Recommendations to boost content spread in social networks Content sharing in social networks is a powerful mechanism for discovering content on the Internet . The degree to which content is disseminated within the network depends on the connectivity relationships among network nodes . Existing schemes for recommending connections in social networks are based on the number of common neighbors , similarity of user profiles , etc. . However , such similarity-based connections do not consider the amount of content discovered . In this paper , we propose novel algorithms for recommending connections that boost content propagation in a social network without compromising on the relevance of the recommendations . Unlike existing work on influence propagation , in our environment , we are looking for edges instead of nodes , with a bound on the number of incident edges per node . We show that the content spread function is not submodular , and develop approximation algorithms for computing a near-optimal set of edges . Through experiments on real-world social graphs such as Flickr and Twitter , we show that our approximation algorithms achieve content spreads that are as much as 90 times higher compared to existing heuristics for recommending connections .

O O O B I O B 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 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 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


The web of things The Web , similar to other successful man made systems is continuously evolving . With the miniaturization and increased performance of computing devices which are also being embedded in common physical objects , it is natural that the Web evolved to also include these - therefore the Web of Things . This tutorial provides an overview of the system vertical structure by identifying the relevant components , illustrating their functionality and showing existing tools and systems . The aim is to show how small devices can be connected to the Web at various levels of abstraction and transform them into `` first-class '' Web residents .

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 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

machine-learning
semantic web
sensor
sensor network
stream-mining
systems and software
text-mining
web-mining


A novel collaborative filtering-based framework for personalized services in m-commerce With the rapid growth of wireless technologies and handheld devices , m-commerce is becoming a promising research area . Personalization is especially important to the success of m-commerce . This paper proposes a novel collaborative filtering-based framework for personalized services in m-commerce . The framework extends our previous work by using Online Analytical Processing ( OLAP ) to represent the relations among user , content and context information , and adopting a multi-dimensional collaborative filtering model to perform inference . It provides a powerful and well-founded mechanism to personalization for m-commerce . We implemented it in an existing m-commerce platform , and experimental results demonstrate its feasibility and correctness .

O O O O O 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 B I 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


Web-assisted annotation , semantic indexing and search of television and radio news The Rich News system , that can automatically annotate radio and television news with the aid of resources retrieved from the World Wide Web , is described . Automatic speech recognition gives a temporally precise but conceptually inaccurate annotation model . Information extraction from related web news sites gives the opposite : conceptual accuracy but no temporal data . Our approach combines the two for temporally accurate conceptual semantic annotation of broadcast news . First low quality transcripts of the broadcasts are produced using speech recognition , and these are then automatically divided into sections corresponding to individual news stories . A key phrases extraction component finds key phrases for each story and uses these to search for web pages reporting the same event . The text and meta-data of the web pages is then used to create index documents for the stories in the original broadcasts , which are semantically annotated using the KIM knowledge management platform . A web interface then allows conceptual search and browsing of news stories , and playing of the parts of the media files corresponding to each news story . The use of material from the World Wide Web allows much higher quality textual descriptions and semantic annotations to be produced than would have been possible using the ASR transcript directly . The semantic annotations can form a part of the Semantic Web , and an evaluation shows that the system operates with high precision , and with a moderate level of recall .

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

automatic speech recognition
semantic annotation
semantic web

key-phrase extraction
media archiving
multimedia
natural language processing
probabilistic algorithms
topical segmentation
web search


Personalization in distributed e-learning environments Personalized support for learners becomes even more important , when e-Learning takes place in open and dynamic learning and information networks . This paper shows how to realize personalized learning support in distributed learning environments based on Semantic Web technologies . Our approach fills the existing gap between current adaptive educational systems with well-established personalization functionality , and open , dynamic learning repository networks . We propose a service-based architecture for establishing personalized e-Learning , where personalization functionality is provided by various web-services . A Personal Learning Assistant integrates personalization services and other supporting services , and provides the personalized access to learning resources in an e-Learning network .

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

adaptation
learning repositories
ontologies
p2p
standards
web services


Using the wisdom of the crowds for keyword generation In the sponsored search model , search engines are paid by businesses that are interested in displaying ads for their site alongside the search results . Businesses bid for keywords , and their ad is displayed when the keyword is queried to the search engine . An important problem in this process is ` keyword generation ' : given a business that is interested in launching a campaign , suggest keywords that are related to that campaign . We address this problem by making use of the query logs of the search engine . We identify queries related to a campaign by exploiting the associations between queries and URLs as they are captured by the user 's clicks . These queries form good keyword suggestions since they capture the `` wisdom of the crowd '' as to what is related to a site . We formulate the problem as a semi-supervised learning problem , and propose algorithms within the Markov Random Field model . We perform experiments with real query logs , and we demonstrate that our algorithms scale to large query logs and produce meaningful results .

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 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

absorbing random walks
markov random fields
query click logs


Analyzing search engine advertising : firm behavior and cross-selling in electronic markets The phenomenon of sponsored search advertising is gaining ground as the largest source of revenues for search engines . Firms across different industries have are beginning to adopt this as the primary form of online advertising . This process works on an auction mechanism in which advertisers bid for different keywords , and final rank for a given keyword is allocated by the search engine . But how different are firm 's actual bids from their optimal bids ? Moreover , what are other ways in which firms can potentially benefit from sponsored search advertising ? Based on the model and estimates from prior work ( 10 ) , we conduct a number of policy simulations in order to investigate to what extent an advertiser can benefit from bidding optimally for its keywords . Further , we build a Hierarchical Bayesian modeling framework to explore the potential for cross-selling or spillovers effects from a given keyword advertisement across multiple product categories , and estimate the model using Markov Chain Monte Carlo ( MCMC ) methods . Our analysis suggests that advertisers are not bidding optimally with respect to maximizing profits . We conduct a detailed analysis with product level variables to explore the extent of cross-selling opportunities across different categories from a given keyword advertisement . We find that there exists significant potential for cross-selling through search keyword advertisements in that consumers often end up buying products from other categories in addition to the product they were searching for . Latency ( the time it takes for consumer to place a purchase order after clicking on the advertisement ) and the presence of a brand name in the keyword are associated with consumer spending on product categories that are different from the one they were originally searching for on the Internet .

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

hierarchical bayesian modeling
online advertising
search engines

electronic commerce
paid search advertising
web 2.0


Information credibility on twitter We analyze the information credibility of news propagated through Twitter , a popular microblogging service . Previous research has shown that most of the messages posted on Twitter are truthful , but the service is also used to spread misinformation and false rumors , often unintentionally . On this paper we focus on automatic methods for assessing the credibility of a given set of tweets . Specifically , we analyze microblog postings related to `` trending '' topics , and classify them as credible or not credible , based on features extracted from them . We use features from users ' posting and re-posting ( `` re-tweeting '' ) behavior , from the text of the posts , and from citations to external sources . We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings . Our results shows that there are measurable differences in the way messages propagate , that can be used to classify them automatically as credible or not credible , with precision and recall in the range of 70 % to 80 % .

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 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 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

social media analytics
social media credibility


Staging transformations for multimodal web interaction management Multimodal interfaces are becoming increasingly ubiquitous with the advent of mobile devices , accessibility considerations , and novel software technologies that combine diverse interaction media . In addition to improving access and delivery capabilities , such interfaces enable flexible and personalized dialogs with websites , much like a conversation between humans . In this paper , we present a software framework for multimodal web interaction management that supports mixed-initiative dialogs between users and websites . A mixed-initiative dialog is one where the user and the website take turns changing the flow of interaction . The framework supports the functional specification and realization of such dialogs using staging transformations -- a theory for representing and reasoning about dialogs based on partial input . It supports multiple interaction interfaces , and offers sessioning , caching , and co-ordination functions through the use of an interaction manager . Two case studies are presented to illustrate the promise of this approach .

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

mixed-initiative interaction
out-of-turn interaction
program transformations
web dialogs


Extending the compatibility notion for abstract WS-BPEL processes WS-BPEL defines a standard for executable processes . Executable processes are business processes which can be automated through an IT infrastructure . The WS-BPEL specification also introduces the concept of abstract processes : In contrast to their executable siblings , abstract processes are not executable and can have parts where business logic is disguised . Nevertheless , the WS-BPEL specification introduces a notion of compatibility between such an under-specified abstract process and a fully specified executable one . Basically , this compatibility notion defines a set of syntactical rules that can be augmented or restricted by profiles . So far , there exist two of such profiles : the Abstract Process Profile for Observable Behavior and the Abstract Process Profile for Templates . None of these profiles defines a concept of behavioral equivalence . Therefore , both profiles are too strict with respect to the rules they impose when deciding whether an executable process is compatible to an abstract one . In this paper , we propose a novel profile that extends the existing Abstract Process Profile for Observable Behavior by defining a behavioral relationship . We also show that our novel profile allows for more flexibility when deciding whether an executable and an abstract process are compatible .

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

abstract profile
compliance
petri nets
ws-bpel


Embedding MindMap as a service for user-driven composition of web applications The World Wide Web is evolving towards a very large distributed platform allowing ubiquitous access to a wide range of Web applications with minimal delay and no installation required . Such Web applications range from having users undertake simple tasks , such as filling a form , to more complex tasks including collaborative work , project management , and more generally , creating , consulting , annotating , and sharing Web content . However , users are lacking a simple but yet powerful mechanism to compose Web applications , similarly to what desktop environments allowed for decades using the file explorer paradigm and the desktop metaphor . Attempts have been made to adapt the desktop metaphor to the Web environment giving birth to Webtops ( Web desktops ) . It essentially consisted of embedding a desktop environment in a Web browser and provide access to various Web applications within the same User Interface . However , those attempts did not take into consideration to the radical differences between Web and desktop environments and applications . In this work , we introduce a new approach for Web application composition based on the mindmap metaphor . It allows browsing artifacts ( Web resources ) and enabling user-driven composition of their associated Web applications . Essentially , a mindmap is a graph of widgets representing artifacts created or used by Web applications and allow to list and launch all possible Web applications associated to each artifact . A tool has been developed to experiment the new metaphor and is provided as a service to be embedded in Web applications via a Web browser 's plug-in . We demonstrate in this paper three case studies regarding the DBLP Web site , Wikipedia and Google Picasa Web applications .

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

miscellaneous
software engineering
user-driven composition


Autopedia : automatic domain-independent Wikipedia article generation This paper proposes a general framework , named Autopedia , to generate high-quality wikipedia articles for given concepts in any domains , by automatically selecting the best wikipedia template consisting the sub-topics to organize the article for the input concept . Experimental results on 4,526 concepts validate the effectiveness of Autopedia , and the wikipedia template selection approach which takes into account both the template quality and the semantic relatedness between the input concept and its sibling concepts , performs the best .

O O O O B B I 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 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 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

article generation
template selection
wikipedia


An adaptive , fast , and safe XML parser based on byte sequences memorization XML ( Extensible Markup Language ) processing can incur significant runtime overhead in XML-based infrastructural middleware such as Web service application servers . This paper proposes a novel mechanism for efficiently processing similar XML documents . Given a new XML document as a byte sequence , the XML parser proposed in this paper normally avoids syntactic analysis but simply matches the document with previously processed ones , reusing those results . Our parser is adaptive since it partially parses and then remembers XML document fragments that it has not met before . Moreover , it processes safely since its partial parsing correctly checks the well-formedness of documents . Our implementation of the proposed parser complies with the JSR 63 standard of the Java API for XML Processing ( JAXP ) 1.1 specification . We evaluated Deltarser performance with messages using Google Web services . Comparing to Piccolo ( and Apache Xerces ) , it effectively parses 35 % ( 106 % ) faster in a server-side use-case scenario , and 73 % ( 126 % ) faster in a client-side use-case scenario .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 to model relatedness for news recommendation With the explosive growth of online news readership , recommending interesting news articles to users has become extremely important . While existing Web services such as Yahoo ! and Digg attract users ' initial clicks by leveraging various kinds of signals , how to engage such users algorithmically after their initial visit is largely under-explored . In this paper , we study the problem of post-click news recommendation . Given that a user has perused a current news article , our idea is to automatically identify `` related '' news articles which the user would like to read afterwards . Specifically , we propose to characterize relatedness between news articles across four aspects : relevance , novelty , connection clarity , and transition smoothness . Motivated by this understanding , we define a set of features to capture each of these aspects and put forward a learning approach to model relatedness . In order to quantitatively evaluate our proposed measures and learn a unified relatedness function , we construct a large test collection based on a four-month commercial news corpus with editorial judgments . The experimental results show that the proposed heuristics can indeed capture relatedness , and that the learned unified relatedness function works quite effectively .

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

connection clarity
learning
novelty
relatedness
relevance
transition smoothness


Hardening Web browsers against man-in-the-middle and eavesdropping attacks Existing Web browsers handle security errors in a manner that often confuses users . In particular , when a user visits a secure site whose certificate the browser can not verify , the browser typically allows the user to view and install the certificate and connect to the site despite the verification failure . However , few users understand the risk of man-in-the-middle attacks and the principles behind certificate-based authentication . We propose context-sensitive certificate verification ( CSCV ) , whereby the browser interrogates the user about the context in which a certificate verification error occurs . Considering the context , the browser then guides the user in handling and possibly overcoming the security error . We also propose specific password warnings ( SPW ) when users are about to send passwords in a form vulnerable to eavesdropping . We performed user studies to evaluate CSCV and SPW . Our results suggest that CSCV and SPW can greatly improve Web browsing security and are easy to use even without training . Moreover , CSCV had greater impact than did staged security training .

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

eavesdropping attack
https
interaction styles
just-in-time instruction
man-in-the-middle attack
safe staging
screen design
ssl
web browser
well-in-advance instruction


Improved annotation of the blogosphere via autotagging and hierarchical clustering Tags have recently become popular as a means of annotating and organizing Web pages and blog entries . Advocates of tagging argue that the use of tags produces a ` folksonomy ' , a system in which the meaning of a tag is determined by its use among the community as a whole . We analyze the effectiveness of tags for classifying blog entries by gathering the top 350 tags from Technorati and measuring the similarity of all articles that share a tag . We find that tags are useful for grouping articles into broad categories , but less effective in indicating the particular content of an article . We then show that automatically extracting words deemed to be highly relevant can produce a more focused categorization of articles . We also show that clustering algorithms can be used to reconstruct a topical hierarchy among tags , and suggest that these approaches may be used to address some of the weaknesses in current tagging systems .

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

automated annotation
blogs
content analysis and indexing
information search and retrieval


Exploring social annotations for the semantic web In order to obtain a machine understandable semantics for web resources , research on the Semantic Web tries to annotate web resources with concepts and relations from explicitly defined formal ontologies . This kind of formal annotation is usually done manually or semi-automatically . In this paper , we explore a complement approach that focuses on the `` social annotations of the web '' which are annotations manually made by normal web users without a pre-defined formal ontology . Compared to the formal annotations , although social annotations are coarse-grained , informal and vague , they are also more accessible to more people and better reflect the web resources ' meaning from the users ' point of views during their actual usage of the web resources . Using a social bookmark service as an example , we show how emergent semantics ( 2 ) can be statistically derived from the social annotations . Furthermore , we apply the derived emergent semantics to discover and search shared web bookmarks . The initial evaluation on our implementation shows that our method can effectively discover semantically related web bookmarks that current social bookmark service can not discover easily .

O O O O O B I 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 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

miscellaneous
social annotation
social bookmarks


A unified constraint model for XML

constraint reasoning
integrity constraints
keys
object identity
subtyping
xml schema


Generating diverse and representative image search results for landmarks Can we leverage the community-contributed collections of rich media on the web to automatically generate representative and diverse views of the world 's landmarks ? We use a combination of context - and content-based tools to generate representative sets of images for location-driven features and landmarks , a common search task . To do that , we using location and other metadata , as well as tags associated with images , and the images ' visual features . We present an approach to extracting tags that represent landmarks . We show how to use unsupervised methods to extract representative views and images for each landmark . This approach can potentially scale to provide better search and representation for landmarks , worldwide . We evaluate the system in the context of image search using a real-life dataset of 110,000 images from the San Francisco area .

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

geo-referenced photographs
miscellaneous
photo collections
social media


Mobile web publishing and surfing based on environmental sensing data

gps
location
personalization
rfid
sensor
systems and software
web browsing


Rapid prototyping of web applications combining domain specific languages and model driven design There have been several authoring methods proposed in the literature that are model based , essentially following the Model Driven Design philosophy . While useful , such methods need an effective way to allow the application designer to somehow synthesize the actual running application from the specification . In this paper , we describe HyperDE , an environment that combines Model Driven Design and Domain Specific Languages to enable rapid prototyping of Web applications .

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

hypermedia authoring
model-based designs


A fault model and mutation testing of access control policies To increase confidence in the correctness of specified policies , policy developers can conduct policy testing by supplying typical test inputs ( requests ) and subsequently checking test outputs ( responses ) against expected ones . Unfortunately , manual testing is tedious and few tools exist for automated testing of access control policies . We present a fault model for access control policies and a framework to explore it . The framework includes mutation operators used to implement the fault model , mutant generation , equivalent-mutant detection , and mutant-killing determination . This framework allows us to investigate our fault model , evaluate coverage criteria for test generation and selection , and determine a relationship between structural coverage and fault-detection effectiveness . We have implemented the framework and applied it to various policies written in XACML . Our experimental results offer valuable insights into choosing mutation operators in mutation testing and choosing coverage criteria in test generation and selection .

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

access control policies
fault model
mutation testing
test generation


Popular web hot spots identification and visualization This work aims a two-fold contribution : it presents a software to analyze logfiles and visualize popular web hot spots and , additionally , presents an algorithm to use this information in order to identify subsets of the website that display large access patterns . Such information is extremely valuable to the site maintainer , since it indicates points that may need content intervention or\/and site graph restructuring . Experimental validation verified that the visualization tool , when coupled with algorithms that infer frequent traversal patterns , is both effective in indicating popular hot spots and efficient in doing so by using graph-based representations of popular traversals .

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

access visualization
hypertext/hypermedia
maximal forward path
metrics
usage mining


Inferring query intent from reformulations and clicks Many researchers have noted that web search queries are often ambiguous or unclear . We present an approach for identifying the popular meanings of queries using web search logs and user click behavior . We show our approach to produce more complete and user-centric intents than expert judges by evaluating on TREC queries . This approach was also used by the TREC 2009 Web Track judges to obtain more representative topic descriptions from real queries .

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

diversity
information search and retrieval
subtopics


Privacy preserving frequency capping in internet banner advertising We describe an optimize-and-dispatch approach for delivering pay-per-impression advertisements in online advertising . The platform provider for an advertising network commits to showing advertisers ' banner ads while capping the number of advertising message shown to a unique user as the user transitions through the network . The traditional approach for enforcing frequency caps has been to use cross-site cookies to track users . However,cross-site cookies and other tracking mechanisms can infringe on the user privacy . In this paper , we propose a novel linear programming approach that decides when to show an ad to the user based solely on the page currently viewed by the users . We show that the frequency caps are fulfilled in expectation . We show the efficacy of that approach using simulation results .

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 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


WPBench : a benchmark for evaluating the client-side performance of web 2.0 applications In this paper , a benchmark called WPBench is reported to evaluate the responsiveness of Web browsers for modern Web 2.0 applications . In WPBench , variations of servers and networks are removed and the benchmark result is the closest to what Web users would perceive . To achieve these , WPBench records users ' interactions with typical Web 2.0 applications , and then replays Web navigations when benchmarking browsers . The replay mechanism can emulate the actual user interactions and the characteristics of the servers and the networks in a consistent way independent of browsers so that any browser compliant to the standards can be benchmarked fairly . In addition to describing the design and generation of WPBench , we also report the WPBench comparison results on the responsiveness performance for three popular Web browsers : Internet Explorer , Firefox and Chrome .

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


Domain-independent entity extraction from web search query logs Query logs of a Web search engine have been increasingly used as a vital source for data mining . This paper presents a study on large-scale domain-independent entity extraction from search query logs . We present a completely unsupervised method to extract entities by applying pattern-based heuristics and statistical measures . We compare against existing techniques that use Web documents as well as search logs , and show that we improve over the state of the art . We also provide an in-depth qualitative analysis outlining differences and commonalities between these methods .

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

data mining
entity extraction
query logs


Anonymizing user profiles for personalized web search We study the problem of anonymizing user profiles so that user privacy is sufficiently protected while the anonymized profiles are still effective in enabling personalized web search . We propose a Bayes-optimal privacy notion to bound the prior and posterior probability of associating a user with an individual term in the anonymized user profile set . We also propose a novel bundling technique that clusters user profiles into groups by taking into account the semantic relationships between the terms while satisfying the privacy constraint . We evaluate our approach through a set of preliminary experiments using real data demonstrating its feasibility and effectiveness .

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

anonymization
information search and retrieval
personalized search
privacy-preserving data publishing


Deducing trip related information from flickr Uploading tourist photos is a popular activity on photo sharing platforms . These photographs and their associated metadata ( tags , geo-tags , and temporal information ) should be useful for mining information about the sites visited . However , user-supplied metadata are often noisy and efficient filtering methods are needed before extracting useful knowledge . We focus here on exploiting temporal information , associated with tourist sites that appear in Flickr . From automatically filtered sets of geo-tagged photos , we deduce answers to questions like `` how long does it take to visit a tourist attraction ? '' or `` what can I visit in one day in this city ? '' Our method is evaluated and validated by comparing the automatically obtained visit duration times to manual estimations .

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

geographical gazetteer
georeferencing
image mining
text mining
visit times


Personalized web exploration with task models Personalized Web search has emerged as one of the hottest topics for both the Web industry and academic researchers . However , the majority of studies on personalized search focused on a rather simple type of search , which leaves an important research topic - the personalization in exploratory searches - as an under-studied area . In this paper , we present a study of personalization in task-based information exploration using a system called TaskSieve . TaskSieve is a Web search system that utilizes a relevance feedback based profile , called a `` task model '' , for personalization . Its innovations include flexible and user controlled integration of queries and task models , task-infused text snippet generation , and on-screen visualization of task models . Through an empirical study using human subjects conducting task-based exploration searches , we demonstrate that TaskSieve pushes significantly more relevant documents to the top of search result lists as compared to a traditional search system . TaskSieve helps users select significantly more accurate information for their tasks , allows the users to do so with higher productivity , and is viewed more favorably by subjects under several usability related characteristics .

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

empirical study
personalization
task model

adaptive search
task-based information exploration
user profile


Web-based personalization and management of interactive video

dynamic content generation
interaction styles
j2ee
media asset management
micro-payment
smil
video
video personalization


Deriving music theme annotations from user tags Music theme annotations would be really beneficial for supporting retrieval , but are often neglected by users while annotating . Thus , in order to support users in tagging and to fill the gaps in the tag space , in this paper we develop algorithms for recommending theme annotations . Our methods exploit already existing user tags , the lyrics of music tracks , as well as combinations of both . We compare the results for our recommended theme annotations against genre and style recommendations - a much easier and already studied task . We evaluate the quality of our recommended tags against an expert ground truth data set . Our results are promising and provide interesting insights into possible extensions for music tagging systems to support music search .

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

collaborative tagging
content analysis and indexing
high-level music descriptors
information search and retrieval
metadata enrichment
theme tag recommendations


Towards a flash search engine based on expressive semantics Flash , as a multimedia format , becomes more and more popular on the Web . However , previous works on Flash are totally based on low-level features , which make it unpractical to build a content-based Flash search engine . To address this problem , our paper proposes expressive semantics for bridging the gap between low-level features and user queries . To smoothly incorporate expressive semantics into a search engine , an eigenvector-based model is devised to map a user query to expressive semantics with the aid of link analysis method . Our experiment results confirm that expressive semantics is a promising approach to understanding and hence searching Flash movies more efficiently .

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

classification
eigenvector
flash retrieval
web application


The paths more taken : matching DOM trees to search logs for accurate webpage clustering An unsupervised clustering of the webpages on a website is a primary requirement for most wrapper induction and automated data extraction methods . Since page content can vary drastically across pages of one cluster ( e.g. , all product pages on amazon.com ) , traditional clustering methods typically use some distance function between the DOM trees representing a pair of webpages . However , without knowing which portions of the DOM tree are `` important , '' such distance functions might discriminate between similar pages based on trivial features ( e.g. , differing number of reviews on two product pages ) , or club together distinct types of pages based on superficial features present in the DOM trees of both ( e.g. , matching footer\/copyright ) , leading to poor clustering performance . We propose using search logs to automatically find paths in the DOM trees that mark out important portions of pages , e.g. , the product title in a product page . Such paths are identified via a global analysis of the entire website , whereby search data for popular pages can be used to infer good paths even for other pages that receive little or no search traffic . The webpages on the website are then clustered using these `` key '' paths . Our algorithm only requires information on search queries , and the webpages clicked in response to them ; there is no need for human input , and it does not need to be told which portion of a webpage the user found interesting . The resulting clusterings achieve an adjusted RAND score of over 0.9 on half of the websites ( a score of 1 indicating a perfect clustering ) , and 59 % better scores on average than competing algorithms . Besides leading to refined clusterings , these key paths can be useful in the wrapper induction process itself , as shown by the high degree of match between the key paths and the manually identified paths used in existing wrappers for these sites ( 90 % average precision ) .

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

clustering
search logs
wrapper induction


Learning and inferencing in user ontology for personalized semantic web services Domain ontology has been used in many Semantic Web applications . However , few applications explore the use of ontology for personalized services . This paper proposes an ontology based user model consisting of both concepts and semantic relations to represent users ' interests . Specifically , we adopt a statistical approach to learning a semantic-based user ontology model from domain ontology and a spreading activation procedure for inferencing in the user ontology model . We apply the methods of learning and exploiting user ontology to a semantic search engine for finding academic publications . Our experimental results support the efficacy of user ontology and spreading activation theory ( SAT ) for providing personalized semantic services .

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 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 B 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 O O O O O O O O

information search and retrieval
spreading-activation theory


Unparsing RDF\/XML It is difficult to serialize an RDF graph as a humanly readable RDF\/XML document . This paper describes the approach taken in Jena 1.2 , in which a design pattern of guarded procedures invoked using top down recursive descent is used . Each procedure corresponds to a grammar rule ; the guard makes the choice about the applicability of the production . This approach is seen to correspond closely to the design of an LL ( k ) parser , and a theoretical justification of this correspondence is found in universal algebra .

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 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 B I O


Expertise networks in online communities : structure and algorithms Web-based communities have become important places for people to seek and share expertise . We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web . Systems targeted to augment web-based communities by automatically identifying users with expertise , for example , need to adapt to the underlying interaction dynamics . In this study , we analyze the Java Forum , a large online help-seeking community , using social network analysis methods . We test a set of network-based ranking algorithms , including PageRank and HITS , on this large size social network in order to identify users with high expertise . We then use simulations to identify a small number of simple simulation rules governing the question-answer dynamic in the network . These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum , but also allow us to evaluate how other algorithms may perform in communities with different characteristics . We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities .

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 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 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

online communities
simulation
social network analysis

expertise finding
expertise locators
help seeking


Topological spaces of the web

general
separation
topology density
web metrics


The powerrank web link analysis algorithm The web graph follows the power law distribution and has a hierarchy structure . But neither the PageRank algorithm nor any of its improvements leverage these attributes . In this paper , we propose a novel link analysis algorithm `` the PowerRank algorithm '' , which makes use of the power law distribution attribute and the hierarchy structure of the web graph . The algorithm consists two parts . In the first part , special treatment is applied to the web pages with low `` importance '' score . In the second part , the global `` importance '' score for each web page is obtained by combining those scores together . Our experimental results show that : 1 ) The PowerRank algorithm computes 10 % -30 % faster than PageRank algorithm . 2 ) Top web pages in PowerRank algorithm remain similar to that of the PageRank algorithm .

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 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

graph theory
page rank algorithm
power distribution


Recrawl scheduling based on information longevity It is crucial for a web crawler to distinguish between ephemeral and persistent content . Ephemeral content ( e.g. , quote of the day ) is usually not worth crawling , because by the time it reaches the index it is no longer representative of the web page from which it was acquired . On the other hand , content that persists across multiple page updates ( e.g. , recent blog postings ) may be worth acquiring , because it matches the page 's true content for a sustained period of time . In this paper we characterize the longevity of information found on the web , via both empirical measurements and a generative model that coincides with these measurements . We then develop new recrawl scheduling policies that take longevity into account . As we show via experiments over real web data , our policies obtain better freshness at lower cost , compared with previous approaches .

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

information search and retrieval
recrawling


Analysis of interacting BPEL web services This paper presents a set of tools and techniques for analyzing interactions of composite web services which are specified in BPEL and communicate through asynchronous XML messages . We model the interactions of composite web services as conversations , the global sequence of messages exchanged by the web services . As opposed to earlier work , our tool-set handles rich data manipulation via XPath expressions . This allows us to verify designs at a more detailed level and check properties about message content . We present a framework where BPEL specifications of web services are translated to an intermediate representation , followed by the translation of the intermediate representation to a verification language . As an intermediate representation we use guarded automata augmented with unbounded queues for incoming messages , where the guards are expressed as XPath expressions . As the target verification language we use Promela , input language of the model checker SPIN . Since SPIN model checker is a finite-state verification tool we can only achieve partial verification by fixing the sizes of the input queues in the translation . We propose the concept of synchronizability to address this problem . We show that if a composite web service is synchronizable , then its conversation set remains same when asynchronous communication is replaced with synchronous communication . We give a set of sufficient conditions that guarantee synchronizability and that can be checked statically . Based on our synchronizability results , we show that a large class of composite web services with unbounded input queues can be completely verified using a finite state model checker such as SPIN .

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

asynchronous communication
bpel
conversation
spin
synchronizability
web service
xpath

model checking
software/program verification
systems and information theory


Choreography conformance via synchronizability Choreography analysis has been a crucial problem in service oriented computing . Interactions among services involve message exchanges across organizational boundaries in a distributed computing environment , and in order to build such systems in a reliable manner , it is necessary to develop techniques for analyzing such interactions . Choreography conformance involves verifying that a set of services behave according to a given choreography specification that characterizes their interactions . Unfortunately this is an undecidable problem when services interact with asynchronous communication . In this paper we present techniques that identify if the interaction behavior for a set of services remain the same when asynchronous communication is replaced with synchronous communication . This is called the synchronizability problem and determining the synchronizability of a set of services has been an open problem for several years . We solve this problem in this paper . Our results can be used to identify synchronizable services for which choreography conformance can be checked efficiently . Our results on synchronizability are applicable to any software infrastructure that supports message-based interactions .

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 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 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


Query-free news search Many daily activities present information in the form of a stream of text , and often people can benefit from additional information on the topic discussed . TV broadcast news can be treated as one such stream of text ; in this paper we discuss finding news articles on the web that are relevant to news currently being broadcast . We evaluated a variety of algorithms for this problem , looking at the impact of inverse document frequency , stemming , compounds , history , and query length on the relevance and coverage of news articles returned in real time during a broadcast . We also evaluated several postprocessing techniques for improving the precision , including reranking using additional terms , reranking by document similarity , and filtering on document similarity . For the best algorithm , 84 % -91 % of the articles found were relevant , with at least 64 % of the articles being on the exact topic of the broadcast . In addition , a relevant article was found for at least 70 % of the topics .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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
on-line information services
query-free search
web information retrieval


StatSnowball : a statistical approach to extracting entity relationships Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples . Bootstrapping systems significantly reduce the number of training examples , but they usually apply heuristic-based methods to combine a set of strict hard rules , which limit the ability to generalize and thus generate a low recall . Furthermore , existing bootstrapping methods do not perform open information extraction ( Open IE ) , which can identify various types of relations without requiring pre-specifications . In this paper , we propose a statistical extraction framework called Statistical Snowball ( StatSnowball ) , which is a bootstrapping system and can perform both traditional relation extraction and Open IE . StatSnowball uses the discriminative Markov logic networks ( MLNs ) and softens hard rules by learning their weights in a maximum likelihood estimate sense . MLN is a general model , and can be configured to perform different levels of relation extraction . In StatSnwoball , pattern selection is performed by solving an l1-norm penalized maximum likelihood estimation , which enjoys well-founded theories and efficient solvers . We extensively evaluate the performance of StatSnowball in different configurations on both a small but fully labeled data set and large-scale Web data . Empirical results show that StatSnowball can achieve a significantly higher recall without sacrificing the high precision during iterations with a small number of seeds , and the joint inference of MLN can improve the performance . Finally , StatSnowball is efficient and we have developed a working entity relation search engine called Renlifang based on it .

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

relationship extraction
statistical models


Compressing and searching XML data via two zips XML is fast becoming the standard format to store , exchange and publish over the web , and is getting embedded in applications . Two challenges in handling XML are its size ( the XML representation of a document is significantly larger than its native state ) and the complexity of its search ( XML search involves path and content searches on labeled tree structures ) . We address the basic problems of compression , navigation and searching of XML documents . In particular , we adopt recently proposed theoretical algorithms ( 11 ) for succinct tree representations to design and implement a compressed index for XML , called XBZIPiNDEX , in which the XML document is maintained in a highly compressed format , and both navigation and searching can be done uncompressing only a tiny fraction of the data . This solution relies on compressing and indexing two arrays derived from the XML data . With detailed experiments we compare this with other compressed XML indexing and searching engines to show that XBZIPiNDEX has compression ratio up to 35 % better than the ones achievable by those other tools , and its time performance on some path and content search operations is order of magnitudes faster : few milliseconds over hundreds of MBs of XML files versus tens of seconds , on standard XML data sources .

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

labeled trees
xml compression and indexing


On optimal service selection While many works have been devoted to service matchmaking and modeling nonfunctional properties , the problem of matching service requests to offers in an optimal way has not yet been extensively studied . In this paper we formalize three kinds of optimal service selection problems , based on different criteria . Then we study their complexity and implement solutions . We prove that one-time costs make the optimal selection problem computationally hard ; in the absence of these costs the problem can be solved in polynomial time . We designed and implemented both exact and heuristic ( suboptimal ) algorithms for the hard case , and carried out a preliminary experimental evaluation with interesting results .

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

nonfunctional properties
service matchmaking

automatic service composition
service selection problem


Answering similarity queries in peer-to-peer networks


Modeling the author bias between two on-line computer science citation databases We examine the difference and similarities between two on-line computer science citation databases DBLP and CiteSeer . The database entries in DBLP are inserted manually while the CiteSeer entries are obtained autonomously . We show that the CiteSeer database contains considerably fewer single author papers . This bias can be modeled by an exponential process with intuitive explanation . The model permits us to predict that the DBLP database covers approximately 30 % of the entire literature of Computer Science .

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

acquisition bias
bibliometrics
miscellaneous


XHTML meta data profiles In this paper , we describe XHTML Meta Data Profiles ( XMDP ) which use XHTML to define a simple profile format which is both human and machine readable . XMDP can be used to extend XHTML by defining new link relationships , meta data properties\/values , and class name semantics . XMDP has already been used to extend semantic XHTML to represent social networks , document licensing , voting , and tagging .

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

link relationships
meta data
profiles
xhtml
xmdp

class names
html
lowercase semantic web
microformats
reuse
schema
world wide web
www
xfn


A framework for evaluating network measures for functional importance Many metrics such as degree , closeness , and PageRank have been introduced to determine the relative importance of a node within a network . The desired function of a network , however , is domain-specific . For example , the robustness can be crucial for a communication network , while efficiency is more preferred for fast spreading of advertisements in viral marketing . The information provided by some widely used measures are often conflicting under such varying demands . In this paper , we present a novel framework for evaluating network metrics regarding typical functional requirements . We also propose an analysis of five well established measures to compare their performance of ranking nodes on functional importance in a real-life network .

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 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 B I O O O O O


Hearsay : enabling audio browsing on hypertext content In this paper we present HearSay , a system for browsing hypertext Web documents via audio . The HearSay system is based on our novel approach to automatically creating audio browsable content from hypertext Web documents . It combines two key technologies : ( 1 ) automatic partitioning of Web documents through tightly coupled structural and semantic analysis , which transforms raw HTML documents into semantic structures so as to facilitate audio browsing ; and ( 2 ) VoiceXML , an already standardized technology which we adopt to represent voice dialogs automatically created from the XML output of partitioning . This paper describes the software components of HearSay and presents an initial system evaluation .

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

audio browser
software architectures
structural analysis
user interface
world wide web


Dynamic maintenance of web indexes using landmarks Recent work on incremental crawling has enabled the indexed document collection of a search engine to be more synchronized with the changing World Wide Web . However , this synchronized collection is not immediately searchable , because the keyword index is rebuilt from scratch less frequently than the collection can be refreshed . An inverted index is usually used to index documents crawled from the web . Complete index rebuild at high frequency is expensive . Previous work on incremental inverted index updates have been restricted to adding and removing documents . Updating the inverted index for previously indexed documents that have changed has not been addressed . In this paper , we propose an efficient method to update the inverted index for previously indexed documents whose contents have changed . Our method uses the idea of landmarks together with the diff algorithm to significantly reduce the number of postings in the inverted index that need to be updated . Our experiments verify that our landmark-diff method results in significant savings in the number of update operations on the inverted index .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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
inverted files
miscellaneous
update processing


Personal TV viewing by using live chat as metadata We propose a new TV viewing method by personalizing TV programs with live chat information on the Web . It enables a new way of viewing TV content from different perspectives reflecting viewers ' viewpoints .

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

digest
fusion of broadcast and web content
metadata generation
semantic analysis
video
viewer
viewpoint


Efficient structural joins with on-the-fly indexing Previous work on structural joins mostly focuses on maintaining offline indexes on disks . Most of them also require the elements in both sets to be sorted . In this paper , we study an on-the-fly , in-memory indexing approach to structural joins . There is no need to sort the elements or maintain indexes on disks . We identify the similarity between the structural join problem and the stabbing query problem , and extend a main memory-based indexing technique for stabbing queries to structural joins .

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 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 B I O

containment queries
information search and retrieval
xml


Modeling redirection in geographically diverse server sets Internet server selection mechanisms attempt to optimize , subject to a variety of constraints , the distribution of client requests to a geographically and topologically diverse pool of servers . Research on server selection has thus far focused primarily on techniques for choosing a server from a group administered by single entity , like a content distribution network provider . In a federated , multi-provider computing system , however , selection must occur over distributed server sets deployed by the participating providers , without the benefit of the full information available in the single-provider case . Intelligent server set selection algorithms will require a model of the expected performance clients would receive from a candidate server set . In this paper , we study whether the complex policies and dynamics of intelligent server selection can be effectively modeled in order to predict client performance for server sets . We introduce a novel server set distance metric , and use it in a measurement study of several million server selection transactions to develop simple models of existing server selection schemes . We then evaluate these models in terms of their ability to accurately predict performance for a second , larger set of distributed clients . We show that our models are able to predict performance within 20ms for over 90 % of the observed samples . Our analysis demonstrates that although existing deployments use a variety of complex and dynamic server selection criteria , most of which are proprietary , these schemes can be modeled with surprising accuracy .

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

content distribution network
server selection

performance of systems
web traffic redirection


Online spelling correction for query completion In this paper , we study the problem of online spelling correction for query completions . Misspelling is a common phenomenon among search engines queries . In order to help users effectively express their information needs , mechanisms for automatically correcting misspelled queries are required . Online spelling correction aims to provide spell corrected completion suggestions as a query is incrementally entered . As latency is crucial to the utility of the suggestions , such an algorithm needs to be not only accurate , but also efficient . To tackle this problem , we propose and study a generative model for input queries , based on a noisy channel transformation of the intended queries . Utilizing spelling correction pairs , we train a Markov n-gram transformation model that captures user spelling behavior in an unsupervised fashion . To find the top spell-corrected completion suggestions in real-time , we adapt the A \* search algorithm with various pruning heuristics to dynamically expand the search space efficiently . Evaluation of the proposed methods demonstrates a substantial increase in the effectiveness of online spelling correction over existing techniques .

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

query completion
spelling correction
transformation model


Adaptive web sites : user studies and simulation Adaptive web sites have been proposed to enhance ease of navigation and information retrieval . A variety of approaches are described in the literature , but consideration of interface presentation issues and realistic user studies are generally lacking . We report here a large-scale study of sites with dynamic information collections and user interests , where adaptation is based on an Ant Colony Optimization technique . We find that most users were able to locate information effectively without needing to perform explicit searches . The behavior of users who did search was similar to that on Internet search engines . Simulations based on site and user models give insight into the adaptive behavior and correspond to observations .

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

adaptive web site
learning
model validation and analysis


Scalable techniques for memory-efficient CDN simulations Since CDN simulations are known to be highly memory-intensive , in this paper , we argue the need for reducing the memory requirements of such simulations . We propose a novel memory-efficient data structure that stores cache state for a small subset of popular objects accurately and uses approximations for storing the state for the remaining objects . Since popular objects receive a large fraction of the requests while less frequently accessed objects consume much of the memory space , this approach yields large memory savings and reduces errors . We use bloom filters to store approximate state and show that careful choice of parameters can substantially reduce the probability of errors due to approximations . We implement our techniques into a user library for constructing proxy caches in CDN simulators . Our experimental results show up to an order of magnitude reduction in memory requirements of CDN simulations , while incurring a 5-10 % error .

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

approximate data structures
content distribution networks
simulation
web proxy cache


A classification based framework for concept summarization In this paper we propose a novel classification based framework for finding a small number of images summarizing a concept . Our method exploits metadata information available with the images to get the category information using Latent Dirichlet Allocation . We modify the import vector machine formulation based on kernel logistic regression to solve the underlying classification problem . We show that the import vectors provide a good summary satisfying important properties such as coverage , diversity and balance . Furthermore , the framework allows users to specify desired distributions over category , time etc , that a summary should satisfy . Experimental results show that the proposed method performs better than state-of-the-art summarization methods in terms of satisfying important visual and semantic properties .

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

concept and image summarization
miscellaneous


A fast XPATH evaluation technique with the facility of updates This paper addresses the problem of fast retrieval of data from XML documents by providing a labeling schema that can easily handle simple as well as complex XPATH queries and also provide for updates without the need for the entire document being re-indexed in the RDBMS . We introduce a new labeling schema called the `` Z-Label '' for efficiently processing XPATH queries involving child and descendant axes . The use of `` Z-Label '' coupled with the indexing schema provides for smooth updates in the XML document .

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 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 B O O B O O

biaxes path expression
dewey indexing
xpath query optimization


Gossip based streaming In this paper , we propose a novel multicast streaming protocol foroverlay networks , called Gossip Based Streaming ( GBS ) . In GBS , streaming contents are not come from a single upstream source , but delivered from several sources to a client . Though being similarto existing gossip protocols , the unique requirements forstreaming , such as continuous playback , are addressed in our design . Preliminary results show that GBS performs much better indynamic user environments .

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


Measurement and analysis of an online content voting network : a case study of Digg In online content voting networks , aggregate user activities ( e.g. , submitting and rating content ) make high-quality content thrive through the unprecedented scale , high dynamics and divergent quality of user generated content ( UGC ) . To better understand the nature and impact of online content voting networks , we have analyzed Digg , a popular online social news aggregator and rating website . Based on a large amount of data collected , we provide an in-depth study of Digg . We study structural properties of Digg social network , revealing some strikingly distinct properties such as low link symmetry and the power-law distribution of node outdegree with truncated tails . We explore impact of the social network on user digging activities , and investigate the issues of content promotion , content filtering , vote spam and content censorship , which are inherent to content rating networks . We also provide insight into design of content promotion algorithms and recommendation-assisted content discovery . Overall , we believe that the results presented in this paper are crucial in understanding online content rating networks .

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

miscellaneous
social and behavioral sciences
social networks


Living the TV revolution : unite MHP to the web or face IDTV irrelevance ! The union of Interactive Digital TV ( IDTV ) and Web promotes the development of new interactive multimedia services , enjoyable while watching TV even on the new handheld digital TV receivers . Yet , several design constraints complicate the deployment of this new pattern of services . Indeed , for a suitable presentation on a TV set , Web contents must be structured in such a way that they can be effectively displayed on TV screens via low-end Set Top Boxes ( STBs ) . Moreover , usable interfaces for IDTV platforms are needed which ensure a smooth access to contents . Our claim is that the distribution of Web contents over the IDTV broadcast channels may bring IDTV to a new life . A failure of this attempt may put IDTV on a progressive track towards irrelevance . We propose a system for the distribution of Web contents towards IDTV under the Digital Video Broadcasting - Multimedia Home Platform ( DVB-MHP ) standard . Our system is able to automatically transcode Web contents and ensure a proper visualization on IDTV . The system is endowed with a client application which permits to easily browse contents on the TV via a remote control . Real assessments have confirmed the effectiveness for such an automatic online service able to reconfigure Web contents for an appropriate distribution and presentation on IDTV .

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

computers in other systems
dvb
web contents transcoding


Defection detection : predicting search engine switching Searchers have a choice about which Web search engine they use when looking for information online . If they are unsuccessful on one engine , users may switch to a different engine to continue their search . By predicting when switches are likely to occur , the search experience can be modified to retain searchers or ensure a quality experience for incoming searchers . In this poster , we present research on a technique for predicting search engine switches . Our findings show that prediction is possible at a reasonable level of accuracy , particularly when personalization or user grouping is employed . These findings have implications for the design of applications to support more effective online searching .

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 O O O O O


An axiomatic approach for result diversification Understanding user intent is key to designing an effective ranking system in a search engine . In the absence of any explicit knowledge of user intent , search engines want to diversify results to improve user satisfaction . In such a setting , the probability ranking principle-based approach of presenting the most relevant results on top can be sub-optimal , and hence the search engine would like to trade-off relevance for diversity in the results . In analogy to prior work on ranking and clustering systems , we use the axiomatic approach to characterize and design diversification systems . We develop a set of natural axioms that a diversification system is expected to satisfy , and show that no diversification function can satisfy all the axioms simultaneously . We illustrate the use of the axiomatic framework by providing three example diversification objectives that satisfy different subsets of the axioms . We also uncover a rich link to the facility dispersion problem that results in algorithms for a number of diversification objectives . Finally , we propose an evaluation methodology to characterize the objectives and the underlying axioms . We conduct a large scale evaluation of our objectives based on two data sets : a data set derived from the Wikipedia disambiguation pages and a product database .

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

axiomatic framework
diversification
facility dispersion
search engine
wikipedia

approximation algorithms
information search and retrieval


Facetedpedia : dynamic generation of query-dependent faceted interfaces for wikipedia This paper proposes Facetedpedia , a faceted retrieval system for information discovery and exploration in Wikipedia . Given the set of Wikipedia articles resulting from a keyword query , Facetedpedia generates a faceted interface for navigating the result articles . Compared with other faceted retrieval systems , Facetedpedia is fully automatic and dynamic in both facet generation and hierarchy construction , and the facets are based on the rich semantic information from Wikipedia . The essence of our approach is to build upon the collaborative vocabulary in Wikipedia , more specifically the intensive internal structures ( hyperlinks ) and folksonomy ( category system ) . Given the sheer size and complexity of this corpus , the space of possible choices of faceted interfaces is prohibitively large . We propose metrics for ranking individual facet hierarchies by user 's navigational cost , and metrics for ranking interfaces ( each with k facets ) by both their average pairwise similarities and average navigational costs . We thus develop faceted interface discovery algorithms that optimize the ranking metrics . Our experimental evaluation and user study verify the effectiveness of the system .

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

data exploration
faceted search
information search and retrieval


Delay tolerant applications for low bandwidth and intermittently connected users : the aAQUA experience With the explosive growth and spread of Internet , web access from mobile and rural users has become significant . But these users face problems of low bandwidth and intermittent Internet connectivity . To make the benefits of the Internet reach the common man in developing countries , accessibility and availability of the information has to be improved . aAQUA is an online multilingual , multimedia agricultural portal for disseminating information from and to rural communities . Considering resource constrained rural environments , we have designed and implemented an offline solution which provides an online experience to users in disconnected mode . Our solution is based on heterogeneous database synchronization which involves only a small synchronization payload ensuring an efficient use of available bandwidth . Offline aAQUA has been deployed in the field and systematic studies of our solution show that user experience has improved tremendously not only in disconnected mode but also in connected mode .

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

information and communication technologies for development


Discovering geographical topics in the twitter stream Micro-blogging services have become indispensable communication tools for online users for disseminating breaking news , eyewitness accounts , individual expression , and protest groups . Recently , Twitter , along with other online social networking services such as Foursquare , Gowalla , Facebook and Yelp , have started supporting location services in their messages , either explicitly , by letting users choose their places , or implicitly , by enabling geo-tagging , which is to associate messages with latitudes and longitudes . This functionality allows researchers to address an exciting set of questions : 1 ) How is information created and shared across geographical locations , 2 ) How do spatial and linguistic characteristics of people vary across regions , and 3 ) How to model human mobility . Although many attempts have been made for tackling these problems , previous methods are either complicated to be implemented or oversimplified that can not yield reasonable performance . It is a challenge task to discover topics and identify users ' interests from these geo-tagged messages due to the sheer amount of data and diversity of language variations used on these location sharing services . In this paper we focus on Twitter and present an algorithm by modeling diversity in tweets based on topical diversity , geographical diversity , and an interest distribution of the user . Furthermore , we take the Markovian nature of a user 's location into account . Our model exploits sparse factorial coding of the attributes , thus allowing us to deal with a large and diverse set of covariates efficiently . Our approach is vital for applications such as user profiling , content recommendation and topic tracking . We show high accuracy in location estimation based on our model . Moreover , the algorithm identifies interesting topics based on location and language .

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

content analysis and indexing
general
geolocation
graphical model
language model
latent variable inference
topic models


A probabilistic approach to automated bidding in alternative auctions This paper presents an approach to develop bidding agents that participate in multiple alternative auctions , with the goal of obtaining an item at the lowest price . The approach consists of a prediction method and a planning algorithm . The prediction method exploits the history of past auctions in order to build probability functions capturing the belief that a bid of a given price may win a given auction . The planning algorithm computes the lowest price , such that by sequentially bidding in a subset of the relevant auctions , the agent can obtain the item at that price with an acceptable probability . The approach addresses the case where the auctions are for substitutable items with different values . Experimental results are reported , showing that the approach increases the payoff of their users and the welfare of the market .

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Generating summaries for large collections of geo-referenced photographs We describe a framework for automatically selecting a summary set of photographs from a large collection of geo-referenced photos . The summary algorithm is based on spatial patterns in photo sets , but can be expanded to support social , temporal , as well as textual-topical factors of the photo set . The summary set can be biased by the user , the content of the user 's query , and the context in which the query is made . An initial evaluation on a set of geo-referenced photos shows that our algorithm performs well , producing results that are highly rated by users .

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collection summary
geo-referenced information
geo-referenced photographs
information search and retrieval
photo browsing
photo collections
semantic zoom


An adaptive ontology-based approach to identify correlation between publications In this paper , we propose an adaptive ontology-based approach for related paper identification , to meet most researchers ' practical needs . By searching ontology , we can return a diverse set of papers that are explicitly and implicitly related to an input paper . Moreover , our approach does not rely on known ontology . Instead , we build and update ontology for a collection with any domain of interest . Being independent from known ontology , our approach is much more adaptive for different domains .

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adaptive approach
correlation of publications
information search and retrieval
ontology-based


Learning to re-rank : query-dependent image re-ranking using click data Our objective is to improve the performance of keyword based image search engines by re-ranking their original results . To this end , we address three limitations of existing search engines in this paper . First , there is no straight-forward , fully automated way of going from textual queries to visual features . Image search engines therefore primarily rely on static and textual features for ranking . Visual features are mainly used for secondary tasks such as finding similar images . Second , image rankers are trained on query-image pairs labeled with relevance judgments determined by human experts . Such labels are well known to be noisy due to various factors including ambiguous queries , unknown user intent and subjectivity in human judgments . This leads to learning a sub-optimal ranker . Finally , a static ranker is typically built to handle disparate user queries . The ranker is therefore unable to adapt its parameters to suit the query at hand which again leads to sub-optimal results . We demonstrate that all of these problems can be mitigated by employing a re-ranking algorithm that leverages aggregate user click data . We hypothesize that images clicked in response to a query are mostly relevant to the query . We therefore re-rank the original search results so as to promote images that are likely to be clicked to the top of the ranked list . Our re-ranking algorithm employs Gaussian Process regression to predict the normalized click count for each image , and combines it with the original ranking score . Our approach is shown to significantly boost the performance of the Bing image search engine on a wide range of tail queries .

O O O O O O 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 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 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

image re-ranking
information search and retrieval