, this paper analyzes two forms of cheating in sealed-bid auctions. The first type of cheating we consider occurs when the seller spies on the bids of a second-price auction and then inserts a fake bid in order to increase the payment of the wi... | [
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] | On Cheating in Sealed-Bid Auctions Ryan Porter rwporter@stanford.edu Yoav Shoham shoham@stanford.edu Computer Science Department Stanford University Stanford, CA 94305 ABSTRACT Motivated by the rise of online auctions and their relative lack of security, this paper analyzes two forms of cheating in sealed-bid auctions.... | On Cheating in Sealed-Bid Auctions
Motivated by the rise of online auctions and their relative lack of security, this paper analyzes two forms of cheating in sealed-bid auctions.
The first type of cheating we consider occurs when the seller spies on the bids of a second-price auction and then inserts a fake bid in orde... | On Cheating in Sealed-Bid Auctions
Motivated by the rise of online auctions and their relative lack of security, this paper analyzes two forms of cheating in sealed-bid auctions.
The first type of cheating we consider occurs when the seller spies on the bids of a second-price auction and then inserts a fake bid in orde... | On Cheating in Sealed-Bid Auctions
Motivated by the rise of online auctions and their relative lack of security, this paper analyzes two forms of cheating in sealed-bid auctions.
The first type of cheating we consider occurs when the seller spies on the bids of a second-price auction and then inserts a fake bid in orde... |
C-50 | CenWits: A Sensor-Based Loosely Coupled Search and Rescue System Using Witnesses | This paper describes the design, implementation and evaluation of a search and rescue system called CenWits. CenWits uses several small, commonly-available RF-based sensors, and a small number of storage and processing devices. It is designed for search and rescue of people in emergency situations in wilderness areas. ... | [
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] | CenWits: A Sensor-Based Loosely Coupled Search and Rescue System Using Witnesses Jyh-How Huang Department of Computer Science University of Colorado, Campus Box 0430 Boulder, CO 80309-0430 huangjh@cs.colorado.edu Saqib Amjad Department of Computer Science University of Colorado, Campus Box 0430 Boulder, CO 80309-0430 S... | CenWits: A Sensor-Based Loosely Coupled Search and Rescue System Using Witnesses
University of Colorado, Campus Box 0430 Boulder, CO 80309-0430
ABSTRACT
This paper describes the design, implementation and evaluation of a search and rescue system called CenWits.
CenWits uses several small, commonly-available RF-based se... | CenWits: A Sensor-Based Loosely Coupled Search and Rescue System Using Witnesses
University of Colorado, Campus Box 0430 Boulder, CO 80309-0430
ABSTRACT
This paper describes the design, implementation and evaluation of a search and rescue system called CenWits.
CenWits uses several small, commonly-available RF-based se... | CenWits: A Sensor-Based Loosely Coupled Search and Rescue System Using Witnesses
University of Colorado, Campus Box 0430 Boulder, CO 80309-0430
ABSTRACT
This paper describes the design, implementation and evaluation of a search and rescue system called CenWits.
CenWits uses several small, commonly-available RF-based se... |
C-78 | An Architectural Framework and a Middleware for Cooperating Smart Components | In a future networked physical world, a myriad of smart sensors and actuators assess and control aspects of their environments and autonomously act in response to it. Examples range in telematics, traffic management, team robotics or home automation to name a few. To a large extent, such systems operate proactively and... | [
"smart sensor",
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"sentient object",
"dissemin qualiti",
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] | An Architectural Framework and a Middleware for Cooperating Smart Components ∗ Ant´onio Casimiro U.Lisboa casim@di.fc.ul.pt J¨org Kaiser U.Ulm kaiser@informatik.uniulm.de Paulo Ver´ıssimo U.Lisboa pjv@di.fc.ul.pt ABSTRACT In a future networked physical world, a myriad of smart sensors and actuators assess and control a... | An Architectural Framework and a Middleware for Cooperating Smart Components *
U.Lisboa U.Ulm U.Lisboa casim@di.fc.ul.pt kaiser@informatik.uni- pjv@di.fc.ul.pt ulm.de
ABSTRACT
In a future networked physical world, a myriad of smart sensors and actuators assess and control aspects of their environments and autonomously ... | An Architectural Framework and a Middleware for Cooperating Smart Components *
U.Lisboa U.Ulm U.Lisboa casim@di.fc.ul.pt kaiser@informatik.uni- pjv@di.fc.ul.pt ulm.de
ABSTRACT
In a future networked physical world, a myriad of smart sensors and actuators assess and control aspects of their environments and autonomously ... | An Architectural Framework and a Middleware for Cooperating Smart Components *
U.Lisboa U.Ulm U.Lisboa casim@di.fc.ul.pt kaiser@informatik.uni- pjv@di.fc.ul.pt ulm.de
ABSTRACT
In a future networked physical world, a myriad of smart sensors and actuators assess and control aspects of their environments and autonomously ... |
J-49 | Information Markets vs. Opinion Pools: An Empirical Comparison | In this paper, we examine the relative forecast accuracy of information markets versus expert aggregation. We leverage a unique data source of almost 2000 people's subjective probability judgments on 2003 US National Football League games and compare with the market probabilities given by two different information mark... | [
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] | Information Markets vs. Opinion Pools: An Empirical Comparison Yiling Chen Chao-Hsien Chu Tracy Mullen School of Information Sciences & Technology The Pennsylvania State University University Park, PA 16802 {ychen|chu|tmullen}@ist.
psu.edu David M. Pennock Yahoo! Research Labs 74 N. Pasadena Ave, 3rd Floor Pasadena, CA... | Information Markets vs. Opinion Pools: An Empirical Comparison
ABSTRACT
In this paper, we examine the relative forecast accuracy of information markets versus expert aggregation.
We leverage a unique data source of almost 2000 people's subjective probability judgments on 2003 US National Football League games and compa... | Information Markets vs. Opinion Pools: An Empirical Comparison
ABSTRACT
In this paper, we examine the relative forecast accuracy of information markets versus expert aggregation.
We leverage a unique data source of almost 2000 people's subjective probability judgments on 2003 US National Football League games and compa... | Information Markets vs. Opinion Pools: An Empirical Comparison
ABSTRACT
In this paper, we examine the relative forecast accuracy of information markets versus expert aggregation.
We leverage a unique data source of almost 2000 people's subjective probability judgments on 2003 US National Football League games and compa... |
C-79 | A Cross-Layer Approach to Resource Discovery and Distribution in Mobile ad-hoc Networks | This paper describes a cross-layer approach to designing robust P2P system over mobile ad-hoc networks. The design is based on simple functional primitives that allow routing at both P2P and network layers to be integrated to reduce overhead. With these primitives, the paper addresses various load balancing techniques.... | [
"resourc discoveri",
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] | A Cross-Layer Approach to Resource Discovery and Distribution in Mobile ad-hoc Networks Chaiporn Jaikaeo Computer Engineering Kasetsart University, Thailand (+662) 942-8555 Ext 1424 cpj@cpe.ku.ac.th Xiang Cao Computer and Information Sciences University of Delaware, USA (+1) 302-831-1131 cao@cis.udel.edu Chien-Chung Sh... | A Cross-Layer Approach to Resource Discovery and Distribution in Mobile Ad hoc Networks
ABSTRACT
This paper describes a cross-layer approach to designing robust P2P system over mobile ad hoc networks.
The design is based on simple functional primitives that allow routing at both P2P and network layers to be integrated ... | A Cross-Layer Approach to Resource Discovery and Distribution in Mobile Ad hoc Networks
ABSTRACT
This paper describes a cross-layer approach to designing robust P2P system over mobile ad hoc networks.
The design is based on simple functional primitives that allow routing at both P2P and network layers to be integrated ... | A Cross-Layer Approach to Resource Discovery and Distribution in Mobile Ad hoc Networks
ABSTRACT
This paper describes a cross-layer approach to designing robust P2P system over mobile ad hoc networks.
The design is based on simple functional primitives that allow routing at both P2P and network layers to be integrated ... |
C-45 | StarDust: A Flexible Architecture for Passive Localization in Wireless Sensor Networks | The problem of localization in wireless sensor networks where nodes do not use ranging hardware, remains a challenging problem, when considering the required location accuracy, energy expenditure and the duration of the localization phase. In this paper we propose a framework, called StarDust, for wireless sensor netwo... | [
"local",
"wireless sensor network",
"rang",
"sensor node",
"imag process",
"perform",
"corner-cube retro-reflector",
"aerial vehicl",
"scene label",
"consist",
"probabl",
"uniqu map",
"connect"
] | [
"P",
"P",
"P",
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"M",
"M",
"M",
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"U",
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"U"
] | StarDust: A Flexible Architecture for Passive Localization in Wireless Sensor Networks ∗ Radu Stoleru, Pascal Vicaire, Tian He†, John A. Stankovic Department of Computer Science, University of Virginia †Department of Computer Science and Engineering, University of Minnesota {stoleru, pv9f}@cs.
virginia.edu, tianhe@cs.u... | StarDust: A Flexible Architecture for Passive Localization in Wireless Sensor Networks *
Abstract
The problem of localization in wireless sensor networks where nodes do not use ranging hardware, remains a challenging problem, when considering the required location accuracy, energy expenditure and the duration of the lo... | StarDust: A Flexible Architecture for Passive Localization in Wireless Sensor Networks *
Abstract
The problem of localization in wireless sensor networks where nodes do not use ranging hardware, remains a challenging problem, when considering the required location accuracy, energy expenditure and the duration of the lo... | StarDust: A Flexible Architecture for Passive Localization in Wireless Sensor Networks *
Abstract
The problem of localization in wireless sensor networks where nodes do not use ranging hardware, remains a challenging problem, when considering the required location accuracy, energy expenditure and the duration of the lo... |
J-61 | ICE: An Iterative Combinatorial Exchange | We present the first design for an iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language that is concise and expressive for CEs. Bidders specify lower and upper bounds on their value for different trades. These bounds allow price discovery and useful preference elicitation in e... | [
"iter combinatori exchang",
"combinatori exchang",
"bid",
"trade",
"price",
"prefer elicit",
"doubl auction",
"combinatori auction",
"buyer and seller",
"tree-base bid languag",
"winner-determin",
"threshold payment",
"vcg"
] | [
"P",
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"M",
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] | ICE: An Iterative Combinatorial Exchange David C. Parkes∗ † Ruggiero Cavallo† Nick Elprin† Adam Juda† S´ebastien Lahaie† Benjamin Lubin† Loizos Michael† Jeffrey Shneidman† Hassan Sultan† ABSTRACT We present the first design for an iterative combinatorial exchange (ICE).
The exchange incorporates a tree-based bidding la... | ICE: An Iterative Combinatorial Exchange David C. Parkes * s Ruggiero Cavallos Nick Elprins Adam Judas S ´ ebastien Lahaies
ABSTRACT
We present the first design for an iterative combinatorial exchange (ICE).
The exchange incorporates a tree-based bidding language that is concise and expressive for CEs.
Bidders specify ... | ICE: An Iterative Combinatorial Exchange David C. Parkes * s Ruggiero Cavallos Nick Elprins Adam Judas S ´ ebastien Lahaies
ABSTRACT
We present the first design for an iterative combinatorial exchange (ICE).
The exchange incorporates a tree-based bidding language that is concise and expressive for CEs.
Bidders specify ... | ICE: An Iterative Combinatorial Exchange David C. Parkes * s Ruggiero Cavallos Nick Elprins Adam Judas S ´ ebastien Lahaies
ABSTRACT
We present the first design for an iterative combinatorial exchange (ICE).
The exchange incorporates a tree-based bidding language that is concise and expressive for CEs.
Bidders specify ... |
H-64 | Machine Learning for Information Architecture in a Large Governmental Website | This paper describes ongoing research into the application of machine learning techniques for improving access to governmental information in complex digital libraries. Under the auspices of the GovStat Project, our goal is to identify a small number of semantically valid concepts that adequately spans the intellectual... | [
"machin learn",
"inform architectur",
"machin learn techniqu",
"access",
"complex digit librari",
"digit librari",
"data-driven approach",
"supervis and unsupervis learn techniqu",
"bureau of labor statist",
"eigenvalu",
"bl collect",
"k-mean cluster",
"multiwai classif",
"interfac design"... | [
"P",
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] | Machine Learning for Information Architecture in a Large Governmental Website ∗ Miles Efron School of Information & Library Science CB#3360, 100 Manning Hall University of North Carolina Chapel Hill, NC 27599-3360 efrom@ils.unc.edu Jonathan Elsas School of Information & Library Science CB#3360, 100 Manning Hall Univers... | Machine Learning for Information Architecture in a Large Governmental Website *
ABSTRACT
This paper describes ongoing research into the application of machine learning techniques for improving access to governmental information in complex digital libraries.
Under the auspices of the GovStat Project, our goal is to iden... | Machine Learning for Information Architecture in a Large Governmental Website *
ABSTRACT
This paper describes ongoing research into the application of machine learning techniques for improving access to governmental information in complex digital libraries.
Under the auspices of the GovStat Project, our goal is to iden... | Machine Learning for Information Architecture in a Large Governmental Website *
ABSTRACT
This paper describes ongoing research into the application of machine learning techniques for improving access to governmental information in complex digital libraries.
Under the auspices of the GovStat Project, our goal is to iden... |
C-53 | Globally Synchronized Dead-Reckoning with Local Lag for Continuous Distributed Multiplayer Games | Dead-Reckoning (DR) is an effective method to maintain consistency for Continuous Distributed Multiplayer Games (CDMG). Since DR can filter most unnecessary state updates and improve the scalability of a system, it is widely used in commercial CDMG. However, DR cannot maintain high consistency, and this constrains its ... | [
"dead-reckon",
"local lag",
"multiplay game",
"consist",
"gs-dr-ll",
"network transmiss delai",
"time warp",
"accur state",
"correct",
"physic clock",
"usabl and fair",
"distribut multi-player game",
"continu replic applic"
] | [
"P",
"P",
"P",
"P",
"P",
"U",
"U",
"M",
"U",
"U",
"M",
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] | Globally Synchronized Dead-Reckoning with Local Lag for Continuous Distributed Multiplayer Games Yi Zhang1 , Ling Chen1, 2 , Gencai Chen1 1College of Computer Science, Zhejiang University, Hangzhou 310027, P.R. China 2School of Computer Science and IT, The University of Nottingham, Nottingham NG8 1BB, UK {m05zhangyi, l... | Globally Synchronized Dead-Reckoning with Local Lag for Continuous Distributed Multiplayer Games
ABSTRACT
Dead-Reckoning (DR) is an effective method to maintain consistency for Continuous Distributed Multiplayer Games (CDMG).
Since DR can filter most unnecessary state updates and improve the scalability of a system, it... | Globally Synchronized Dead-Reckoning with Local Lag for Continuous Distributed Multiplayer Games
ABSTRACT
Dead-Reckoning (DR) is an effective method to maintain consistency for Continuous Distributed Multiplayer Games (CDMG).
Since DR can filter most unnecessary state updates and improve the scalability of a system, it... | Globally Synchronized Dead-Reckoning with Local Lag for Continuous Distributed Multiplayer Games
ABSTRACT
Dead-Reckoning (DR) is an effective method to maintain consistency for Continuous Distributed Multiplayer Games (CDMG).
Since DR can filter most unnecessary state updates and improve the scalability of a system, it... |
J-63 | Negotiation-Range Mechanisms: Exploring the Limits of Truthful Efficient Markets | This paper introduces a new class of mechanisms based on negotiation between market participants. This model allows us to circumvent Myerson and Satterthwaite's impossibility result and present a bilateral market mechanism that is efficient, individually rational, incentive compatible and budget balanced in the single-... | [
"effici market",
"imposs result",
"individu ration",
"incent compat",
"effici truth market",
"negoti base mechan",
"buyer and seller",
"good exchang",
"negotiationrang market",
"negoti-rang mechan",
"real-world market environ",
"util",
"possibl agreement zone",
"mechan design"
] | [
"P",
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"R",
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"U",
"M",
"M",
"M",
"U",
"R",
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] | Negotiation-Range Mechanisms: Exploring the Limits of Truthful Efficient Markets Yair Bartal ∗ School of Computer Science and Engineering The Hebrew University of Jerusalem, Israel yair@cs.huji.ac.il Rica Gonen School of Computer Science and Engineering The Hebrew University of Jerusalem, Israel rgonen@cs.huji.ac.il Pi... | Negotiation-Range Mechanisms: Exploring the Limits of Truthful Efficient Markets
ABSTRACT
This paper introduces a new class of mechanisms based on negotiation between market participants.
This model allows us to circumvent Myerson and Satterthwaite's impossibility result and present a bilateral market mechanism that is... | Negotiation-Range Mechanisms: Exploring the Limits of Truthful Efficient Markets
ABSTRACT
This paper introduces a new class of mechanisms based on negotiation between market participants.
This model allows us to circumvent Myerson and Satterthwaite's impossibility result and present a bilateral market mechanism that is... | Negotiation-Range Mechanisms: Exploring the Limits of Truthful Efficient Markets
ABSTRACT
This paper introduces a new class of mechanisms based on negotiation between market participants.
This model allows us to circumvent Myerson and Satterthwaite's impossibility result and present a bilateral market mechanism that is... |
C-84 | Selfish Caching in Distributed Systems: A Game-Theoretic Analysis | We analyze replication of resources by server nodes that act selfishly, using a game-theoretic approach. We refer to this as the selfish caching problem. In our model, nodes incur either cost for replicating resources or cost for access to a remote replica. We show the existence of pure strategy Nash equilibria and inv... | [
"cach",
"distribut system",
"game-theoret approach",
"cach problem",
"remot replica",
"network topolog",
"nash equilibrium",
"peer-to-peer file system",
"demand distribut",
"anarchi price",
"instrument server",
"primal-dual techniqu",
"social util",
"submodular",
"group strategyproof",
... | [
"P",
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"M",
"M",
"R",
"M",
"U",
"M",
"U",
"U",
"U",
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] | Selfish Caching in Distributed Systems: A Game-Theoretic Analysis Byung-Gon Chun ∗ bgchun@cs.berkeley.edu Kamalika Chaudhuri † kamalika@cs.berkeley.edu Hoeteck Wee ‡ hoeteck@cs.berkeley.edu Marco Barreno § barreno@cs.berkeley.edu Christos H. Papadimitriou † christos@cs.berkeley.edu John Kubiatowicz ∗ kubitron@cs.berkel... | Selfish Caching in Distributed Systems: A Game-Theoretic Analysis
ABSTRACT
We analyze replication of resources by server nodes that act selfishly, using a game-theoretic approach.
We refer to this as the selfish caching problem.
In our model, nodes incur either cost for replicating resources or cost for access to a rem... | Selfish Caching in Distributed Systems: A Game-Theoretic Analysis
ABSTRACT
We analyze replication of resources by server nodes that act selfishly, using a game-theoretic approach.
We refer to this as the selfish caching problem.
In our model, nodes incur either cost for replicating resources or cost for access to a rem... | Selfish Caching in Distributed Systems: A Game-Theoretic Analysis
ABSTRACT
We analyze replication of resources by server nodes that act selfishly, using a game-theoretic approach.
We refer to this as the selfish caching problem.
In our model, nodes incur either cost for replicating resources or cost for access to a rem... |
J-62 | Weak Monotonicity Suffices for Truthfulness on Convex Domains | Weak monotonicity is a simple necessary condition for a social choice function to be implementable by a truthful mechanism. Roberts [10] showed that it is sufficient for all social choice functions whose domain is unrestricted. Lavi, Mu'alem and Nisan [6] proved the sufficiency of weak monotonicity for functions over o... | [
"weak monoton",
"truth",
"truth",
"convex domain",
"social choic function",
"individu prefer",
"truth implement",
"recognit algorithm",
"nonneg cycl properti",
"affin maxim",
"non-truth function",
"domin strategi",
"mechan design",
"strategyproof"
] | [
"P",
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"P",
"P",
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"U",
"R",
"U",
"U",
"U",
"M",
"U",
"M",
"U"
] | Weak Monotonicity Suffices for Truthfulness on Convex Domains Michael Saks ∗ Dept. of Mathematics Rutgers University 110 Frelinghuysen Road Piscataway, NJ, 08854 saks@math.rutgers.edu Lan Yu † Dept. of Computer Science Rutgers University 110 Frelinghuysen Road Piscataway, NJ, 08854 lanyu@paul.rutgers.edu ABSTRACT Weak ... | Weak Monotonicity Suffices for Truthfulness on Convex Domains
ABSTRACT
Weak monotonicity is a simple necessary condition for a social choice function to be implementable by a truthful mechanism.
Roberts [10] showed that it is sufficient for all social choice functions whose domain is unrestricted.
Lavi, Mu'alem and Nis... | Weak Monotonicity Suffices for Truthfulness on Convex Domains
ABSTRACT
Weak monotonicity is a simple necessary condition for a social choice function to be implementable by a truthful mechanism.
Roberts [10] showed that it is sufficient for all social choice functions whose domain is unrestricted.
Lavi, Mu'alem and Nis... | Weak Monotonicity Suffices for Truthfulness on Convex Domains
ABSTRACT
Weak monotonicity is a simple necessary condition for a social choice function to be implementable by a truthful mechanism.
Roberts [10] showed that it is sufficient for all social choice functions whose domain is unrestricted.
Lavi, Mu'alem and Nis... |
C-46 | TSAR: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs | Archival storage of sensor data is necessary for applications that query, mine, and analyze such data for interesting features and trends. We argue that existing storage systems are designed primarily for flat hierarchies of homogeneous sensor nodes and do not fully exploit the multi-tier nature of emerging sensor netw... | [
"interv skip graph",
"archiv",
"archiv storag",
"sensor data",
"distribut index structur",
"data separ",
"analysi",
"flood",
"geograph hash tabl",
"homogen architectur",
"multi-tier sensor network",
"flash storag",
"metada",
"spatial scope",
"interv tree",
"wireless sensor network",
... | [
"P",
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"M",
"M",
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] | TSAR: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs ∗ Peter Desnoyers, Deepak Ganesan, and Prashant Shenoy Department of Computer Science University of Massachusetts Amherst, MA 01003 pjd@cs.umass.edu, dganesan@cs.umass.edu, shenoy@cs.umass.edu ABSTRACT Archival storage of sensor data is necessary f... | TSAR: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs *
ABSTRACT
Archival storage of sensor data is necessary for applications that query, mine, and analyze such data for interesting features and trends.
We argue that existing storage systems are designed primarily for flat hierarchies of homogeneous ... | TSAR: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs *
ABSTRACT
Archival storage of sensor data is necessary for applications that query, mine, and analyze such data for interesting features and trends.
We argue that existing storage systems are designed primarily for flat hierarchies of homogeneous ... | TSAR: A Two Tier Sensor Storage Architecture Using Interval Skip Graphs *
ABSTRACT
Archival storage of sensor data is necessary for applications that query, mine, and analyze such data for interesting features and trends.
We argue that existing storage systems are designed primarily for flat hierarchies of homogeneous ... |
C-52 | Fairness in Dead-Reckoning based Distributed Multi-Player Games | In a distributed multi-player game that uses dead-reckoning vectors to exchange movement information among players, there is inaccuracy in rendering the objects at the receiver due to network delay between the sender and the receiver. The object is placed at the receiver at the position indicated by the dead-reckoning ... | [
"fair",
"dead-reckon",
"dead-reckon vector",
"accuraci",
"mean error",
"budget base algorithm",
"schedul algorithm",
"distribut multi-player game",
"quantiz",
"export error",
"bucket synchron",
"network delai",
"clock synchron"
] | [
"P",
"P",
"P",
"P",
"P",
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"U",
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] | Fairness in Dead-Reckoning based Distributed Multi-Player Games Sudhir Aggarwal Hemant Banavar Department of Computer Science Florida State University, Tallahassee, FL Email: {sudhir, banavar}@cs.
fsu.edu Sarit Mukherjee Sampath Rangarajan Center for Networking Research Bell Laboratories, Holmdel, NJ Email: {sarit, sam... | Fairness in Dead-Reckoning based Distributed Multi-Player Games
ABSTRACT
In a distributed multi-player game that uses dead-reckoning vectors to exchange movement information among players, there is inaccuracy in rendering the objects at the receiver due to network delay between the sender and the receiver.
The object i... | Fairness in Dead-Reckoning based Distributed Multi-Player Games
ABSTRACT
In a distributed multi-player game that uses dead-reckoning vectors to exchange movement information among players, there is inaccuracy in rendering the objects at the receiver due to network delay between the sender and the receiver.
The object i... | Fairness in Dead-Reckoning based Distributed Multi-Player Games
ABSTRACT
In a distributed multi-player game that uses dead-reckoning vectors to exchange movement information among players, there is inaccuracy in rendering the objects at the receiver due to network delay between the sender and the receiver.
The object i... |
H-98 | Using Asymmetric Distributions to Improve Text Classifier Probability Estimates | Text classifiers that give probability estimates are more readily applicable in a variety of scenarios. For example, rather than choosing one set decision threshold, they can be used in a Bayesian risk model to issue a run-time decision which minimizes a user-specified cost function dynamically chosen at prediction tim... | [
"text classifi",
"probabl estim",
"decis threshold",
"bayesian risk model",
"empir score distribut",
"parametr model",
"inform retriev",
"logist regress framework",
"posterior function",
"asymmetr laplac distribut",
"search engin retriev",
"symmetr distribut",
"asymmetr gaussian",
"maximum... | [
"P",
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] | Using Asymmetric Distributions to Improve Text Classifier Probability Estimates Paul N. Bennett Computer Science Dept. Carnegie Mellon University Pittsburgh, PA 15213 pbennett+@cs.cmu.edu ABSTRACT Text classifiers that give probability estimates are more readily applicable in a variety of scenarios.
For example, rather... | Using Asymmetric Distributions to Improve Text Classifier Probability Estimates
ABSTRACT
Text classifiers that give probability estimates are more readily applicable in a variety of scenarios.
For example, rather than choosing one set decision threshold, they can be used in a Bayesian risk model to issue a run-time dec... | Using Asymmetric Distributions to Improve Text Classifier Probability Estimates
ABSTRACT
Text classifiers that give probability estimates are more readily applicable in a variety of scenarios.
For example, rather than choosing one set decision threshold, they can be used in a Bayesian risk model to issue a run-time dec... | Using Asymmetric Distributions to Improve Text Classifier Probability Estimates
ABSTRACT
Text classifiers that give probability estimates are more readily applicable in a variety of scenarios.
For example, rather than choosing one set decision threshold, they can be used in a Bayesian risk model to issue a run-time dec... |
H-73 | Unified Utility Maximization Framework for Resource Selection | This paper presents a unified utility framework for resource selection of distributed text information retrieval. This new framework shows an efficient and effective way to infer the probabilities of relevance of all the documents across the text databases. With the estimated relevance information, resource selection c... | [
"resourc select",
"databas recommend",
"distribut document retriev",
"distribut inform retriev",
"distribut text inform retriev resourc select",
"feder search",
"hidden web content",
"resourc represent",
"retriev and result merg",
"logist transform model",
"semi-supervis learn",
"unifi util ma... | [
"P",
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] | Unified Utility Maximization Framework for Resource Selection Luo Si Language Technology Inst.
School of Compute Science Carnegie Mellon University Pittsburgh, PA 15213 lsi@cs.cmu.edu Jamie Callan Language Technology Inst.
School of Compute Science Carnegie Mellon University Pittsburgh, PA 15213 callan@cs.cmu.edu ABSTR... | Unified Utility Maximization Framework for Resource Selection
ABSTRACT
This paper presents a unified utility framework for resource selection of distributed text information retrieval.
This new framework shows an efficient and effective way to infer the probabilities of relevance of all the documents across the text da... | Unified Utility Maximization Framework for Resource Selection
ABSTRACT
This paper presents a unified utility framework for resource selection of distributed text information retrieval.
This new framework shows an efficient and effective way to infer the probabilities of relevance of all the documents across the text da... | Unified Utility Maximization Framework for Resource Selection
ABSTRACT
This paper presents a unified utility framework for resource selection of distributed text information retrieval.
This new framework shows an efficient and effective way to infer the probabilities of relevance of all the documents across the text da... |
H-42 | HITS Hits TREC: Exploring IR Evaluation Results with Network Analysis | We propose a novel method of analysing data gathered from TREC or similar information retrieval evaluation experiments. We define two normalized versions of average precision, that we use to construct a weighted bipartite graph of TREC systems and topics. We analyze the meaning of well known -- and somewhat generalized... | [
"hit",
"trec",
"ir evalu",
"network analysi",
"inform retriev evalu experi",
"weight bipartit graph",
"social network analysi",
"system-topic graph",
"hit algorithm",
"human assessor",
"mean averag precis",
"web search engin implement",
"link analysi techniqu",
"inlink",
"pagerank",
"s... | [
"P",
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] | HITS Hits TRECExploring IR Evaluation Results with Network Analysis Stefano Mizzaro Dept. of Mathematics and Computer Science University of Udine Via delle Scienze, 206 - 33100 Udine, Italy mizzaro@dimi.uniud.it Stephen Robertson Microsoft Research 7 JJ Thomson Avenue Cambridge CB3 0FB, UK ser@microsoft.com ABSTRACT We... | HITS Hits TREC--Exploring IR Evaluation Results with Network Analysis
ABSTRACT
We propose a novel method of analysing data gathered from TREC or similar information retrieval evaluation experiments.
We define two normalized versions of average precision, that we use to construct a weighted bipartite graph of TREC syste... | HITS Hits TREC--Exploring IR Evaluation Results with Network Analysis
ABSTRACT
We propose a novel method of analysing data gathered from TREC or similar information retrieval evaluation experiments.
We define two normalized versions of average precision, that we use to construct a weighted bipartite graph of TREC syste... | HITS Hits TREC--Exploring IR Evaluation Results with Network Analysis
ABSTRACT
We propose a novel method of analysing data gathered from TREC or similar information retrieval evaluation experiments.
We define two normalized versions of average precision, that we use to construct a weighted bipartite graph of TREC syste... |
H-95 | Handling Locations in Search Engine Queries | This paper proposes simple techniques for handling place references in search engine queries, an important aspect of geographical information retrieval. We address not only the detection, but also the disambiguation of place references, by matching them explicitly with concepts at an ontology. Moreover, when a query do... | [
"search engin queri",
"place refer",
"geograph inform retriev",
"geograph context",
"name entiti recognit algorithm",
"ner",
"disambigu result",
"textual string",
"web search engin",
"queri string",
"search queri",
"locationimplicit queri",
"geograph ontolog",
"geograph type express",
"t... | [
"P",
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] | Handling Locations in Search Engine Queries Bruno Martins, Mário J. Silva, Sérgio Freitas and Ana Paula Afonso Faculdade de Ciências da Universidade de Lisboa 1749-016 Lisboa, Portugal {bmartins,mjs,sfreitas,apa}@xldb.
di.fc.ul.pt ABSTRACT This paper proposes simple techniques for handling place references in search en... | Handling Locations in Search Engine Queries
ABSTRACT
This paper proposes simple techniques for handling place references in search engine queries, an important aspect of geographical information retrieval.
We address not only the detection, but also the disambiguation of place references, by matching them explicitly wi... | Handling Locations in Search Engine Queries
ABSTRACT
This paper proposes simple techniques for handling place references in search engine queries, an important aspect of geographical information retrieval.
We address not only the detection, but also the disambiguation of place references, by matching them explicitly wi... | Handling Locations in Search Engine Queries
ABSTRACT
This paper proposes simple techniques for handling place references in search engine queries, an important aspect of geographical information retrieval.
We address not only the detection, but also the disambiguation of place references, by matching them explicitly wi... |
H-81 | Distance Measures for MPEG-7-based Retrieval | In visual information retrieval the careful choice of suitable proximity measures is a crucial success factor. The evaluation presented in this paper aims at showing that the distance measures suggested by the MPEG-7 group for the visual descriptors can be beaten by general-purpose measures. Eight visual MPEG-7 descrip... | [
"distanc measur",
"distanc measur",
"mpeg-7",
"visual inform retriev",
"visual descriptor",
"media collect",
"perform indic",
"mpeg-7-base retriev",
"visual media",
"meehl index",
"human similar percept",
"predic-base model",
"content-base imag retriev",
"content-base video retriev",
"si... | [
"P",
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] | Distance Measures for MPEG-7-based Retrieval Horst Eidenberger Vienna University of Technology, Institute of Software Technology and Interactive Systems Favoritenstrasse 9-11 - A-1040 Vienna, Austria Tel. + 43-1-58801-18853 eidenberger@ims.tuwien.ac.at ABSTRACT In visual information retrieval the careful choice of suit... | Distance Measures for MPEG-7-based Retrieval
ABSTRACT
In visual information retrieval the careful choice of suitable proximity measures is a crucial success factor.
The evaluation presented in this paper aims at showing that the distance measures suggested by the MPEG-7 group for the visual descriptors can be beaten by... | Distance Measures for MPEG-7-based Retrieval
ABSTRACT
In visual information retrieval the careful choice of suitable proximity measures is a crucial success factor.
The evaluation presented in this paper aims at showing that the distance measures suggested by the MPEG-7 group for the visual descriptors can be beaten by... | Distance Measures for MPEG-7-based Retrieval
ABSTRACT
In visual information retrieval the careful choice of suitable proximity measures is a crucial success factor.
The evaluation presented in this paper aims at showing that the distance measures suggested by the MPEG-7 group for the visual descriptors can be beaten by... |
J-53 | A Price-Anticipating Resource Allocation Mechanism for Distributed Shared Clusters | In this paper we formulate the fixed budget resource allocation game to understand the performance of a distributed market-based resource allocation system. Multiple users decide how to distribute their budget (bids) among multiple machines according to their individual preferences to maximize their individual utility.... | [
"resourc alloc",
"distribut share cluster",
"util",
"effici",
"fair",
"simul",
"algorithm",
"bid mechan",
"price-anticip scheme",
"nash equilibrium",
"parallel",
"anarchi price",
"price-anticip mechan"
] | [
"P",
"P",
"P",
"P",
"P",
"P",
"U",
"R",
"R",
"M",
"U",
"U",
"R"
] | A Price-Anticipating Resource Allocation Mechanism for Distributed Shared Clusters Michal Feldman∗ mfeldman@sims.berkeley.edu Kevin Lai† kevin.lai@hp.com Li Zhang† l.zhang@hp.com ABSTRACT In this paper we formulate the fixed budget resource allocation game to understand the performance of a distributed marketbased reso... | A Price-Anticipating Resource Allocation Mechanism for Distributed Shared Clusters
ABSTRACT
In this paper we formulate the fixed budget resource allocation
game to understand the performance of a distributed marketbased resource allocation system.
Multiple users decide how to distribute their budget (bids) among multip... | A Price-Anticipating Resource Allocation Mechanism for Distributed Shared Clusters
ABSTRACT
In this paper we formulate the fixed budget resource allocation
game to understand the performance of a distributed marketbased resource allocation system.
Multiple users decide how to distribute their budget (bids) among multip... | A Price-Anticipating Resource Allocation Mechanism for Distributed Shared Clusters
ABSTRACT
In this paper we formulate the fixed budget resource allocation
game to understand the performance of a distributed marketbased resource allocation system.
Multiple users decide how to distribute their budget (bids) among multip... |
C-77 | Tracking Immediate Predecessors in Distributed Computations | A distributed computation is usually modeled as a partially ordered set of relevant events (the relevant events are a subset of the primitive events produced by the computation). An important causality-related distributed computing problem, that we call the Immediate Predecessors Tracking (IPT) problem, consists in ass... | [
"immedi predecessor",
"distribut comput",
"relev event",
"immedi predecessor track",
"transit reduct",
"hass diagram",
"timestamp",
"piggyback",
"control inform",
"ipt protocol",
"common global memori",
"messag transfer delai",
"vector clock",
"track causal",
"vector timestamp",
"chann... | [
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] | Tracking Immediate Predecessors in Distributed Computations Emmanuelle Anceaume Jean-Michel H´elary Michel Raynal IRISA, Campus Beaulieu 35042 Rennes Cedex, France FirstName.LastName@irisa.fr ABSTRACT A distributed computation is usually modeled as a partially ordered set of relevant events (the relevant events are a s... | Tracking Immediate Predecessors in Distributed Computations
ABSTRACT
A distributed computation is usually modeled as a partially ordered set of relevant events (the relevant events are a subset of the primitive events produced by the computation).
An important causality-related distributed computing problem, that we ca... | Tracking Immediate Predecessors in Distributed Computations
ABSTRACT
A distributed computation is usually modeled as a partially ordered set of relevant events (the relevant events are a subset of the primitive events produced by the computation).
An important causality-related distributed computing problem, that we ca... | Tracking Immediate Predecessors in Distributed Computations
ABSTRACT
A distributed computation is usually modeled as a partially ordered set of relevant events (the relevant events are a subset of the primitive events produced by the computation).
An important causality-related distributed computing problem, that we ca... |
J-47 | On the Computational Power of Iterative Auctions | We embark on a systematic analysis of the power and limitations of iterative combinatorial auctions. Most existing iterative combinatorial auctions are based on repeatedly suggesting prices for bundles of items, and querying the bidders for their demand under these prices. We prove a large number of results showing the... | [
"combinatori auction",
"price",
"bidder",
"demand queri",
"polynomi demand",
"ascend-price auction",
"bound",
"approxim factor",
"optim alloc",
"prefer elicit",
"ascend auction",
"commun complex"
] | [
"P",
"P",
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"U",
"U",
"U",
"U",
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"U"
] | On the Computational Power of Iterative Auctions∗ [Extended Abstract] Liad Blumrosen School of Engineering and Computer Science The Hebrew University of Jerusalem Jerusalem, Israel liad@cs.huji.ac.il Noam Nisan School of Engineering and Computer Science The Hebrew University of Jerusalem Jerusalem, Israel noam@cs.huji.... | On the Computational Power of Iterative Auctions *
ABSTRACT
We embark on a systematic analysis of the power and limitations of iterative combinatorial auctions.
Most existing iterative combinatorial auctions are based on repeatedly suggesting prices for bundles of items, and querying the bidders for their "demand" unde... | On the Computational Power of Iterative Auctions *
ABSTRACT
We embark on a systematic analysis of the power and limitations of iterative combinatorial auctions.
Most existing iterative combinatorial auctions are based on repeatedly suggesting prices for bundles of items, and querying the bidders for their "demand" unde... | On the Computational Power of Iterative Auctions *
ABSTRACT
We embark on a systematic analysis of the power and limitations of iterative combinatorial auctions.
Most existing iterative combinatorial auctions are based on repeatedly suggesting prices for bundles of items, and querying the bidders for their "demand" unde... |
C-62 | Network Monitors and Contracting Systems: Competition and Innovation | Today's Internet industry suffers from several well-known pathologies, but none is as destructive in the long term as its resistance to evolution. Rather than introducing new services, ISPs are presently moving towards greater commoditization. It is apparent that the network's primitive system of contracts does not ali... | [
"network monitor",
"monitor",
"contract system",
"contract",
"innov",
"commodit",
"incent",
"smart market",
"rout stagequ",
"verifi monitor",
"contract monitor",
"rout polici",
"clean-slate architectur design"
] | [
"P",
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] | Network Monitors and Contracting Systems: Competition and Innovation Paul Laskowski John Chuang UC Berkeley {paul,chuang}@sims.
berkeley.edu ABSTRACT Today``s Internet industry suffers from several well-known pathologies, but none is as destructive in the long term as its resistance to evolution.
Rather than introducin... | Network Monitors and Contracting Systems: Competition and Innovation
ABSTRACT
Today's Internet industry suffers from several well-known pathologies, but none is as destructive in the long term as its resistance to evolution.
Rather than introducing new services, ISPs are presently moving towards greater commoditization... | Network Monitors and Contracting Systems: Competition and Innovation
ABSTRACT
Today's Internet industry suffers from several well-known pathologies, but none is as destructive in the long term as its resistance to evolution.
Rather than introducing new services, ISPs are presently moving towards greater commoditization... | Network Monitors and Contracting Systems: Competition and Innovation
ABSTRACT
Today's Internet industry suffers from several well-known pathologies, but none is as destructive in the long term as its resistance to evolution.
Rather than introducing new services, ISPs are presently moving towards greater commoditization... |
J-52 | Hidden-Action in Multi-Hop Routing | In multi-hop networks, the actions taken by individual intermediate nodes are typically hidden from the communicating endpoints; all the endpoints can observe is whether or not the end-to-end transmission was successful. Therefore, in the absence of incentives to the contrary, rational (i.e., selfish) intermediate node... | [
"multi-hop",
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"multi-hop network",
"intermedi node",
"endpoint",
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"prioriti",
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"mechan",
"cost",
"failur caus",
"hidden action",
"moral hazard",
"princip-agent model",
"hidden-action",
"mechan design",
"moralhazard"
] | [
"P",
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"U",
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"U",
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] | Hidden-Action in Multi-Hop Routing Michal Feldman1 mfeldman@sims.berkeley.edu John Chuang1 chuang@sims.berkeley.edu Ion Stoica2 istoica@cs.berkeley.edu Scott Shenker2 shenker@icir.org 1 School of Information Management and Systems U.C. Berkeley 2 Computer Science Division U.C. Berkeley ABSTRACT In multi-hop networks, t... | Hidden-Action in Multi-Hop Routing
ABSTRACT
In multi-hop networks, the actions taken by individual intermediate nodes are typically hidden from the communicating endpoints; all the endpoints can observe is whether or not the end-to-end transmission was successful.
Therefore, in the absence of incentives to the contrary... | Hidden-Action in Multi-Hop Routing
ABSTRACT
In multi-hop networks, the actions taken by individual intermediate nodes are typically hidden from the communicating endpoints; all the endpoints can observe is whether or not the end-to-end transmission was successful.
Therefore, in the absence of incentives to the contrary... | Hidden-Action in Multi-Hop Routing
ABSTRACT
In multi-hop networks, the actions taken by individual intermediate nodes are typically hidden from the communicating endpoints; all the endpoints can observe is whether or not the end-to-end transmission was successful.
Therefore, in the absence of incentives to the contrary... |
C-76 | Assured Service Quality by Improved Fault Management Service-Oriented Event Correlation | The paradigm shift from device-oriented to service-oriented management has also implications to the area of event correlation. Today's event correlation mainly addresses the correlation of events as reported from management tools. However, a correlation of user trouble reports concerning services should also be perform... | [
"fault manag",
"event correl",
"process manag framework",
"servic manag",
"servic-orient event correl",
"servic level agreement",
"qo",
"custom servic manag",
"servic-orient manag",
"case-base reason",
"rule-base reason"
] | [
"P",
"P",
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] | Assured Service Quality by Improved Fault Management Service-Oriented Event Correlation Andreas Hanemann Munich Network Management Team Leibniz Supercomputing Center Barer Str.
21, D-80333 Munich, Germany hanemann@lrz.de Martin Sailer Munich Network Management Team University of Munich (LMU) Oettingenstr.
67, D-80538 M... | Assured Service Quality by Improved Fault Management Service-Oriented Event Correlation
ABSTRACT
The paradigm shift from device-oriented to service-oriented management has also implications to the area of event correlation.
Today's event correlation mainly addresses the correlation of events as reported from management... | Assured Service Quality by Improved Fault Management Service-Oriented Event Correlation
ABSTRACT
The paradigm shift from device-oriented to service-oriented management has also implications to the area of event correlation.
Today's event correlation mainly addresses the correlation of events as reported from management... | Assured Service Quality by Improved Fault Management Service-Oriented Event Correlation
ABSTRACT
The paradigm shift from device-oriented to service-oriented management has also implications to the area of event correlation.
Today's event correlation mainly addresses the correlation of events as reported from management... |
H-43 | Combining Content and Link for Classification using Matrix Factorization | The world wide web contains rich textual contents that are interconnected via complex hyperlinks. This huge database violates the assumption held by most of conventional statistical methods that each web page is considered as an independent and identical sample. It is thus difficult to apply traditional mining or learn... | [
"combin content and link",
"classif",
"matrix factor",
"web mine problem",
"link structur",
"content inform",
"author inform",
"joint factor",
"linkag adjac matrix",
"document-term matrix",
"low-dimension factor space",
"webkb and cora benchmark",
"relationship",
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... | [
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] | Combining Content and Link for Classification using Matrix Factorization Shenghuo Zhu Kai Yu Yun Chi Yihong Gong {zsh,kyu,ychi,ygong}@sv.
nec-labs.
com NEC Laboratories America, Inc. 10080 North Wolfe Road SW3-350 Cupertino, CA 95014, USA ABSTRACT The world wide web contains rich textual contents that are interconnecte... | Combining Content and Link for Classification using Matrix Factorization
ABSTRACT
The world wide web contains rich textual contents that are interconnected via complex hyperlinks.
This huge database violates the assumption held by most of conventional statistical methods that each web page is considered as an independe... | Combining Content and Link for Classification using Matrix Factorization
ABSTRACT
The world wide web contains rich textual contents that are interconnected via complex hyperlinks.
This huge database violates the assumption held by most of conventional statistical methods that each web page is considered as an independe... | Combining Content and Link for Classification using Matrix Factorization
ABSTRACT
The world wide web contains rich textual contents that are interconnected via complex hyperlinks.
This huge database violates the assumption held by most of conventional statistical methods that each web page is considered as an independe... |
H-69 | Ranking Web Objects from Multiple Communities | Vertical search is a promising direction as it leverages domain-specific knowledge and can provide more precise information for users. In this paper, we study the Web object-ranking problem, one of the key issues in building a vertical search engine. More specifically, we focus on this problem in cases when objects lac... | [
"rank",
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"imag search queri",
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] | Ranking Web Objects from Multiple Communities Le Chen ∗ Le.Chen@idiap.ch Lei Zhang leizhang@ microsoft.com Feng Jing fengjing@ microsoft.com Ke-Feng Deng kefengdeng@hotmail.com Wei-Ying Ma wyma@microsoft.com Microsoft Research Asia 5F, Sigma Center, No. 49, Zhichun Road Haidian District, Beijing, 100080, P R China ABST... | Ranking Web Objects from Multiple Communities
ABSTRACT
Vertical search is a promising direction as it leverages domainspecific knowledge and can provide more precise information for users.
In this paper, we study the Web object-ranking problem, one of the key issues in building a vertical search engine.
More specifical... | Ranking Web Objects from Multiple Communities
ABSTRACT
Vertical search is a promising direction as it leverages domainspecific knowledge and can provide more precise information for users.
In this paper, we study the Web object-ranking problem, one of the key issues in building a vertical search engine.
More specifical... | Ranking Web Objects from Multiple Communities
ABSTRACT
Vertical search is a promising direction as it leverages domainspecific knowledge and can provide more precise information for users.
In this paper, we study the Web object-ranking problem, one of the key issues in building a vertical search engine.
More specifical... |
H-41 | HITS on the Web: How does it Compare? | This paper describes a large-scale evaluation of the effectiveness of HITS in comparison with other link-based ranking algorithms, when used in combination with a state-of-the-art text retrieval algorithm exploiting anchor text. We quantified their effectiveness using three common performance measures: the mean recipro... | [
"hit",
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] | HITS on the Web: How does it Compare?
Marc Najork Microsoft Research 1065 La Avenida Mountain View, CA, USA najork@microsoft.com Hugo Zaragoza ∗ Yahoo! Research Barcelona Ocata 1 Barcelona 08003, Spain hugoz@es.yahoo-inc.com Michael Taylor Microsoft Research 7 J J Thompson Ave Cambridge CB3 0FB, UK mitaylor@microsoft.c... | SIGIR 2007 Proceedings Session 20: Link Analysis HITS on the Web: How does it Compare?
*
ABSTRACT
This paper describes a large-scale evaluation of the effectiveness of HITS in comparison with other link-based ranking algorithms, when used in combination with a state-ofthe-art text retrieval algorithm exploiting anchor ... | SIGIR 2007 Proceedings Session 20: Link Analysis HITS on the Web: How does it Compare?
*
ABSTRACT
This paper describes a large-scale evaluation of the effectiveness of HITS in comparison with other link-based ranking algorithms, when used in combination with a state-ofthe-art text retrieval algorithm exploiting anchor ... | SIGIR 2007 Proceedings Session 20: Link Analysis HITS on the Web: How does it Compare?
*
ABSTRACT
This paper describes a large-scale evaluation of the effectiveness of HITS in comparison with other link-based ranking algorithms, when used in combination with a state-ofthe-art text retrieval algorithm exploiting anchor ... |
H-82 | Downloading Textual Hidden Web Content Through Keyword Queries | An ever-increasing amount of information on the Web today is available only through search interfaces: the users have to type in a set of keywords in a search form in order to access the pages from certain Web sites. These pages are often referred to as the Hidden Web or the Deep Web. Since there are no static links to... | [
"hidden web",
"keyword queri",
"deep web",
"hidden web crawler",
"queri-select polici",
"crawl polici",
"textual databas",
"gener-frequenc polici",
"independ estim",
"adapt polici",
"hide web crawl",
"deep web crawler",
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] | [
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] | Downloading Textual Hidden Web Content Through Keyword Queries Alexandros Ntoulas UCLA Computer Science ntoulas@cs.ucla.edu Petros Zerfos UCLA Computer Science pzerfos@cs.ucla.edu Junghoo Cho UCLA Computer Science cho@cs.ucla.edu ABSTRACT An ever-increasing amount of information on the Web today is available only throu... | Downloading Textual Hidden Web Content Through Keyword Queries
ABSTRACT
An ever-increasing amount of information on the Web today is available only through search interfaces: the users have to type in a set of keywords in a search form in order to access the pages from certain Web sites.
These pages are often referred ... | Downloading Textual Hidden Web Content Through Keyword Queries
ABSTRACT
An ever-increasing amount of information on the Web today is available only through search interfaces: the users have to type in a set of keywords in a search form in order to access the pages from certain Web sites.
These pages are often referred ... | Downloading Textual Hidden Web Content Through Keyword Queries
ABSTRACT
An ever-increasing amount of information on the Web today is available only through search interfaces: the users have to type in a set of keywords in a search form in order to access the pages from certain Web sites.
These pages are often referred ... |
H-96 | A Study of Factors Affecting the Utility of Implicit Relevance Feedback | Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system. IRF is a new method of gathering information on user interest and, if IRF is to be used in operational IR systems, it is important to establish when it p... | [
"implicit relev feedback",
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"interact queri expans featur",
"high complex whilst",
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] | A Study of Factors Affecting the Utility of Implicit Relevance Feedback Ryen W. White Human-Computer Interaction Laboratory Institute for Advanced Computer Studies University of Maryland College Park, MD 20742, USA ryen@umd.edu Ian Ruthven Department of Computer and Information Sciences University of Strathclyde Glasgo... | A Study of Factors Affecting the Utility of Implicit Relevance Feedback
ABSTRACT
Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system.
IRF is a new method of gathering information on user interest and, if IR... | A Study of Factors Affecting the Utility of Implicit Relevance Feedback
ABSTRACT
Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system.
IRF is a new method of gathering information on user interest and, if IR... | A Study of Factors Affecting the Utility of Implicit Relevance Feedback
ABSTRACT
Implicit relevance feedback (IRF) is the process by which a search system unobtrusively gathers evidence on searcher interests from their interaction with the system.
IRF is a new method of gathering information on user interest and, if IR... |
C-48 | Multi-dimensional Range Queries in Sensor Networks | In many sensor networks, data or events are named by attributes. Many of these attributes have scalar values, so one natural way to query events of interest is to use a multidimensional range query. An example is: List all events whose temperature lies between 50◦ and 60◦ , and whose light levels lie between 10 and 15.... | [
"multi-dimension rang queri",
"dim",
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"geograph rout",
"queri cost",
"central index",
"distribut data structur",
"datacentr storag system",
"event insert",
"index techniqu",
"queri flood",
"effici correl",
"local-preserv ge... | [
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] | Multi-dimensional Range Queries in Sensor Networks∗ Xin Li † Young Jin Kim † Ramesh Govindan † Wei Hong ‡ ABSTRACT In many sensor networks, data or events are named by attributes.
Many of these attributes have scalar values, so one natural way to query events of interest is to use a multidimensional range query.
An exa... | Multi-dimensional Range Queries in Sensor Networks *
ABSTRACT
In many sensor networks, data or events are named by attributes.
Many of these attributes have scalar values, so one natural way to query events of interest is to use a multidimensional range query.
An example is: "List all events whose temperature lies betw... | Multi-dimensional Range Queries in Sensor Networks *
ABSTRACT
In many sensor networks, data or events are named by attributes.
Many of these attributes have scalar values, so one natural way to query events of interest is to use a multidimensional range query.
An example is: "List all events whose temperature lies betw... | Multi-dimensional Range Queries in Sensor Networks *
ABSTRACT
In many sensor networks, data or events are named by attributes.
Many of these attributes have scalar values, so one natural way to query events of interest is to use a multidimensional range query.
An example is: "List all events whose temperature lies betw... |
J-44 | Scouts, Promoters, and Connectors: The Roles of Ratings in Nearest Neighbor Collaborative Filtering | Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors. The quality of this aggregation process crucially affects the user experience and hence the effectiveness of recommenders in e-commerce. We present a novel study that disaggregates global recomme... | [
"scout",
"promot",
"connector",
"rate",
"nearest neighbor",
"collabor filter",
"recommend",
"recommend system",
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"collabor filter algorithm",
"purchas",
"opinion",
"list rank accuraci",
"user-base and item-base algorithm"
] | [
"P",
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] | Scouts, Promoters, and Connectors: The Roles of Ratings in Nearest Neighbor Collaborative Filtering Bharath Kumar Mohan Dept. of CSA Indian Institute of Science Bangalore 560 012, India mbk@csa.iisc.ernet.in Benjamin J. Keller Dept. of Computer Science Eastern Michigan University Ypsilanti, MI 48917, USA bkeller@emich.... | Scouts, Promoters, and Connectors: The Roles of Ratings in Nearest Neighbor Collaborative Filtering
ABSTRACT
Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors.
The quality of this aggregation process crucially affects the user experience and henc... | Scouts, Promoters, and Connectors: The Roles of Ratings in Nearest Neighbor Collaborative Filtering
ABSTRACT
Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors.
The quality of this aggregation process crucially affects the user experience and henc... | Scouts, Promoters, and Connectors: The Roles of Ratings in Nearest Neighbor Collaborative Filtering
ABSTRACT
Recommender systems aggregate individual user ratings into predictions of products or services that might interest visitors.
The quality of this aggregation process crucially affects the user experience and henc... |
C-74 | Adapting Asynchronous Messaging Middleware to ad-hoc Networking | The characteristics of mobile environments, with the possibility of frequent disconnections and fluctuating bandwidth, have forced a rethink of traditional middleware. In particular, the synchronous communication paradigms often employed in standard middleware do not appear to be particularly suited to ad-hoc environme... | [
"asynchron messag middlewar",
"asynchron commun",
"messag orient middlewar",
"epidem messag middlewar",
"java messag servic",
"mobil ad-hoc environ",
"messag-orient middlewar",
"epidem protocol",
"cross-layer",
"applic level rout",
"group commun",
"middlewar for mobil comput",
"mobil ad-hoc ... | [
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] | Adapting Asynchronous Messaging Middleware to ad-hoc Networking Mirco Musolesi Dept. of Computer Science, University College London Gower Street, London WC1E 6BT, United Kingdom m.musolesi@cs.ucl.ac.uk Cecilia Mascolo Dept. of Computer Science, University College London Gower Street, London WC1E 6BT, United Kingdom c.m... | Adapting Asynchronous Messaging Middleware to Ad Hoc Networking
ABSTRACT
The characteristics of mobile environments, with the possibility of frequent disconnections and fluctuating bandwidth, have forced a rethink of traditional middleware.
In particular, the synchronous communication paradigms often employed in standa... | Adapting Asynchronous Messaging Middleware to Ad Hoc Networking
ABSTRACT
The characteristics of mobile environments, with the possibility of frequent disconnections and fluctuating bandwidth, have forced a rethink of traditional middleware.
In particular, the synchronous communication paradigms often employed in standa... | Adapting Asynchronous Messaging Middleware to Ad Hoc Networking
ABSTRACT
The characteristics of mobile environments, with the possibility of frequent disconnections and fluctuating bandwidth, have forced a rethink of traditional middleware.
In particular, the synchronous communication paradigms often employed in standa... |
J-50 | Communication Complexity of Common Voting Rules | We determine the communication complexity of the common voting rules. The rules (sorted by their communication complexity from low to high) are plurality, plurality with runoff, single transferable vote (STV), Condorcet, approval, Bucklin, cup, maximin, Borda, Copeland, and ranked pairs. For each rule, we first give a ... | [
"commun",
"commun complex",
"complex",
"vote rule",
"vote",
"stv",
"maximin",
"protocol",
"prefer",
"prefer aggreg",
"resourc alloc",
"elicit problem"
] | [
"P",
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"P",
"P",
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] | Communication Complexity of Common Voting Rules∗ Vincent Conitzer Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA conitzer@cs.cmu.edu Tuomas Sandholm Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA sandholm@cs.cmu.edu ABSTRACT We determine the communication complexity of ... | Communication Complexity of Common Voting Rules *
ABSTRACT
We determine the communication complexity of the common voting rules.
The rules (sorted by their communication complexity from low to high) are plurality, plurality with runoff, single transferable vote (STV), Condorcet, approval, Bucklin, cup, maximin, Borda, ... | Communication Complexity of Common Voting Rules *
ABSTRACT
We determine the communication complexity of the common voting rules.
The rules (sorted by their communication complexity from low to high) are plurality, plurality with runoff, single transferable vote (STV), Condorcet, approval, Bucklin, cup, maximin, Borda, ... | Communication Complexity of Common Voting Rules *
ABSTRACT
We determine the communication complexity of the common voting rules.
The rules (sorted by their communication complexity from low to high) are plurality, plurality with runoff, single transferable vote (STV), Condorcet, approval, Bucklin, cup, maximin, Borda, ... |
C-75 | Composition of a DIDS by Integrating Heterogeneous IDSs on Grids | This paper considers the composition of a DIDS (Distributed Intrusion Detection System) by integrating heterogeneous IDSs (Intrusion Detection Systems). A Grid middleware is used for this integration. In addition, an architecture for this integration is proposed and validated through simulation. | [
"grid",
"distribut intrus detect system",
"intrus detect system",
"grid middlewar",
"heterogen intrus detect system",
"open grid servic architectur",
"comput grid",
"intrus detect servic",
"grid intrus detect architectur",
"id integr",
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"... | [
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] | Composition of a DIDS by Integrating Heterogeneous IDSs on Grids Paulo F. Silva and Carlos B. Westphall and Carla M. Westphall Network and Management Laboratory Department of Computer Science and Statistics Federal University of Santa Catarina, Florianópolis, Brazil Marcos D. Assunção Grid Computing and Distributed Sys... | Composition of a DIDS by Integrating Heterogeneous IDSs on Grids
ABSTRACT
This paper considers the composition of a DIDS (Distributed Intrusion Detection System) by integrating heterogeneous IDSs (Intrusion Detection Systems).
A Grid middleware is used for this integration.
In addition, an architecture for this integra... | Composition of a DIDS by Integrating Heterogeneous IDSs on Grids
ABSTRACT
This paper considers the composition of a DIDS (Distributed Intrusion Detection System) by integrating heterogeneous IDSs (Intrusion Detection Systems).
A Grid middleware is used for this integration.
In addition, an architecture for this integra... | Composition of a DIDS by Integrating Heterogeneous IDSs on Grids
ABSTRACT
This paper considers the composition of a DIDS (Distributed Intrusion Detection System) by integrating heterogeneous IDSs (Intrusion Detection Systems).
A Grid middleware is used for this integration.
In addition, an architecture for this integra... |
J-51 | Complexity of (Iterated) Dominance | We study various computational aspects of solving games using dominance and iterated dominance. We first study both strict and weak dominance (not iterated), and show that checking whether a given strategy is dominated by some mixed strategy can be done in polynomial time using a single linear program solve. We then mo... | [
"domin",
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"iter domin",
"strategi",
"elimin",
"bayesian game",
"normal form game",
"multiag system",
"self-interest agent",
"optim action",
"game theori",
"nash equilibrium"
] | [
"P",
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] | Complexity of (Iterated) Dominance∗ Vincent Conitzer Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA conitzer@cs.cmu.edu Tuomas Sandholm Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213, USA sandholm@cs.cmu.edu ABSTRACT We study various computational aspects of solving games us... | Complexity of (Iterated) Dominance *
ABSTRACT
We study various computational aspects of solving games using dominance and iterated dominance.
We first study both strict and weak dominance (not iterated), and show that checking whether a given strategy is dominated by some mixed strategy can be done in polynomial time u... | Complexity of (Iterated) Dominance *
ABSTRACT
We study various computational aspects of solving games using dominance and iterated dominance.
We first study both strict and weak dominance (not iterated), and show that checking whether a given strategy is dominated by some mixed strategy can be done in polynomial time u... | Complexity of (Iterated) Dominance *
ABSTRACT
We study various computational aspects of solving games using dominance and iterated dominance.
We first study both strict and weak dominance (not iterated), and show that checking whether a given strategy is dominated by some mixed strategy can be done in polynomial time u... |
J-45 | Empirical Mechanism Design: Methods, with Application to a Supply-Chain Scenario | Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings. We illustrate our approach with a design task from a supply-chain trading competition. Designers adopted several rule changes in order to deter particular procurement behavi... | [
"empir mechan",
"empir mechan design",
"paramet set",
"analysi",
"observ behavior",
"interest outcom featur",
"suppli-chain trade",
"two-stage game",
"player",
"particip",
"gametheoret model",
"nash equilibrium",
"game theori"
] | [
"P",
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"P",
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"U",
"U",
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] | Empirical Mechanism Design: Methods, with Application to a Supply-Chain Scenario Yevgeniy Vorobeychik, Christopher Kiekintveld, and Michael P. Wellman University of Michigan Computer Science & Engineering Ann Arbor, MI 48109-2121 USA { yvorobey, ckiekint, wellman }@umich.
edu ABSTRACT Our proposed methods employ learni... | Empirical Mechanism Design: Methods, with Application to a Supply-Chain Scenario
ABSTRACT
Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings.
We illustrate our approach with a design task from a supply-chain trading competiti... | Empirical Mechanism Design: Methods, with Application to a Supply-Chain Scenario
ABSTRACT
Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings.
We illustrate our approach with a design task from a supply-chain trading competiti... | Empirical Mechanism Design: Methods, with Application to a Supply-Chain Scenario
ABSTRACT
Our proposed methods employ learning and search techniques to estimate outcome features of interest as a function of mechanism parameter settings.
We illustrate our approach with a design task from a supply-chain trading competiti... |
C-61 | Authority Assignment in Distributed Multi-Player Proxy-based Games | We present a proxy-based gaming architecture and authority assignment within this architecture that can lead to better game playing experience in Massively Multi-player Online games. The proposed game architecture consists of distributed game clients that connect to game proxies (referred to as communication proxies) w... | [
"author assign",
"author",
"commun proxi",
"multi-player onlin game",
"latenc compens",
"artifici latenc",
"proxi-base game architectur",
"central-server architectur",
"first person shooter",
"role plai game",
"client authorit approach",
"cheat-proof mechan",
"mmog",
"distribut multi-playe... | [
"P",
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"U",
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"M",
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] | Authority Assignment in Distributed Multi-Player Proxy-based Games Sudhir Aggarwal Justin Christofoli Department of Computer Science Florida State University, Tallahassee, FL {sudhir, christof}@cs.
fsu.edu Sarit Mukherjee Sampath Rangarajan Center for Networking Research Bell Laboratories, Holmdel, NJ {sarit, sampath}@... | Authority Assignment in Distributed Multi-Player Proxy-based Games
ABSTRACT
We present a proxy-based gaming architecture and authority assignment within this architecture that can lead to better game playing experience in Massively Multi-player Online games.
The proposed game architecture consists of distributed game c... | Authority Assignment in Distributed Multi-Player Proxy-based Games
ABSTRACT
We present a proxy-based gaming architecture and authority assignment within this architecture that can lead to better game playing experience in Massively Multi-player Online games.
The proposed game architecture consists of distributed game c... | Authority Assignment in Distributed Multi-Player Proxy-based Games
ABSTRACT
We present a proxy-based gaming architecture and authority assignment within this architecture that can lead to better game playing experience in Massively Multi-player Online games.
The proposed game architecture consists of distributed game c... |
C-49 | Evaluating Opportunistic Routing Protocols with Large Realistic Contact Traces | Traditional mobile ad-hoc network (MANET) routing protocols assume that contemporaneous end-to-end communication paths exist between data senders and receivers. In some mobile ad-hoc networks with a sparse node population, an end-to-end communication path may break frequently or may not exist at any time. Many routing ... | [
"rout protocol",
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"simul",
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"frequent link break",
"end-to-end path",
"random mobil model",
"realist mobil trace",
"unicast",
"transfer probabl",
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"ep... | [
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] | Evaluating Opportunistic Routing Protocols with Large Realistic Contact Traces Libo Song and David F. Kotz Institute for Security Technology Studies (ISTS) Department of Computer Science, Dartmouth College, Hanover, NH, USA 03755 ABSTRACT Traditional mobile ad-hoc network (MANET) routing protocols assume that contempor... | Evaluating Opportunistic Routing Protocols with Large Realistic Contact Traces
ABSTRACT
Traditional mobile ad hoc network (MANET) routing protocols assume that contemporaneous end-to-end communication paths exist between data senders and receivers.
In some mobile ad hoc networks with a sparse node population, an end-to... | Evaluating Opportunistic Routing Protocols with Large Realistic Contact Traces
ABSTRACT
Traditional mobile ad hoc network (MANET) routing protocols assume that contemporaneous end-to-end communication paths exist between data senders and receivers.
In some mobile ad hoc networks with a sparse node population, an end-to... | Evaluating Opportunistic Routing Protocols with Large Realistic Contact Traces
ABSTRACT
Traditional mobile ad hoc network (MANET) routing protocols assume that contemporaneous end-to-end communication paths exist between data senders and receivers.
In some mobile ad hoc networks with a sparse node population, an end-to... |
H-97 | Feature Representation for Effective Action-Item Detection | E-mail users face an ever-growing challenge in managing their inboxes due to the growing centrality of email in the workplace for task assignment, action requests, and other roles beyond information dissemination. Whereas Information Retrieval and Machine Learning techniques are gaining initial acceptance in spam filte... | [
"action-item detect",
"e-mail",
"inform retriev",
"topic-driven text classif",
"text classif",
"n-gram",
"chi-squar featur select",
"featur select",
"svm",
"autom model select",
"embed cross-valid",
"text categor",
"genr-classif",
"e-mail prioriti rank",
"speech act identif",
"simpl fa... | [
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] | Feature Representation for Effective Action-Item Detection Paul N. Bennett Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 pbennett+@cs.cmu.edu Jaime Carbonell Language Technologies Institute.
Carnegie Mellon University Pittsburgh, PA 15213 jgc+@cs.cmu.edu ABSTRACT E-mail users face an ever-... | Feature Representation for Effective Action-Item Detection
ABSTRACT
E-mail users face an ever-growing challenge in managing their inboxes due to the growing centrality of email in the workplace for task assignment, action requests, and other roles beyond information dissemination.
Whereas Information Retrieval and Mach... | Feature Representation for Effective Action-Item Detection
ABSTRACT
E-mail users face an ever-growing challenge in managing their inboxes due to the growing centrality of email in the workplace for task assignment, action requests, and other roles beyond information dissemination.
Whereas Information Retrieval and Mach... | Feature Representation for Effective Action-Item Detection
ABSTRACT
E-mail users face an ever-growing challenge in managing their inboxes due to the growing centrality of email in the workplace for task assignment, action requests, and other roles beyond information dissemination.
Whereas Information Retrieval and Mach... |
H-83 | Estimating the Global PageRank of Web Communities | Localized search engines are small-scale systems that index a particular community on the web. They offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search capability over their relevant domains. One disadvantage such ... | [
"global pagerank",
"web commun",
"local search engin",
"algorithm",
"local domain",
"topic-specif domain",
"larg-scale search engin",
"link-base rank",
"subgraph",
"global graph",
"crawl problem",
"experiment"
] | [
"P",
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"P",
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] | Estimating the Global PageRank of Web Communities Jason V. Davis Dept. of Computer Sciences University of Texas at Austin Austin, TX 78712 jdavis@cs.utexas.edu Inderjit S. Dhillon Dept. of Computer Sciences University of Texas at Austin Austin, TX 78712 inderjit@cs.utexas.edu ABSTRACT Localized search engines are small... | Estimating the Global PageRank of Web Communities
ABSTRACT
Localized search engines are small-scale systems that index a particular community on the web.
They offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search cap... | Estimating the Global PageRank of Web Communities
ABSTRACT
Localized search engines are small-scale systems that index a particular community on the web.
They offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search cap... | Estimating the Global PageRank of Web Communities
ABSTRACT
Localized search engines are small-scale systems that index a particular community on the web.
They offer several benefits over their large-scale counterparts in that they are relatively inexpensive to build, and can provide more precise and complete search cap... |
H-54 | Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature | This paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effectiveness in retrieving biomedical literature. The effects of different types of domain-specific kno... | [
"passag extract",
"domain-specif knowledg",
"retriev model",
"keyword search",
"document collect",
"passag-level inform retriev",
"queri concept",
"conceptu ir model",
"passag map",
"aspect map",
"document map",
"document retriev",
"biomed document"
] | [
"P",
"P",
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"M",
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] | Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature Wei Zhou, Clement Yu Department of Computer Science University of Illinois at Chicago wzhou8@uic.edu, yu@cs.uic.edu Neil Smalheiser, Vetle Torvik Department of Psychiatry and Psychiatric Institute (MC912) University of Illinois at ... | Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature
ABSTRACT
This paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effect... | Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature
ABSTRACT
This paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effect... | Knowledge-intensive Conceptual Retrieval and Passage Extraction of Biomedical Literature
ABSTRACT
This paper presents a study of incorporating domain-specific knowledge (i.e., information about concepts and relationships between concepts in a certain domain) in an information retrieval (IR) system to improve its effect... |
H-40 | Cross-Lingual Query Suggestion Using Query Logs of Different Languages | Query suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs. Previously, the suggested terms are mostly in the same language of the input query. In this paper, we extend it to cross-lingual query suggestion (CLQS): for a query in one language, we suggest ... | [
"queri suggest",
"queri log",
"cross-languag inform retriev",
"keyword bid",
"search engin advertis",
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"monolingu queri suggest",
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] | [
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] | Cross-Lingual Query Suggestion Using Query Logs of Different Languages Wei Gao1* , Cheng Niu2 , Jian-Yun Nie3 , Ming Zhou2 , Jian Hu2 , Kam-Fai Wong1 , Hsiao-Wuen Hon2 1 The Chinese University of Hong Kong, Hong Kong, China {wgao, kfwong}@se.
cuhk.edu.hk 2 Microsoft Research Asia, Beijing, China {chengniu, mingzhou, ji... | Cross-Lingual Query Suggestion Using Query Logs of Different Languages
ABSTRACT
Query suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs.
Previously, the suggested terms are mostly in the same language of the input query.
In this paper, we extend it to... | Cross-Lingual Query Suggestion Using Query Logs of Different Languages
ABSTRACT
Query suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs.
Previously, the suggested terms are mostly in the same language of the input query.
In this paper, we extend it to... | Cross-Lingual Query Suggestion Using Query Logs of Different Languages
ABSTRACT
Query suggestion aims to suggest relevant queries for a given query, which help users better specify their information needs.
Previously, the suggested terms are mostly in the same language of the input query.
In this paper, we extend it to... |
H-44 | A Time Machine for Text Search | Text search over temporally versioned document collections such as web archives has received little attention as a research problem. As a consequence, there is no scalable and principled solution to search such a collection as of a specified time t. In this work, we address this shortcoming and propose an efficient sol... | [
"time machin",
"text search",
"version document collect",
"web archiv",
"time-travel text search",
"invert file index",
"tempor search",
"approxim tempor coalesc",
"collabor author environ",
"timestamp inform feed",
"document-content overlap",
"index rang-base valu",
"open sourc search-engin... | [
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] | A Time Machine for Text Search Klaus Berberich Srikanta Bedathur Thomas Neumann Gerhard Weikum Max-Planck Institute for Informatics Saarbr¨ucken, Germany {kberberi, bedathur, neumann, weikum}@mpi-inf.mpg.de ABSTRACT Text search over temporally versioned document collections such as web archives has received little atte... | A Time Machine for Text Search
ABSTRACT
Text search over temporally versioned document collections such as web archives has received little attention as a research problem.
As a consequence, there is no scalable and principled solution to search such a collection as of a specified time t.
In this work, we address this ... | A Time Machine for Text Search
ABSTRACT
Text search over temporally versioned document collections such as web archives has received little attention as a research problem.
As a consequence, there is no scalable and principled solution to search such a collection as of a specified time t.
In this work, we address this ... | A Time Machine for Text Search
ABSTRACT
Text search over temporally versioned document collections such as web archives has received little attention as a research problem.
As a consequence, there is no scalable and principled solution to search such a collection as of a specified time t.
In this work, we address this ... |
H-50 | An Outranking Approach for Rank Aggregation in Information Retrieval | Research in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.). In this paper, we focus on the rank aggregation problem, also called data fusion problem, where rankings of documents, searched into... | [
"outrank approach",
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"inform retriev",
"data fusion problem",
"data fusion",
"decis rule",
"multipl criterion framework",
"metasearch engin",
"combsum and combmnz strategi",
"majoritarian method",
"ir model",
"multipl criterium approach",
"outrank method"
] | [
"P",
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] | An Outranking Approach for Rank Aggregation in Information Retrieval Mohamed Farah Lamsade, Paris Dauphine University Place du Mal de Lattre de Tassigny 75775 Paris Cedex 16, France farah@lamsade.dauphine.fr Daniel Vanderpooten Lamsade, Paris Dauphine University Place du Mal de Lattre de Tassigny 75775 Paris Cedex 16, ... | An Outranking Approach for Rank Aggregation in Information Retrieval
ABSTRACT
Research in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.).
In this paper, we focus on the rank aggregation proble... | An Outranking Approach for Rank Aggregation in Information Retrieval
ABSTRACT
Research in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.).
In this paper, we focus on the rank aggregation proble... | An Outranking Approach for Rank Aggregation in Information Retrieval
ABSTRACT
Research in Information Retrieval usually shows performance improvement when many sources of evidence are combined to produce a ranking of documents (e.g., texts, pictures, sounds, etc.).
In this paper, we focus on the rank aggregation proble... |
H-87 | Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation | This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering. Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we evaluated these methods with non-stationary topics at various granular... | [
"robust",
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"topic detect",
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"cross-corpu paramet optim",
"relev feedback",
"inform retriev",
"topic track",
"statist learn",
"systemat method for paramet tune across mu... | [
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] | Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation Yiming Yang, Shinjae Yoo, Jian Zhang, Bryan Kisiel School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA ABSTRACT This paper reports a cross-benchmark evaluation of regularized logistic regression (LR... | Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation
ABSTRACT
This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering.
Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conference... | Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation
ABSTRACT
This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering.
Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conference... | Robustness of Adaptive Filtering Methods In a Cross-benchmark Evaluation
ABSTRACT
This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering.
Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conference... |
C-65 | Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors | The paper presents a wireless sensor network-based mobile countersniper system. A sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implements the acoustic detection and Time of Arrival (ToA) estimation algorithms on an FPGA. A 3-axis c... | [
"weapon classif",
"internod commun",
"sensorboard",
"self orient",
"data fusion",
"trajectori",
"rang",
"calib",
"weapon type",
"calib estim accuraci",
"calib estim",
"wireless sensor network-base mobil countersnip system",
"helmetmount microphon arrai",
"1degre trajectori precis",
"sens... | [
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] | Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors Peter Volgyesi, Gyorgy Balogh, Andras Nadas, Christopher B. Nash, Akos Ledeczi Institute for Software Integrated Systems, Vanderbilt University Nashville, TN, USA akos.ledeczi@vanderbilt.edu ABSTRACT The paper presents a wireless sen... | Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors
ABSTRACT
The paper presents a wireless sensor network-based mobile countersniper system.
A sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implemen... | Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors
ABSTRACT
The paper presents a wireless sensor network-based mobile countersniper system.
A sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implemen... | Shooter Localization and Weapon Classification with Soldier-Wearable Networked Sensors
ABSTRACT
The paper presents a wireless sensor network-based mobile countersniper system.
A sensor node consists of a helmetmounted microphone array, a COTS MICAz mote for internode communication and a custom sensorboard that implemen... |
J-41 | An Analysis of Alternative Slot Auction Designs for Sponsored Search | Billions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for placement alongside web search results. Slots for ad listings are allocated via an auction-style mechanism where the higher a merchant bids, the more likely his ad is to appear above other ads on the page. In this... | [
"altern slot auction design",
"sponsor search",
"sponsor search",
"ad list",
"rank by bid",
"rank by revenu",
"incomplet inform",
"second price",
"auction-style mechan",
"second-price payment rule",
"equilibrium multitud",
"diverg of econom valu",
"diverg of valu",
"combin market capit",
... | [
"P",
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] | An Analysis of Alternative Slot Auction Designs for Sponsored Search S´ebastien Lahaie ∗ Division of Engineering and Applied Sciences Harvard University, Cambridge, MA 02138 slahaie@eecs.harvard.edu ABSTRACT Billions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for place... | An Analysis of Alternative Slot Auction Designs for Sponsored Search
ABSTRACT
Billions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for placement alongside web search results.
Slots for ad listings are allocated via an auction-style mechanism where the higher a merchant ... | An Analysis of Alternative Slot Auction Designs for Sponsored Search
ABSTRACT
Billions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for placement alongside web search results.
Slots for ad listings are allocated via an auction-style mechanism where the higher a merchant ... | An Analysis of Alternative Slot Auction Designs for Sponsored Search
ABSTRACT
Billions of dollars are spent each year on sponsored search, a form of advertising where merchants pay for placement alongside web search results.
Slots for ad listings are allocated via an auction-style mechanism where the higher a merchant ... |
J-55 | From Optimal Limited To Unlimited Supply Auctions | We investigate the class of single-round, sealed-bid auctions for a set of identical items to bidders who each desire one unit. We adopt the worst-case competitive framework defined by [9, 5] that compares the profit of an auction to that of an optimal single-price sale of least two items. In this paper, we first deriv... | [
"unlimit suppli",
"auction",
"ratio",
"mechan design",
"competit analysi",
"benchmark",
"aggreg auction",
"bound",
"prefer",
"distribut"
] | [
"P",
"P",
"P",
"U",
"M",
"U",
"M",
"U",
"U",
"U"
] | From Optimal Limited To Unlimited Supply Auctions Jason D. Hartline Microsoft Research 1065 La Avenida) Mountain View, CA 94043 hartline@microsoft.com Robert McGrew ∗ Computer Science Department Stanford University Stanford, CA 94305 bmcgrew@stanford.edu ABSTRACT We investigate the class of single-round, sealed-bid auc... | From Optimal Limited To Unlimited Supply Auctions
ABSTRACT
We investigate the class of single-round, sealed-bid auctions for a set of identical items to bidders who each desire one unit.
We adopt the worst-case competitive framework defined by [9, 5] that compares the profit of an auction to that of an optimal single-p... | From Optimal Limited To Unlimited Supply Auctions
ABSTRACT
We investigate the class of single-round, sealed-bid auctions for a set of identical items to bidders who each desire one unit.
We adopt the worst-case competitive framework defined by [9, 5] that compares the profit of an auction to that of an optimal single-p... | From Optimal Limited To Unlimited Supply Auctions
ABSTRACT
We investigate the class of single-round, sealed-bid auctions for a set of identical items to bidders who each desire one unit.
We adopt the worst-case competitive framework defined by [9, 5] that compares the profit of an auction to that of an optimal single-p... |
C-71 | A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks | We propose ι, a novel index for evaluation of point-distribution. ι is the minimum distance between each pair of points normalized by the average distance between each pair of points. We find that a set of points that achieve a maximum value of ι result in a honeycomb structure. We propose that ι can serve as a good in... | [
"point-distribut index",
"sensor-group",
"wireless sensor network",
"honeycomb structur",
"surveil",
"redund",
"fault toler",
"node-deduct process",
"increment coverag qualiti algorithm",
"sleep configur protocol",
"sensor coverag",
"sensor group"
] | [
"P",
"P",
"P",
"P",
"U",
"U",
"U",
"M",
"M",
"U",
"M",
"M"
] | A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks Yangfan Zhou Haixuan Yang Michael R. Lyu Edith C.-H.
Ngai Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong, China {yfzhou, hxyang, lyu, chngai}@cse.
cuhk.edu.hk ABSTRACT We propose ι... | A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks
ABSTRACT
We propose t, a novel index for evaluation of point-distribution.
t is the minimum distance between each pair of points normalized by the average distance between each pair of points.
We find that a set of points that... | A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks
ABSTRACT
We propose t, a novel index for evaluation of point-distribution.
t is the minimum distance between each pair of points normalized by the average distance between each pair of points.
We find that a set of points that... | A Point-Distribution Index and Its Application to Sensor-Grouping in Wireless Sensor Networks
ABSTRACT
We propose t, a novel index for evaluation of point-distribution.
t is the minimum distance between each pair of points normalized by the average distance between each pair of points.
We find that a set of points that... |
J-69 | Robust Incentive Techniques for Peer-to-Peer Networks | Lack of cooperation (free riding) is one of the key problems that confronts today's P2P systems. What makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion, zero-cost identities, and traitors. To tackle these c... | [
"incent",
"p2p system",
"collus",
"reciproc decis function",
"reput",
"adapt stranger polici",
"selfinterest user",
"incent for cooper",
"game-theoret approach",
"maxflow-base algorithm",
"reciproc peer",
"mutual cooper",
"asymmetr payoff",
"generos",
"paramet nomin valu",
"whitewash",... | [
"P",
"P",
"P",
"P",
"P",
"P",
"M",
"R",
"U",
"U",
"M",
"M",
"U",
"U",
"U",
"U",
"R",
"M",
"U",
"U",
"U",
"R"
] | Robust Incentive Techniques for Peer-to-Peer Networks Michal Feldman1 mfeldman@sims.berkeley.edu Kevin Lai2 klai@hp.com Ion Stoica3 istoica@cs.berkeley.edu John Chuang1 chuang@sims.berkeley.edu 1 School of Information Management and Systems U.C. Berkeley 2 HP Labs 3 Computer Science Division U.C. Berkeley ABSTRACT Lack... | Robust Incentive Techniques for Peer-to-Peer Networks
Lack of cooperation (free riding) is one of the key problems that confronts today's P2P systems.
What makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion,... | Robust Incentive Techniques for Peer-to-Peer Networks
Lack of cooperation (free riding) is one of the key problems that confronts today's P2P systems.
What makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion,... | Robust Incentive Techniques for Peer-to-Peer Networks
Lack of cooperation (free riding) is one of the key problems that confronts today's P2P systems.
What makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion,... |
I-38 | Bidding Algorithms for a Distributed Combinatorial Auction | Distributed allocation and multiagent coordination problems can be solved through combinatorial auctions. However, most of the existing winner determination algorithms for combinatorial auctions are centralized. The PAUSE auction is one of a few efforts to release the auctioneer from having to do all the work (it might... | [
"bid algorithm",
"combinatori auction",
"distribut alloc",
"coordin",
"paus auction",
"task and resourc alloc",
"revenu-maxim solut",
"combinatori optim problem",
"agent",
"progress adapt user select environ",
"branch and bound search",
"search tree",
"branch-on-bid tree"
] | [
"P",
"P",
"P",
"P",
"P",
"M",
"M",
"M",
"U",
"U",
"M",
"M",
"U"
] | Bidding Algorithms for a Distributed Combinatorial Auction Benito Mendoza ∗ and Jos´e M. Vidal Computer Science and Engineering University of South Carolina Columbia, SC 29208 mendoza2@engr.sc.edu, vidal@sc.edu ABSTRACT Distributed allocation and multiagent coordination problems can be solved through combinatorial auct... | Bidding Algorithms for a Distributed Combinatorial Auction
ABSTRACT
Distributed allocation and multiagent coordination problems can be solved through combinatorial auctions.
However, most of the existing winner determination algorithms for combinatorial auctions are centralized.
The PAUSE auction is one of a few effort... | Bidding Algorithms for a Distributed Combinatorial Auction
ABSTRACT
Distributed allocation and multiagent coordination problems can be solved through combinatorial auctions.
However, most of the existing winner determination algorithms for combinatorial auctions are centralized.
The PAUSE auction is one of a few effort... | Bidding Algorithms for a Distributed Combinatorial Auction
ABSTRACT
Distributed allocation and multiagent coordination problems can be solved through combinatorial auctions.
However, most of the existing winner determination algorithms for combinatorial auctions are centralized.
The PAUSE auction is one of a few effort... |
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