Efficiently querying rdf data in triple stores Efficiently querying RDF data is being an important factor in applying Semantic Web technologies to real-world applications . In this context , many efforts have been made to store and query RDF data in relational database using particular schemas . In this paper , we propose a new scheme to store , index , and query RDF data in triple stores . Graph feature of RDF data is taken into considerations which might help reduce the join costs on the vertical database structure . We would partition RDF triples into overlapped groups , store them in a triple table with one more column of group identity , and build up a signature tree to index them . Based on this infrastructure , a complex RDF query is decomposed into multiple pieces of sub-queries which could be easily filtered into some RDF groups using signature tree index , and finally is evaluated with a composed and optimized SQL with specific constraints . We compare the performance of our method with prior art on typical queries over a large scaled LUBM and UOBM benchmark data ( more than 10 million triples ) . For some extreme cases , they can promote 3 to 4 orders of magnitude .

O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Generalized link suggestions via web site clustering Proactive link suggestion leads to improved user experience by allowing users to reach relevant information with fewer clicks , fewer pages to read , or simply faster because the right pages are prefetched just in time . In this paper we tackle two new scenarios for link suggestion , which were not covered in prior work owing to scarcity of historical browsing data . In the web search scenario , we propose a method for generating quick links - additional entry points into Web sites , which are shown for top search results for navigational queries - for tail sites , for which little browsing statistics is available . Beyond Web search , we also propose a method for link suggestion in general web browsing , effectively anticipating the next link to be followed by the user . Our approach performs clustering of Web sites in order to aggregate information across multiple sites , and enables relevant link suggestion for virtually any site , including tail sites and brand new sites for which little historical data is available . Empirical evaluation confirms the validity of our method using editorially labeled data as well as real-life search and browsing data from a major US search engine .

O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

assisted browsing
information search and retrieval
website clustering


Statistical properties of community structure in large social and information networks A large body of work has been devoted to identifying community structure in networks . A community is often though of as a set of nodes that has more connections between its members than to the remainder of the network . In this paper , we characterize as a function of size the statistical and structural properties of such sets of nodes . We define the network community profile plot , which characterizes the `` best '' possible community - according to the conductance measure - over a wide range of size scales , and we study over 70 large sparse real-world networks taken from a wide range of application domains . Our results suggest a significantly more refined picture of community structure in large real-world networks than has been appreciated previously . Our most striking finding is that in nearly every network dataset we examined , we observe tight but almost trivial communities at very small scales , and at larger size scales , the best possible communities gradually `` blend in '' with the rest of the network and thus become less `` community-like . '' This behavior is not explained , even at a qualitative level , by any of the commonly-used network generation models . Moreover , this behavior is exactly the opposite of what one would expect based on experience with and intuition from expander graphs , from graphs that are well-embeddable in a low-dimensional structure , and from small social networks that have served as testbeds of community detection algorithms . We have found , however , that a generative model , in which new edges are added via an iterative `` forest fire '' burning process , is able to produce graphs exhibiting a network community structure similar to our observations .

O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O

community structure
conductance
social networks


Topic modeling with network regularization In this paper , we formally define the problem of topic modeling with network structure ( TMN ) . We propose a novel solution to this problem , which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data . The proposed method bridges topic modeling and social network analysis , which leverages the power of both statistical topic models and discrete regularization . The output of this model well summarizes topics in text , maps a topic on the network , and discovers topical communities . With concrete selection of a topic model and a graph-based regularizer , our model can be applied to text mining problems such as author-topic analysis , community discovery , and spatial text mining . Empirical experiments on two different genres of data show that our approach is effective , which improves text-oriented methods as well as network-oriented methods . The proposed model is general ; it can be applied to any text collections with a mixture of topics and an associated network structure .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

graph-based regularization
information search and retrieval
social networks


Lessons from a Gnutella-web gateway We present a gateway between the WWW and the Gnutella peer-to-peer network that permits searchers on one side to be able to search and retrieve files on the other side of the gateway . This work improvesthe accessibility of files across different delivery platforms , making it possible to use a single search modality . We outline our design and implementation , present access statistics from a test deployment and discuss lessons learned .

O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

peer-to-peer
search engine
world wide web


Characterizing search intent diversity into click models Modeling a user 's click-through behavior in click logs is a challenging task due to the well-known position bias problem . Recent advances in click models have adopted the examination hypothesis which distinguishes document relevance from position bias . In this paper , we revisit the examination hypothesis and observe that user clicks can not be completely explained by relevance and position bias . Specifically , users with different search intents may submit the same query to the search engine but expect different search results . Thus , there might be a bias between user search intent and the query formulated by the user , which can lead to the diversity in user clicks . This bias has not been considered in previous works such as UBM , DBN and CCM . In this paper , we propose a new intent hypothesis as a complement to the examination hypothesis . This hypothesis is used to characterize the bias between the user search intent and the query in each search session . This hypothesis is very general and can be applied to most of the existing click models to improve their capacities in learning unbiased relevance . Experimental results demonstrate that after adopting the intent hypothesis , click models can better interpret user clicks and achieve a significant NDCG improvement .

O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Optimal marketing strategies over social networks We discuss the use of social networks in implementing viral marketing strategies . While influence maximization has been studied in this context ( see Chapter 24 of ( 10 ) ) , we study revenue maximization , arguably , a more natural objective . In our model , a buyer 's decision to buy an item is influenced by the set of other buyers that own the item and the price at which the item is offered . We focus on algorithmic question of finding revenue maximizing marketing strategies . When the buyers are completely symmetric , we can find the optimal marketing strategy in polynomial time . In the general case , motivated by hardness results , we investigate approximation algorithms for this problem . We identify a family of strategies called influence-and-exploit strategies that are based on the following idea : Initially influence the population by giving the item for free to carefully a chosen set of buyers . Then extract revenue from the remaining buyers using a ` greedy ' pricing strategy . We first argue why such strategies are reasonable and then show how to use recently developed set-function maximization techniques to find the right set of buyers to influence .

O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

monetizing social networks
submodular maximization


Advertising keyword generation using active learning This paper proposes an efficient relevance feedback based interactive model for keyword generation in sponsored search advertising . We formulate the ranking of relevant terms as a supervised learning problem and suggest new terms for the seed by leveraging user relevance feedback information . Active learning is employed to select the most informative samples from a set of candidate terms for user labeling . Experiments show our approach improves the relevance of generated terms significantly with little user effort required .

O B I O B I O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

active learning
keyword generation
sponsored search


Classifying web sites In this paper , we present a novel method for the classification of Web sites . This method exploits both structure and content of Web sites in order to discern their functionality . It allows for distinguishing between eight of the most relevant functional classes of Web sites . We show that a pre-classification of Web sites utilizing structural properties considerably improves a subsequent textual classification with standard techniques . We evaluate this approach on a dataset comprising more than 16,000 Web sites with about 20 million crawled and 100 million known Web pages . Our approach achieves an accuracy of 92 % for the coarse-grained classification of these Web sites .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

web measurement
web mining
web site classification


Information diffusion through blogspace We study the dynamics of information propagation in environments of low-overhead personal publishing , using a large collection of weblogs over time as our example domain . We characterize and model this collection at two levels . First , we present a macroscopic characterization of topic propagation through our corpus , formalizing the notion of long-running `` chatter '' topics consisting recursively of `` spike '' topics generated by outside world events , or more rarely , by resonances within the community . Second , we present a microscopic characterization of propagation from individual to individual , drawing on the theory of infectious diseases to model the flow . We propose , validate , and employ an algorithm to induce the underlying propagation network from a sequence of posts , and report on the results .

O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

blogs
graph theory
learning
memes
topic characterization
topic structure
viral propagation
viruses


On anonymizing query logs via token-based hashing In this paper we study the privacy preservation properties of aspecific technique for query log anonymization : token-based hashing . In this approach , each query is tokenized , and then a secure hash function is applied to each token . We show that statistical techniques may be applied to partially compromise the anonymization . We then analyze the specific risks that arise from these partial compromises , focused on revelation of identity from unambiguous names , addresses , and so forth , and the revelation of facts associated with an identity that are deemed to be highly sensitive . Our goal in this work is two fold : to show that token-based hashing is unsuitable for anonymization , and to present a concrete analysis of specific techniques that may be effective in breaching privacy , against which other anonymization schemes should be measured .

O O B I O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O


Two-stream indexing for spoken web search This paper presents two-stream processing of audio to index the audio content for Spoken Web search . The first stream indexes the meta-data associated with a particular audio document . The meta-data is usually very sparse , but accurate . This therefore results in a high-precision , low-recall index . The second stream uses a novel language-independent speech recognition to generate text to be indexed . Owing to the multiple languages and the noise in user generated content on the Spoken Web , the speech recognition accuracy of such systems is not high , thus they result in a low-precision , high-recall index . The paper attempts to use these two complementary streams to generate a combined index to increase the precision-recall performance in audio content search . The problem of audio content search is motivated by the real world implication of the Web in developing regions , where due to literacy and affordability issues , people use Spoken Web which consists of interconnected VoiceSites , which have content in audio . The experiments are based on more than 20,000 audio documents spanning over seven live VoiceSites and four different languages . The results suggest significant improvement over a meta-data-only or a speech-recognitiononly system , thus justifying the two-stream processing approach . Audio content search is a growing problem area and this paper wishes to be a first step to solving this at a large scale , across languages , in a Web context .

O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

audio search
information search and retrieval
mobile phone
multimedia information systems
world wide telecom web


Clustering query refinements by user intent We address the problem of clustering the refinements of a user search query . The clusters computed by our proposed algorithm can be used to improve the selection and placement of the query suggestions proposed by a search engine , and can also serve to summarize the different aspects of information relevant to the original user query . Our algorithm clusters refinements based on their likely underlying user intents by combining document click and session co-occurrence information . At its core , our algorithm operates by performing multiple random walks on a Markov graph that approximates user search behavior . A user study performed on top search engine queries shows that our clusters are rated better than corresponding clusters computed using approaches that use only document click or only sessions co-occurrence information .

O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Compressed web indexes Web search engines use indexes to efficiently retrieve pages containing specified query terms , as well as pages linking to specified pages . The problem of compressed indexes that permit such fast retrieval has a long history . We consider the problem : assuming that the terms in ( or links to ) a page are generated from a probability distribution , how well compactly can we build such indexes that allow fast retrieval ? Of particular interest is the case when the probability distribution is Zipfian ( or a similar power law ) , since these are the distributions that arise on the web . We obtain sharp bounds on the space requirement of Boolean indexes for text documents that follow Zipf 's law . In the process we develop a general technique that applies to any probability distribution , not necessarily a power law ; this is the first analysis of compression in indexes under arbitrary distributions . Our bounds lead to quantitative versions of rules of thumb that are folklore in indexing . Our experiments on several document collections show that the distribution of terms appears to follow a double-Pareto law rather than Zipf 's law . Despite widely varying sets of documents , the index sizes observed in the experiments conform well to our theoretical predictions .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

double-pareto
index size
information search and retrieval


OntoTrix : a hybrid visualization for populated ontologies Most Semantic Web data visualization tools structure the representation according to the concept definitions and interrelations that constitute the ontology 's vocabulary . Instances are often treated as somewhat peripheral information , when considered at all . These instances , that populate ontologies , represent an essential part of any knowledge base , and are often orders of magnitude more numerous than the concept definitions that give them machine-processable meaning . We present a visualization technique designed to enable users to visualize large instance sets and the relations that connect them . This hybrid visualization uses both node-link and adjacency matrix representations of graphs to visualize different parts of the data depending on their semantic and local structural properties , exploiting ontological knowledge to drive the graph layout . The representation is embedded in an environment that features advanced interaction techniques for easy navigation , including support for smooth continuous zooming and coordinated views .

O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

exploratory visualization
matrices
user interfaces


Beyond PageRank : machine learning for static ranking Since the publication of Brin and Page 's paper on PageRank , many in the Web community have depended on PageRank for the static ( query-independent ) ordering of Web pages . We show that we can significantly outperform PageRank using features that are independent of the link structure of the Web . We gain a further boost in accuracy by using data on the frequency at which users visit Web pages . We use RankNet , a ranking machine learning algorithm , to combine these and other static features based on anchor text and domain characteristics . The resulting model achieves a static ranking pairwise accuracy of 67.3 % ( vs. 56.7 % for PageRank or 50 % for random ) .

O B O O B O B I O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B O O O O O O O

learning
pagerank
ranknet
static ranking

information search and retrieval
relevance
search engines


SRing : a structured non dht p2p overlay supporting string range queries This paper presents SRing , a structured non DHT P2P overlay that efficiently supports exact and range queries on multiple attribute values . In SRing , all attribute values are interpreted as strings formed by a base alphabet and are published in the lexicographical order . Two virtual rings are built : N-ring is built in a skip-list way for range partition and queries ; D-ring is built in a small-world way for the construction of N-ring . A leave-and-join based load balancing method is used to balance range overload in the network with heterogeneous nodes .

O O O O O O B O O O B I O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O


Finding hierarchy in directed online social networks Social hierarchy and stratification among humans is a well studied concept in sociology . The popularity of online social networks presents an opportunity to study social hierarchy for different types of networks and at different scales . We adopt the premise that people form connections in a social network based on their perceived social hierarchy ; as a result , the edge directions in directed social networks can be leveraged to infer hierarchy . In this paper , we define a measure of hierarchy in a directed online social network , and present an efficient algorithm to compute this measure . We validate our measure using ground truth including Wikipedia notability score . We use this measure to study hierarchy in several directed online social networks including Twitter , Delicious , YouTube , Flickr , LiveJournal , and curated lists of several categories of people based on different occupations , and different organizations . Our experiments on different online social networks show how hierarchy emerges as we increase the size of the network . This is in contrast to random graphs , where the hierarchy decreases as the network size increases . Further , we show that the degree of stratification in a network increases very slowly as we increase the size of the graph .

O B O O O B I O B O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B I O O O O O B O O O O O O O O B O B O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O B O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Duplicate detection in click streams We consider the problem of finding duplicates in data streams . Duplicate detection in data streams is utilized in various applications including fraud detection . We develop a solution based on Bloom Filters ( 9 ) , and discuss the space and time requirements for running the proposed algorithm in both the contexts of sliding , and landmark stream windows . We run a comprehensive set of experiments , using both real and synthetic click streams , to evaluate the performance of the proposed solution . The results demonstrate that the proposed solution yields extremely low error rates .

B I O O O O O O O O O O O B I O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

advertising networks
approximate queries


Visualizing tags over time We consider the problem of visualizing the evolution of tags within the Flickr ( flickr.com ) online image sharing community . Any user of the Flickr service may append a tag to any photo in the system . Over the past year , users have on average added over a million tags each week . Understanding the evolution of these tags over time is therefore a challenging task . We present a new approach based on a characterization of the most interesting tags associated with a sliding interval of time . An animation provided via Flash in a web browser allows the user to observe and interact with the interesting tags as they evolve over time . New algorithms and data structures are required to support the efficient generation of this visualization . We combine a novel solution to an interval covering problem with extensions to previous work on score aggregation in order to create an efficient backend system capable of producing visualizations at arbitrary scales on this large dataset in real time .

O B O O O O O O O O O O O B O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

flickr
interval covering
tags
visualization

information search and retrieval
social media
temporal evolution


Handling forecast errors while bidding for display advertising Most of the online advertising today is sold via an auction , which requires the advertiser to respond with a valid bid within a fraction of a second . As such , most advertisers employ bidding agents to submit bids on their behalf . The architecture of such agents typically has ( 1 ) an offline optimization phase which incorporates the bidder 's knowledge about the market and ( 2 ) an online bidding strategy which simply executes the offline strategy . The online strategy is typically highly dependent on both supply and expected price distributions , both of which are forecast using traditional machine learning methods . In this work we investigate the optimum strategy of the bidding agent when faced with incorrect forecasts . At a high level , the agent can invest resources in improving the forecasts , or can tighten the loop between successive offline optimization cycles in order to detect errors more quickly . We show analytically that the latter strategy , while simple , is extremely effective in dealing with forecast errors , and confirm this finding with experimental evaluations .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

ad exchanges
adaptive bidding
applications
general


Soundness proof of Z semantics of OWL using institutions The correctness of the Z semantics of OWL is the theoretical foundation of using software engineering techniques to verify Web ontologies . As OWL and Z are based on different logical systems , we use institutions to represent their underlying logical systems and use institution morphisms to prove the correctness of the Z semantics for OWL DL .

O O O B O O B O O O O O O B O O B O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O B O O O O O O O B O O B O O


Fully automatic wrapper generation for search engines When a query is submitted to a search engine , the search engine returns a dynamically generated result page containing the result records , each of which usually consists of a link to and\/or snippet of a retrieved Web page . In addition , such a result page often also contains information irrelevant to the query , such as information related to the hosting site of the search engine and advertisements . In this paper , we present a technique for automatically producing wrappers that can be used to extract search result records from dynamically generated result pages returned by search engines . Automatic search result record extraction is very important for many applications that need to interact with search engines such as automatic construction and maintenance of metasearch engines and deep Web crawling . The novel aspect of the proposed technique is that it utilizes both the visual content features on the result page as displayed on a browser and the HTML tag structures of the HTML source file of the result page . Experimental results indicate that this technique can achieve very high extraction accuracy .

O O B I O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Answering relationship queries on the web Finding relationships between entities on the Web , e.g. , the connections between different places or the commonalities of people , is a novel and challenging problem . Existing Web search engines excel in keyword matching and document ranking , but they can not well handle many relationship queries . This paper proposes a new method for answering relationship queries on two entities . Our method first respectively retrieves the top Web pages for either entity from a Web search engine . It then matches these Web pages and generates an ordered list of Web page pairs . Each Web page pair consists of one Web page for either entity . The top ranked Web page pairs are likely to contain the relationships between the two entities . One main challenge in the ranking process is to effectively filter out the large amount of noise in the Web pages without losing much useful information . To achieve this , our method assigns appropriate weights to terms in Web pages and intelligently identifies the potential connecting terms that capture the relationships between the two entities . Only those top potential connecting terms with large weights are used to rank Web page pairs . Finally , the top ranked Web page pairs are presented to the searcher . For each such pair , the query terms and the top potential connecting terms are properly highlighted so that the relationships between the two entities can be easily identified . We implemented a prototype on top of the Google search engine and evaluated it under a wide variety of query scenarios . The experimental results show that our method is effective at finding important relationships with low overhead .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Visitor awareness in the web

ambient media
augmented reality
web activity
web awareness
web-based interaction


Implementing optimal outcomes in social computing : a game-theoretic approach In many social computing applications such as online Q&A forums , the best contribution for each task receives some high reward , while all remaining contributions receive an identical , lower reward irrespective of their actual qualities . Suppose a mechanism designer ( site owner ) wishes to optimize an objective that is some function of the number and qualities of received contributions . When potential contributors are { \ em strategic } agents , who decide whether to contribute or not to selfishly maximize their own utilities , is such a `` best contribution '' mechanism , Mb , adequate to implement an outcome that is optimal for the mechanism designer ? We first show that in settings where a contribution 's value is determined primarily by an agent 's expertise , and agents only strategically choose whether to contribute or not , contests can implement optimal outcomes : for any reasonable objective , the rewards for the best and remaining contributions in Mb can always be chosen so that the outcome in the unique symmetric equilibrium of Mb maximizes the mechanism designer 's utility . We also show how the mechanism designer can learn these optimal rewards when she does not know the parameters of the agents ' utilities , as might be the case in practice . We next consider settings where a contribution 's value depends on both the contributor 's expertise as well as her effort , and agents endogenously choose how much effort to exert in addition to deciding whether to contribute . Here , we show that optimal outcomes can never be implemented by contests if the system can rank the qualities of contributions perfectly . However , if there is noise in the contributions ' rankings , then the mechanism designer can again induce agents to follow strategies that maximize his utility . Thus imperfect rankings can actually help achieve implementability of optimal outcomes when effort is endogenous and influences quality .

O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

game theory
implementation
online q&a forums
user generated content


SafeVchat : detecting obscene content and misbehaving users in online video chat services Online video chat services such as Chatroulette , Omegle , and vChatter that randomly match pairs of users in video chat sessions are fast becoming very popular , with over a million users per month in the case of Chatroulette . A key problem encountered in such systems is the presence of flashers and obscene content . This problem is especially acute given the presence of underage minors in such systems . This paper presents SafeVchat , a novel solution to the problem of flasher detection that employs an array of image detection algorithms . A key contribution of the paper concerns how the results of the individual detectors are fused together into an overall decision classifying the user as misbehaving or not , based on Dempster-Shafer Theory . The paper introduces a novel , motion-based skin detection method that achieves significantly higher recall and better precision . The proposed methods have been evaluated over real-world data and image traces obtained from Chatroulette.com .

O O O O O O O O O B I I O B I I O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Growing parallel paths for entity-page discovery In this paper , we use the structural and relational information on the Web to find entity-pages . Specifically , given a Web site and an entity-page ( e.g. , department and faculty member homepage ) we seek to find all of the entity-pages of the same type ( e.g. , all faculty members in the department ) . To do this , we propose a web structure mining method which grows parallel paths through the web graph and DOM trees . We show that by utilizing these parallel paths we can efficiently discover all entity-pages of the same type . Finally , we demonstrate the accuracy of our method with a case study on various domains .

O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Matchbox : large scale online bayesian recommendations We present a probabilistic model for generating personalised recommendations of items to users of a web service . The Matchbox system makes use of content information in the form of user and item meta data in combination with collaborative filtering information from previous user behavior in order to predict the value of an item for a user . Users and items are represented by feature vectors which are mapped into a low-dimensional ` trait space ' in which similarity is measured in terms of inner products . The model can be trained from different types of feedback in order to learn user-item preferences . Here we present three alternatives : direct observation of an absolute rating each user gives to some items , observation of a binary preference ( like \/ do n't like ) and observation of a set of ordinal ratings on a user-specific scale . Efficient inference is achieved by approximate message passing involving a combination of Expectation Propagation ( EP ) and Variational Message Passing . We also include a dynamics model which allows an item 's popularity , a user 's taste or a user 's personal rating scale to drift over time . By using Assumed-Density Filtering ( ADF ) for training , the model requires only a single pass through the training data . This is an on-line learning algorithm capable of incrementally taking account of new data so the system can immediately reflect the latest user preferences . We evaluate the performance of the algorithm on the MovieLens and Netflix data sets consisting of approximately 1,000,000 and 100,000,000 ratings respectively . This demonstrates that training the model using the on-line ADF approach yields state-of-the-art performance with the option of improving performance further if computational resources are available by performing multiple EP passes over the training data .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

advertising
bayesian inference
machine learning
online services
recommender system


Personal DJ , an architecture for personalised content delivery

internet
moods
personalisation
radio
user evaluation


Seeing the whole in parts : text summarization for web browsing on handheld devices

handheld computers
interaction styles
mobile computing
pda
personal digital assistant
portable devices
ubiquitous computing
wap
wireless computing


The social honeypot project : protecting online communities from spammers We present the conceptual framework of the Social Honeypot Project for uncovering social spammers who target online communities and initial empirical results from Twitter and MySpace . Two of the key components of the Social Honeypot Project are : ( 1 ) The deployment of social honeypots for harvesting deceptive spam profiles from social networking communities ; and ( 2 ) Statistical analysis of the properties of these spam profiles for creating spam classifiers to actively filter out existing and new spammers .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O B O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O


How semantics make better wikis Wikis are popular collaborative hypertext authoring environments , but they neither support structured access nor information reuse . Adding semantic annotations helps to address these limitations . We present an architecture for Semantic Wikis and discuss design decisions including structured access , views , and annotation language . We present our prototype SemperWiki that implements this architecture .

O O O O B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O

information access
semantic annotation
semantic web


Browsing fatigue in handhelds : semantic bookmarking spells relief Focused Web browsing activities such as periodically looking up headline news , weather reports , etc. , which require only selective fragments of particular Web pages , can be made more efficient for users of limited-display-size handheld mobile devices by delivering only the target fragments . Semantic bookmarks provide a robust conceptual framework for recording and retrieving such targeted content not only from the specific pages used in creating the bookmarks but also from any user-specified page with similar content semantics . This paper describes a technique for realizing semantic bookmarks by coupling machine learning with Web page segmentation to create a statistical model of the bookmarked content . These models are used to identify and retrieve the bookmarked content from Web pages that share a common content domain . In contrast to ontology-based approaches where semantic bookmarks are limited to available concepts in the ontology , the learning-based approach allows users to bookmark ad-hoc personalized semantic concepts to effectively target content that fits the limited display of handhelds . User evaluation measuring the effectiveness of a prototype implementation of learning-based semantic bookmarking at reducing browsing fatigue in handhelds is provided .

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O

handheld device content adaptation
web page partitioning


Video quality estimation for internet streaming


Logical structure based semantic relationship extraction from semi-structured documents Addressed in this paper is the issue of semantic relationship extraction from semi-structured documents . Many research efforts have been made so far on the semantic information extraction . However , much of the previous work focuses on detecting ` isolated ' semantic information by making use of linguistic analysis or linkage information in web pages and limited research has been done on extracting semantic relationship from the semi-structured documents . In this paper , we propose a method for semantic relationship extraction by using the logical information in the semi-structured document ( semi-structured document usually has various types of structure information , e.g. a semi-structured document may be hierarchical laid out ) . To the best of our knowledge , extracting semantic relationships by using logical information has not been investigated previously . A probabilistic approach has been proposed in the paper . Features used in the probabilistic model have been defined .

B I O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

logical structure
relationship extraction


Mapping XML instances For XML-based applications in general and B2B applications in particular , mapping between differently structured XML documents , to enable exchange of data , is a basic problem . A generic solution to the problem is of interest and desirable both in an academic and practical sense . We present a case study of the problem that arises in an XML based project , which involves mapping of different XML schemas to each other . We describe our approach to solving the problem , its advantages and limitations . We also compare and contrast our approach with previously known approaches and commercially available software solutions .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Batch rekeying for secure group communications

group key management
security and protection


Persistence in web based collaborations We outline work on web based support for group creativity . We focus on a study of the effect persistence of participants ' musical contributions has on their mutual engagement .

O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O

collaboration
group and organization interfaces
hci
music
user interfaces


Instance-based probabilistic reasoning in the semantic web Most of the approaches for dealing with uncertainty in the Semantic Web rely on the principle that this uncertainty is already asserted . In this paper , we propose a new approach to learn and reason about uncertainty in the Semantic Web . Using instance data , we learn the uncertainty of an OWL ontology , and use that information to perform probabilistic reasoning on it . For this purpose , we use Markov logic , a new representation formalism that combines logic with probabilistic graphical models .

O B I O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O

markov logic
probabilistic reasoning
semantic web

uncertainty, fuzzy, and probabilistic reasoning


Spatial variation in search engine queries Local aspects of Web search - associating Web content and queries with geography - is a topic of growing interest . However , the underlying question of how spatial variation is manifested in search queries is still not well understood . Here we develop a probabilistic framework for quantifying such spatial variation ; on complete Yahoo ! query logs , we find that our model is able to localize large classes of queries to within a few miles of their natural centers based only on the distribution of activity for the query . Our model provides not only an estimate of a query 's geographic center , but also a measure of its spatial dispersion , indicating whether it has highly local interest or broader regional or national appeal . We also show how variations on our model can track geographically shifting topics over time , annotate a map with each location 's `` distinctive queries '' , and delineate the `` spheres of influence '' for competing queries in the same general domain .

O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Anchor-based proximity measures We present a family of measures of proximity of an arbitrary node in a directed graph to a pre-specified subset of nodes , called the anchor . Our measures are based on three different propagation schemesand two different uses of the connectivity structure of the graph . We consider a web-specific application of the above measures with two disjoint anchors - good and bad web pages - and study the accuracy of these measures in this context .

O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

harmonic rank
link propagation
miscellaneous


Multimedia meets computer graphics in SMIL2 .0 : a time model for the web Multimedia scheduling models provide a rich variety of tools for managing the synchronization of media like video and audio , but generally have an inflexible model for time itself . In contrast , modern animation models in the computer graphics community generally lack tools for synchronization and structural time , but allow for a flexible concept of time , including variable pacing , acceleration and deceleration and other tools useful for controlling and adapting animation behaviors . Multimedia authors have been forced to choose one set of features over the others , limiting the range of presentations they can create . Some programming models addressed some of these problems , but provided no declarative means for authors and authoring tools to leverage the functionality . This paper describes a new model incorporated into SMIL 2.0 that combines the strengths of scheduling models with the flexible time manipulations of animation models . The implications of this integration are discussed with respect to scheduling and structured time , drawing upon experience with SMIL 2.0 timing and synchronization , and the integration with XHTML .

B O O O O O O O O O O O O O B O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O B O O O O O O O

animation multimedia synchronization timing video


Incorporating site-level knowledge to extract structured data from web forums Web forums have become an important data resource for many web applications , but extracting structured data from unstructured web forum pages is still a challenging task due to both complex page layout designs and unrestricted user created posts . In this paper , we study the problem of structured data extraction from various web forum sites . Our target is to find a solution as general as possible to extract structured data , such as post title , post author , post time , and post content from any forum site . In contrast to most existing information extraction methods , which only leverage the knowledge inside an individual page , we incorporate both page-level and site-level knowledge and employ Markov logic networks ( MLNs ) to effectively integrate all useful evidence by learning their importance automatically . Site-level knowledge includes ( 1 ) the linkages among different object pages , such as list pages and post pages , and ( 2 ) the interrelationships of pages belonging to the same object . The experimental results on 20 forums show a very encouraging information extraction performance , and demonstrate the ability of the proposed approach on various forums . We also show that the performance is limited if only page-level knowledge is used , while when incorporating the site-level knowledge both precision and recall can be significantly improved .

O O O O O B I O B I B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

information extraction
markov logic networks
structured data
web forums


Browsing on small screens : recasting web-page segmentation into an efficient machine learning framework Fitting enough information from webpages to make browsing on small screens compelling is a challenging task . One approach is to present the user with a thumbnail image of the full web page and allow the user to simply press a single key to zoom into a region ( which may then be transcoded into wml\/xhtml , summarized , etc ) . However , if regions for zooming are presented naively , this yields a frustrating experience because of the number of coherent regions , sentences , images , and words that may be inadvertently separated . Here , we cast the web page segmentation problem into a machine learning framework , where we re-examine this task through the lens of entropy reduction and decision tree learning . This yields an efficient and effective page segmentation algorithm . We demonstrate how simple techniques from computer vision can be used to fine-tune the results . The resulting segmentation keeps coherent regions together when tested on a broad set of complex webpages .

O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

browser
information interfaces and presentation
mobile browsing
mobile devices
small screen
thumbnail browsing


Strategic formation of credit networks Credit networks are an abstraction for modeling trust between agents in a network . Agents who do not directly trust each other can transact through exchange of IOUs ( obligations ) along a chain of trust in the network . Credit networks are robust to intrusion , can enable transactions between strangers in exchange economies , and have the liquidity to support a high rate of transactions . We study the formation of such networks when agents strategically decide how much credit to extend each other . When each agent trusts a fixed set of other agents , and transacts directly only with those it trusts , the formation game is a potential game and all Nash equilibria are social optima . Moreover , the Nash equilibria of this game are equivalent in a very strong sense : the sequences of transactions that can be supported from each equilibrium credit network are identical . When we allow transactions over longer paths , the game may not admit a Nash equilibrium , and even when it does , the price of anarchy may be unbounded . Hence , we study two special cases . First , when agents have a shared belief about the trustworthiness of each agent , the networks formed in equilibrium have a star-like structure . Though the price of anarchy is unbounded , myopic best response quickly converges to a social optimum . Similar star-like structures are found in equilibria of heuristic strategies found via simulation . In addition , we simulate a second case where agents may have varying information about each others ' trustworthiness based on their distance in a social network . Empirical game analysis of these scenarios suggests that star structures arise only when defaults are relatively rare , and otherwise , credit tends to be issued over short social distances conforming to the locality of information .

O O O B I B I O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

electronic commerce
empirical game-theoretic simulations
strategic network formation


Finding influential mediators in social networks Given a social network , who are the key players controlling the bottlenecks of influence propagation if some persons would like to activate specific individuals ? In this paper , we tackle the problem of selecting a set of k mediator nodes as the influential gateways whose existence determines the activation probabilities of targeted nodes from some given seed nodes . We formally define the k-Mediators problem . To have an effective and efficient solution , we propose a three-step greedy method by considering the probabilistic influence and the structural connectivity on the pathways from sources to targets . To the best of our knowledge , this is the first work to consider the k-Mediators problem in networks . Experiments on the DBLP co-authorship graph show the effectiveness and efficiency of the proposed method .

O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Efficient training on biased minimax probability machine for imbalanced text classification The Biased Minimax Probability Machine ( BMPM ) constructs a classifier which deals with the imbalanced learning tasks . In this paper , we propose a Second Order Cone Programming ( SOCP ) based algorithm to train the model . We outline the theoretical derivatives of the biased classification model , and address the text classification tasks where negative training documents significantly outnumber the positive ones using the proposed strategy . We evaluated the learning scheme in comparison with traditional solutions on three different datasets . Empirical results have shown that our method is more effective and robust to handle imbalanced text classification problems .

O O O B I I I O O B I O B I I I O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O B I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O

biased classification
biased minimax probability machine
second order cone programming
text classification


Web scale NLP : a case study on url word breaking This paper uses the URL word breaking task as an example to elaborate what we identify as crucial in designing statistical natural language processing ( NLP ) algorithms for Web scale applications : ( 1 ) rudimentary multilingual capabilities to cope with the global nature of the Web , ( 2 ) multi-style modeling to handle diverse language styles seen in the Web contents , ( 3 ) fast adaptation to keep pace with the dynamic changes of the Web , ( 4 ) minimal heuristic assumptions for generalizability and robustness , and ( 5 ) possibilities of efficient implementations and minimal manual efforts for processing massive amount of data at a reasonable cost . We first show that the state-of-the-art word breaking techniques can be unified and generalized under the Bayesian minimum risk ( BMR ) framework that , using a Web scale N-gram , can meet the first three requirements . We discuss how the existing techniques can be viewed as introducing additional assumptions to the basic BMR framework , and describe a generic yet efficient implementation called word synchronous beam search . Testing the framework and its implementation on a series of large scale experiments reveals the following . First , the language style used to build the model plays a critical role in the word breaking task , and the most suitable for the URL word breaking task appears to be that of the document title where the best performance is obtained . Models created from other language styles , such as from document body , anchor text , and even queries , exhibit varying degrees of mismatch . Although all styles benefit from increasing modeling power which , in our experiments , corresponds to the use of a higher order N-gram , the gain is most recognizable for the title model . The heuristics proposed by the prior arts do contribute to the word breaking performance for mismatched or less powerful models , but are less effective and , in many cases , lead to poorer performance than the matched model with minimal assumptions . For the matched model based on document titles , an accuracy rate of 97.18 % can already be achieved using simple trigram without any heuristics .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

compound splitting
multi-style language model
url segmentation
web scale word breaking
word segmentation


Boosting SVM classifiers by ensemble By far , the support vector machines ( SVM ) achieve the state-of-the-art performance for the text classification ( TC ) tasks . Due to the complexity of the TC problems , it becomes a challenge to systematically develop classifiers with better performance . We try to attack this problem by ensemble methods , which are often used for boosting weak classifiers , such as decision tree , neural networks , etc. , and whether they are effective for strong classifiers is not clear .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

classifier design and evaluation
design methodology
information filtering
information search and retrieval
machine learning
neural nets
text processing


Homepage live : automatic block tracing for web personalization The emergence of personalized homepage services , e.g. personalized Google Homepage and Microsoft Windows Live , has enabled Web users to select Web contents of interest and to aggregate them in a single Web page . The web contents are often predefined content blocks provided by the service providers . However , it involves intensive manual efforts to define the content blocks and maintain the information in it . In this paper , we propose a novel personalized homepage system , called . Homepage Live. , to allow end users to use drag-and-drop actions to collect their favorite Web content blocks from existing Web pages and organize them in a single page . Moreover , Homepage Live automatically traces the changes of blocks with the evolvement of the container pages by measuring the tree edit distance of the selected blocks . By exploiting the immutable elements of Web pages , the tracing algorithm performance is significantly improved . The experimental results demonstrate the effectiveness and efficiency of our algorithm .

O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O

adaptive user interfaces
tree pruning
web blocks


Predictive ranking : a novel page ranking approach by estimating the web structure PageRank ( PR ) is one of the most popular ways to rank web pages . However , as the Web continues to grow in volume , it is becoming more and more difficult to crawl all the available pages . As a result , the page ranks computed by PR are only based on a subset of the whole Web . This produces inaccurate outcome because of the inherent incomplete information ( dangling pages ) that exist in the calculation . To overcome this incompleteness , we propose a new variant of the PageRank algorithm called , Predictive Ranking ( PreR ) , in which different classes of dangling pages are analyzed individually so that the link structure can be predicted more accurately . We detail our proposed steps . Furthermore , experimental results show that this algorithm achieves encouraging results when compared with previous methods .

B I O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

information search and retrieval
link analysis


Algorithms and programming models for efficient representation of XML for Internet applications


On the temporal dimension of search Web search is probably the single most important application on the Internet . The most famous search techniques are perhaps the PageRank and HITS algorithms . These algorithms are motivated by the observation that a hyperlink from a page to another is an implicit conveyance of authority to the target page . They exploit this social phenomenon to identify quality pages , e.g. , `` authority '' pages and `` hub '' pages . In this paper we argue that these algorithms miss an important dimension of the Web , the temporal dimension . The Web is not a static environment . It changes constantly . Quality pages in the past may not be quality pages now or in the future . These techniques favor older pages because these pages have many in-links accumulated over time . New pages , which may be of high quality , have few or no in-links and are left behind . Bringing new and quality pages to users is important because most users want the latest information . Research publication search has exactly the same problem . This paper studies the temporal dimension of search in the context of research publication search . We propose a number of methods deal with the problem . Our experimental results show that these methods are highly effective .

O O B I I I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I I I O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O

publication search
temporal dimension of search
web search


A unified approach to learning task-specific bit vector representations for fast nearest neighbor search Fast nearest neighbor search is necessary for a variety of large scale web applications such as information retrieval , nearest neighbor classification and nearest neighbor regression . Recently a number of machine learning algorithms have been proposed for representing the data to be searched as ( short ) bit vectors and then using hashing to do rapid search . These algorithms have been limited in their applicability in that they are suited for only one type of task -- e.g. Spectral Hashing learns bit vector representations for retrieval , but not say , classification . In this paper we present a unified approach to learning bit vector representations for many applications that use nearest neighbor search . The main contribution is a single learning algorithm that can be customized to learn a bit vector representation suited for the task at hand . This broadens the usefulness of bit vector representations to tasks beyond just conventional retrieval . We propose a learning-to-rank formulation to learn the bit vector representation of the data . LambdaRank algorithm is used for learning a function that computes a task-specific bit vector from an input data vector . Our approach outperforms state-of-the-art nearest neighbor methods on a number of real world text and image classification and retrieval datasets . It is scalable and learns a 32-bit representation on 1.46 million training cases in two days .

O O O O O O O O O O O B I I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

information search and retrieval
learning to rank


Extraction and mining of an academic social network This paper addresses several key issues in extraction and mining of an academic social network : 1 ) extraction of a researcher social network from the existing Web ; 2 ) integration of the publications from existing digital libraries ; 3 ) expertise search on a given topic ; and 4 ) association search between researchers . We developed a social network system , called ArnetMiner , based on proposed methods to the above problems . In total , 448,470 researcher profiles and 981,599 publications were extracted\/integrated after the system having been in operation for two years . The paper describes the architecture and main features of the system . It also briefly presents the experimental results of the proposed methods .

O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

database applications
information extraction
information search and retrieval
learning


The discoverability of the web Previous studies have highlighted the high arrival rate of new contenton the web . We study the extent to which this new content can beefficiently discovered by a crawler . Our study has two parts . First , we study the inherent difficulty of the discovery problem using amaximum cover formulation , under an assumption of perfect estimates oflikely sources of links to new content . Second , we relax thisassumption and study a more realistic setting in which algorithms mustuse historical statistics to estimate which pages are most likely toyield links to new content . We recommend a simple algorithm thatperforms comparably to all approaches we consider . We measure the emphoverhead of discovering new content , defined asthe average number of fetches required to discover one new page . Weshow first that with perfect foreknowledge of where to explore forlinks to new content , it is possible to discover 90 % of all newcontent with under 3 % overhead , and 100 % of new content with 9 % overhead . But actual algorithms , which do not have access to perfectforeknowledge , face a more difficult task : one quarter of new contentis simply not amenable to efficient discovery . Of the remaining threequarters , 80 % of new content during a given week may be discoveredwith 160 % overhead if content is recrawled fully on a monthly basis .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

crawling
greedy
max cover
miscellaneous
set cover


An incremental XSLT transformation processor for XML document manipulation In this paper , we present an incremental transformation framework called incXSLT . This framework has been experimented for the XSLT language defined at the World Wide Web Consortium . For the currently available tools , designing the XML content and the transformation sheets is an inefficient , a tedious and an error prone experience . Incremental transformation processors such as incXSLT represent a better alternative to help in the design of both the content and the transformation sheets . We believe that such frameworks are a first step toward fully interactive transformation-based authoring environments .

O O B O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

authoring tools
incremental transformations


A storage and indexing framework for p2p systems We present a modularized storage and indexing framework that cleanly separates the functional components of a P2P system , enabling us to tailor the P2P infrastructure to the specific needs of various Internet applications eat , without having to devise completely new storage management and index structures for each application .

O O O B I O B O O O O O O O B I O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O


Collective context-aware topic models for entity disambiguation A crucial step in adding structure to unstructured data is to identify references to entities and disambiguate them . Such disambiguated references can help enhance readability and draw similarities across different pieces of running text in an automated fashion . Previous research has tackled this problem by first forming a catalog of entities from a knowledge base , such as Wikipedia , and then using this catalog to disambiguate references in unseen text . However , most of the previously proposed models either do not use all text in the knowledge base , potentially missing out on discriminative features , or do not exploit word-entity proximity to learn high-quality catalogs . In this work , we propose topic models that keep track of the context of every word in the knowledge base ; so that words appearing within the same context as an entity are more likely to be associated with that entity . Thus , our topic models utilize all text present in the knowledge base and help learn high-quality catalogs . Our models also learn groups of co-occurring entities thus enabling collective disambiguation . Unlike most previous topic models , our models are non-parametric and do not require the user to specify the exact number of groups present in the knowledge base . In experiments performed on an extract of Wikipedia containing almost 60,000 references , our models outperform SVM-based baselines by as much as 18 % in terms of disambiguation accuracy translating to an increment of almost 11,000 correctly disambiguated references .

O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Learning search engine specific query transformations for question answering

information retrieval
information search and retrieval
query expansion
web search


Characterizing web-based video sharing workloads


Similarity spreading : a unified framework for similarity calculation of interrelated objects In many Web search applications , similarities between objects of one type ( say , queries ) can be affected by the similarities between their interrelated objects of another type ( say , Web pages ) , and vice versa . We propose a novel framework called similarity spreading to take account of the interrelationship and improve the similarity calculation . Experiment results show that the proposed framework can significantly improve the accuracy of the similarity measurement of the objects in a search engine .

B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Combining classifiers to identify online databases We address the problem of identifying the domain of onlinedatabases . More precisely , given a set F of Web forms automaticallygathered by a focused crawler and an online databasedomain D , our goal is to select from F only the formsthat are entry points to databases in D. Having a set ofWebforms that serve as entry points to similar online databasesis a requirement for many applications and techniques thataim to extract and integrate hidden-Web information , suchas meta-searchers , online database directories , hidden-Webcrawlers , and form-schema matching and merging . We propose a new strategy that automatically and accuratelyclassifies online databases based on features that canbe easily extracted from Web forms . By judiciously partitioningthe space of form features , this strategy allows theuse of simpler classifiers that can be constructed using learningtechniques that are better suited for the features of eachpartition . Experiments using real Web data in a representativeset of domains show that the use of different classifiersleads to high accuracy , precision and recall . This indicatesthat our modular classifier composition provides an effectiveand scalable solution for classifying online databases .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

hidden web
hierarchical classifiers
learning classifiers
web crawlers


Learning information diffusion process on the web Many text documents on the Web are not originally created but forwarded or copied from other source documents . The phenomenon of document forwarding or transmission between various web sites is denoted as Web information diffusion . This paper focuses on mining information diffusion processes for specific topics on the Web . A novel system called LIDPW is proposed to address this problem using matching learning techniques . The source site and source document of each document are identified and the diffusion process composed of a sequence of diffusion relationships is visually presented to users . The effectiveness of LIDPW is validated on a real data set . A preliminary user study is performed and the results show that LIDPW does benefit users to monitor the information diffusion process of a specific topic , and aid them to discover the diffusion start and diffusion center of the topic .

O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O

information flow
miscellaneous
web mining


Towards extracting flickr tag semantics We address the problem of extracting semantics of tags -- short , unstructured text-labels assigned to resources on the Web -- based on each tag 's metadata patterns . In particular , we describe an approach for extracting place and event semantics for tags that are assigned to photos on Flickr , a popular photo sharing website supporting time and location ( latitude\/longitude ) metadata . The approach can be generalized to other domains where text terms can be extracted and associated with metadata patterns , such as geo-annotated web pages .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

event identification
miscellaneous
place identification
tagging systems
word semantics


Mining newsgroups using networks arising from social behavior Recent advances in information retrieval over hyperlinked corpora have convincingly demonstrated that links carry less noisy information than text . We investigate the feasibility of applying link-based methods in new applications domains . The specific application we consider is to partition authors into opposite camps within a given topic in the context of newsgroups . A typical newsgroup posting consists of one or more quoted lines from another posting followed by the opinion of the author . This social behavior gives rise to a network in which the vertices are individuals and the links represent `` responded-to '' relationships . An interesting characteristic of many newsgroups is that people more frequently respond to a message when they disagree than when they agree . This behavior is in sharp contrast to the WWW link graph , where linkage is an indicator of agreement or common interest . By analyzing the graph structure of the responses , we are able to effectively classify people into opposite camps . In contrast , methods based on statistical analysis of text yield low accuracy on such datasets because the vocabulary used by the two sides tends to be largely identical , and many newsgroup postings consist of relatively few words of text .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O

link analysis
social network
text mining
web mining


Adaptive page ranking with neural networks Recent developments in the area of neural networks provided new models which are capable of processing general types of graph structures . Neural networks are well-known for their generalization capabilities . This paper explores the idea of applying a novel neural network model to a web graph to compute an adaptive ranking of pages . Some early experimental results indicate that the new neural network models generalize exceptionally well when trained on a relatively small number of pages .

O O O O B I O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


From user-centric web traffic data to usage data In this paper , we describe a user-centric Internet usage data processing platform . Raw usage data is collected using a software probe installed on a panel of Internet users ' workstations . It is then processed by our platform . The transformation of raw usage data into qualified and usable information by Internet usage sociology researchers means setting up a series of relatively complex processes using quite a wide variety of resources . We use a combination of ad hoc rule-based systems and external resources to qualify the visited Web pages . We also implemented topological and temporal indicators in order to describe the dynamics of Web sessions .

O O O O O O B I O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

internet uses
systems and software
traffic analysis
user-centric traffic data
web usage mining


Buzz-based recommender system In this paper , we describe a buzz-based recommender system based on a large source of queries in an eCommerce application . The system detects bursts in query trends . These bursts are linked to external entities like news and inventory information to find the queries currently in-demand which we refer to as buzz queries . The system follows the paradigm of limited quantity merchandising , in the sense that on a per-day basis the system shows recommendations around a single buzz query with the intent of increasing user curiosity , and improving activity and stickiness on the site . A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O


A self-training approach for resolving object coreference on the semantic web An object on the Semantic Web is likely to be denoted with multiple URIs by different parties . Object coreference resolution is to identify `` equivalent '' URIs that denote the same object . Driven by the Linking Open Data ( LOD ) initiative , millions of URIs have been explicitly linked with owl : sameAs statements , but potentially coreferent ones are still considerable . Existing approaches address the problem mainly from two directions : one is based upon equivalence inference mandated by OWL semantics , which finds semantically coreferent URIs but probably omits many potential ones ; the other is via similarity computation between property-value pairs , which is not always accurate enough . In this paper , we propose a self-training approach for object coreference resolution on the Semantic Web , which leverages the two classes of approaches to bridge the gap between semantically coreferent URIs and potential candidates . For an object URI , we firstly establish a kernel that consists of semantically coreferent URIs based on owl : sameAs , ( inverse ) functional properties and ( max - ) cardinalities , and then extend such kernel iteratively in terms of discriminative property-value pairs in the descriptions of URIs . In particular , the discriminability is learned with a statistical measurement , which not only exploits key characteristics for representing an object , but also takes into account the matchability between properties from pragmatics . In addition , frequent property combinations are mined to improve the accuracy of the resolution . We implement a scalable system and demonstrate that our approach achieves good precision and recall for resolving object coreference , on both benchmark and large-scale datasets .

O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O

data fusion
interoperability
object consolidation
property combination
self-training


Earthquake shakes Twitter users : real-time event detection by social sensors Twitter , a popular microblogging service , has received much attention recently . An important characteristic of Twitter is its real-time nature . For example , when an earthquake occurs , people make many Twitter posts ( tweets ) related to the earthquake , which enables detection of earthquake occurrence promptly , simply by observing the tweets . As described in this paper , we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event . To detect a target event , we devise a classifier of tweets based on features such as the keywords in a tweet , the number of words , and their context . Subsequently , we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location . We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering , which are widely used for location estimation in ubiquitous\/pervasive computing . The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons . As an application , we construct an earthquake reporting system in Japan . Because of the numerous earthquakes and the large number of Twitter users throughout the country , we can detect an earthquake with high probability ( 96 % of earthquakes of Japan Meteorological Agency ( JMA ) seismic intensity scale 3 or more are detected ) merely by monitoring tweets . Our system detects earthquakes promptly and sends e-mails to registered users . Notification is delivered much faster than the announcements that are broadcast by the JMA .

B O B O O O B I O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O B O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

earthquake
event detection
location estimation
twitter


Towards autonomic web-sites based on learning automata Autonomics or self-reorganization becomes pertinent for web-sites serving a large number of users with highly varying workloads . An important component of self-adaptation is to model the behavior of users and adapt accordingly . This paper proposes a learning-automata based technique for model discovery . User access patterns are used to construct an FSM model of user behavior that in turn is used for prediction and prefetching . The proposed technique uses a generalization algorithm to classify behavior patterns into a small number of generalized classes . It has been tested on both synthetic and live data-sets and has shown a prediction hit-rate of up to 89 % on a real web-site .

O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O

autonomic website
learning automata
models


Comparing apples and oranges : normalized pagerank for evolving graphs PageRank is the best known technique for link-based importance ranking . The computed importance scores , however , are not directly comparable across different snapshots of an evolving graph . We present an efficiently computable normalization for PageRank scores that makes them comparable across graphs . Furthermore , we show that the normalized PageRank scores are robust to non-local changes in the graph , unlike the standard PageRank measure .

O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O


Game theoretic models for social network analysis The existing methods and techniques for social network analysis are inadequate to capture both the behavior ( such as rationality and intelligence ) of individuals and the strategic interactions that occur among these individuals . Game theory is a natural tool to overcome this inadequacy since it provides rigorous mathematical models of strategic interaction among autonomous , intelligent , and rational agents . Motivated by the above observation , this tutorial provides the conceptual underpinnings of the use of game theoretic models in social network analysis . In the first part of the tutorial , we provide rigorous foundations of relevant concepts in game theory and social network analysis . In the second part of the tutorial , we present a comprehensive study of four contemporary and pertinent problems in social networks : social network formation , determining in influential individuals for viral marketing , query incentive networks , and community detection .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O B I O B I I O O B I O

community detection
game theory
network formation
query incentive networks
social networks
viral marketing


Raise semantics at the user level for dynamic and interactive SOA-based portals In this paper , we describe the fully dynamic semantic portal we implemented , integrating Semantic Web technologies and Service Oriented Architecture ( SOA ) . The goals of the portal are twofold : first it helps administrators to easily propose new features in the portal using semantics to ease the orchestration process ; secondly it automatically generates a customized user interface for these scenarios . This user interface takes into account different devices and assists end-users in the use of the portal taking benefit of context awareness . All the added-value of this portal is based on a core semantics defined by an ontology . We present here the main features of this portal and how it was implemented using state-of-the-art technologies and frameworks .

O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A web personalization system based on web usage mining techniques In the past few years , web usage mining techniques have grown rapidly together with the explosive growth of the web , both in the research and commercial areas . In this work we present a Web mining strategy for Web personalization based on a novel pattern recognition strategy which analyzes and classifies both static and dynamic features . The results of experiments on the data from a large commercial web site are presented to show the effectiveness of the proposed system .

O B I O O O B I I O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O