The World Wide Web Conference is the global event that brings together key researchers, innovators, decision-makers, technologists, businesses, and standards bodies working to shape the Web. Organized by IW3C2 since 1994, the WWW conference is the annual opportunity for the International community to discuss and debate the evolution of the Web.
“The emergence of personalized homepage services, e.g. personalized Google Homepage and Microsoft Windows Live, has enabled Web users to select Web contents of interest and to aggregate them in a single Web page. The web contents are often predefined content blocks provided by the service providers. However, it involves intensive manual efforts to define the content blocks and maintain the information in it. In this paper, we propose a novel personalized homepage system, called "Homepage Live", to allow end users to use drag-and-drop actions to collect their favorite Web content blocks from existing Web pages and organize them in a single page. Moreover, Homepage Live automatically traces the changes of blocks with the evolvement of the container pages by measuring the tree edit distance of the selected blocks. By exploiting the immutable elements of Web pages, the tracing algorithm performance is significantly improved. The experimental results demonstrate the effectiveness and efficiency of our algorithm.”
“Service-level agreements (SLAs) establish a contract between service providers and clients concerning Quality of Service (QoS) parameters. Without proper penalties, service providers have strong incentives to deviate from the advertised QoS, causing losses to the clients. Reliable QoS monitoring (and proper penalties computed on the basis of delivered QoS) are therefor essential for the trustworthiness of a service-oriented environment. In this paper, we present a novel QoS monitoring mechanism based on quality ratings from the clients. A reputation mechanism collects the ratings and computes the actual quality delivered to the clients. The mechanism provides incentives for the clients to report honestly, and pays special attention to minimizing cost and overhead.”
“As part of a large effort to acquire large repositories of facts from unstructured text on the Web, a seed-based framework for textual information extraction allows for weakly supervised extraction of class attributes (e.g., side effects and generic equivalent for drugs) from anonymized query logs. The extraction is guided by a small set of seed attributes, without any need for handcrafted extraction patterns or further domain-specific knowledge. The attributes of classes pertaining to various domains of interest to Web search users have accuracy levels significantly exceeding current state of the art. Inherently noisy search queries are shown to be a highly valuable, albeit unexplored, resource for Web-based information extraction, in particular for the task of class attribute extraction.”
“A key challenge for dynamic Web service selection is that Web services are typically highly configurable and service requesters often have dynamic preferences on service configurations. Current approaches, such as WS-Agreement, describe Web services by enumerating the various possible service configurations, an inefficient approach when dealing with numerous service attributes with large value spaces. We model Web service configurations and associated prices and preferences more compactly using utility function policies, which also allows us to draw from multi-attribute decision theory methods to develop an algorithm for optimal service selection. In this paper, we present an OWL ontology for the specification of configurable Web service offers and requests, and a flexible and extensible framework for optimal service selection that combines declarative logic-based matching rules with optimization methods, such as linear programming. Assuming additive price/preference functions, experimental results indicate that our algorithm introduces an overhead of only around 2 sec.~compared to random service selection, while giving optimal results. The overhead, as percentage of total time, decreases as the number of offers and configurations increase.”
“Web processes must often operate in volatile environments where the quality of service parameters of the participating service providers change during the life time of the process. In order to remain optimal, the Web process must adapt to these changes. Adaptation requires knowledge about the parameter changes of each of the service providers and using this knowledge to determine whether the Web process should make a different more optimal decision. Previously, we defined a mechanism called the value of changed information which measures the impact of expected changes in the service parameters on the Web process, thereby offering a way to query and incorporate those changes that are useful and cost-efficient. However, computing the value of changed information incurs a substantial computational overhead. In this paper, we use service expiration times obtained from pre-defined service level agreements to reduce the computational overhead of adaptation. We formalize the intuition that services whose parameters have not expired need not be considered for querying for revised information. Using two realistic scenarios, we illustrate our approach and demonstrate the associated computational savings.”
“Automated matching of semantic service descriptions is the key to automatic service discovery and binding. But when trying to find a match for a certain request it may often happen, that the request cannot be serviced by a single offer but could be handled by combining existing offers. In this case automatic service composition is needed. Although automatic composition is an active field of research it is mainly viewed as a planning problem and treated separately from service discovery. In this paper we argue that an integrated approach to the problem is better than separating these issues as is usually done. We propose an approach that integrates service composition into service discovery and matchmaking to match service requests that ask for multiple connected effects, discuss general issues involved in describing and matching such services and present an efficient algorithm implementing our ideas.”
“Keyword search for smallest lowest common ancestors (SLCAs) in XML data has recently been proposed as a meaningful way to identify interesting data nodes inXML data where their subtrees contain an input set of keywords. In this paper, we generalize this useful search paradigm to support keyword search beyond the traditional AND semantics to include both AND and OR boolean operators as well. We first analyze properties of the LCA computation and propose improved algorithms to solve the traditional keyword search problem (with only AND semantics). We then extend our approach to handle general keyword search involving combinations of AND and OR boolean operators. The effectiveness of our new algorithms is demonstrated with a comprehensive experimental performance study.”
“Clio is an existing schema-mapping tool that provides user-friendly means to manage and facilitate the complex task of transformation and integration of heterogeneous data such as XML over the Web or in XML databases. By means of mappings from source to target schemas, Clio can help users conveniently establish the precise semantics of data transformation and integration. In this paper we study the problem of how to efficiently implement such data transformation (i.e., generating target data from the source data based on schema mappings). We present a three-phase framework for high-performance XML-to-XML transformation based on schema mappings, and discuss methodologies and algorithms for implementing these phases. In particular, we elaborate on novel techniques such as streamed extraction of mapped source values and scalable disk-based merging of overlapping data (including duplicate elimination). We compare our transformation framework with alternative methods such as using XQuery or SQL/XML provided by current commercial databases. The results demonstrate that the three-phase framework (although as simple as it is) is highly scalable and outperforms the alternative methods by orders of magnitude.”
“As XML database sizes grow, the amount of space used for storing the data and auxiliary data structures becomes a major factor in query and update performance. This paper presents a new storage scheme for XML data that supports all navigational operations in near constant time. In addition to supporting efficient queries, the space requirement of the proposed scheme is within a constant factor of the information theoretic minimum, while insertions and deletions can be performed in near constant time as well. As a result, the proposed structure features a small memory footprint that increases cache locality, whilst still supporting standard APIs, such as DOM, and necessary database operations, such as queries and updates, efficiently. Analysis and experiments show that the proposed structure is space and time efficient.”
“XML delivers key advantages in interoperability due to its flexibility, expressiveness, and platform-neutrality. As XML has become a performance-critical aspect of the next generation of business computing infrastructure, however, it has become increasingly clear that XML parsing often carries a heavy performance penalty, and that current, widely-used parsing technologies are unable to meet the performance demands of an XML-based computing infrastructure. Several efforts have been made to address this performance gap through the use of grammar-based parser generation. While the performance of generated parsers has been significantly improved, adoption of the technology has been hindered by the complexity of compiling and deploying the generated parsers. Through careful analysis of the operations required for parsing and validation, we have devised a set of specialized byte codes, designed for the task of XML parsing and validation. These byte codes are designed to engender the benefits of fine-grained composition of parsing and validation that make existing compiled parsers fast, while being coarse-grained enough to minimize interpreter overhead. This technique of using an interpretive, validating parser balances the need for performance against the requirements of simple tooling and robust scalable infrastructure. Our approach is demonstrated with a specialized schema compiler, used to generate byte codes which in turn drive an interpretive parser. With almost as little tooling and deployment complexity as a traditional interpretive parser, the byte code-driven parser usually demonstrates performance within 20% of the fastest fully compiled solutions.”
“Over the last five years, a range of projects have focused on progressively more elaborated techniques for adaptive news delivery. However, the adaptation process in these systems has become more complicated and thus less transparent to the users. In this paper, we concentrate on the application of open user models in adding transparency and controllability to adaptive news systems. We present a personalized news system, YourNews, which allows users to view and edit their interest profiles, and report a user study on the system. Our results confirm that users prefer transparency and control in their systems, and generate more trust to such systems. However, similar to previous studies, our study demonstrate that this ability to edit user profiles may also harm the system's performance and has to be used with caution.”
“We consider the problem of DUST: Different URLs with Similar Text. Such duplicate URLs are prevalent in web sites, as web server software often uses aliases and redirections, and dynamically generates the same page from various different URL requests. We present a novel algorithm, DustBuster, for uncovering DUST; that is, for discovering rules that transform a given URL to others that are likely to have similar content. DustBuster mines DUST effectively from previous crawl logs or web server logs, without examining page contents. Verifying these rules via sampling requires fetching few actual web pages. Search engines can benefit from information about DUST to increase the effectiveness of crawling, reduce indexing overhead, and improve the quality of popularity statistics such as PageRank.”
“Indian business clusters have contributed immensely to the country's industrial output, poverty alleviation and employment generation. However, with recent globalization these clusters can loose out to international competitors if they do not continuously innovate and take advantage of the new opportunities that are available through economic liberalization. In this paper, we discuss how information and communication technologies (ICT) can help in improving the productivity and growth of these clusters.”
“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.”
“This work proposes a novel cautious surfer to incorporate trust into the process of calculating authority for web pages. We evaluate a total of sixty queries over two large, real-world datasets to demonstrate that incorporating trust can improve PageRank's performance.”
“Traditional clustering algorithms work on "flat" data, making the assumption that the data instances can only be represented by a set of homogeneous and uniform features. Many real world data, however, is heterogeneous in nature, comprising of multiple types of interrelated components. We present a clustering algorithm, K-SVMeans, that integrates the well known K-Means clustering with the highly popular Support Vector Machines (SVM) in order to utilize the richness of data. Our experimental results on authorship analysis of scientific publications show that K-SVMeans achieves better clustering performance than homogeneous data clustering.”
“Search engines largely rely on Web robots to collect information from the Web. Due to the unregulated open-access nature of the Web, robot activities are extremely diverse. Such crawling activities can be regulated from the server side by deploying the Robots Exclusion Protocol in a file called robots.txt. Although it is not an enforcement standard, ethical robots (and many commercial) will follow the rules specified in robots.txt. With our focused crawler, we investigate 7,593 websites from education, government, news, and business domains. Five crawls have been conducted in succession to study the temporal changes. Through statistical analysis of the data, we present a survey of the usage of Web robots rules at the Web scale. The results also show that the usage of robots.txt has increased over time.”
“This paper introduces a novel link-based ranking algorithm based on a model of focused Web surfers. FocusedRank is described and compared to implementations of PageRank and Topic-Sensitive PageRank. We report a user study that measures the relevance and precision of each approach. FocusedRank gives superior relevancy over PageRank, while significantly reducing the computational complexity compared to the Topic-Senstive PageRank.”
“Hierarchical models are commonly used to organize a Website's content. A Website's content structure can be represented by a topic hierarchy, a directed tree rooted at a Website's homepage in which the vertices and edges correspond to Web pages and hyperlinks. In this work, we propose a new method for constructing the topic hierarchy of a Website. We model the Website's link structure using weighted directed graph, in which the edge weights are computed using a classifier that predicts if an edge connects a pair of nodes representing a topic and a sub-topic. We then pose the problem of building the topic hierarchy as finding the shortest-path tree and directed minimum spanning tree in the weighted graph. We've done extensive experiments using real Websites and obtained very promising results.”
“In this paper, we propose a novel Chinese word segmentation method which leverages the huge deposit of Web documents and search technology. It simultaneously solves ambiguous phrase boundary resolution and unknown word identification problems. Evaluations prove its effectiveness.”
“We present a family of measures of proximity of an arbitrary node in a directed graph to a pre-specified subset of nodes, called the anchor. Our measures are based on three different propagation schemes and two different uses of the connectivity structure of the graph. We consider a web-specific application of the above measures with two disjoint anchors -- good and bad web pages -- and study the accuracy of these measures in this context.”
“Performance evaluation is an important issue in Web search engine researches. Traditional evaluation methods rely on much human efforts and are therefore quite time-consuming. With click-through data analysis, we proposed an automatic search engine performance evaluation method. This method generates navigational type query topics and answers automatically based on search users. querying and clicking behavior. Experimental results based on a commercial Chinese search engine's user logs show that the automatically method gets a similar evaluation result with traditional assessor-based ones.”
“Tables are ubiquitous. Unfortunately, no search engine supports table search. In this paper, we propose a novel table specific searching engine, TableSeer, to facilitate the table extracting, indexing, searching, and sharing. In addition, we propose an extensive set of medium-independent metadata to precisely present tables. Given a query, TableSeer ranks the returned results using an innovative ranking algorithm -- TableRank with a tailored vector space model and a novel term weighting scheme. Experimental results show that TableSeer outperforms existing search engines on table search. In addition, incorporating multiple weighting factors can significantly improve the ranking results.”
“This paper makes an intensive investigation of the application of Bayesian network in sentence retrieval and introduces three Bayesian network based sentence retrieval models with or without consideration of term relationships. Term relationships in this paper are considered from two perspectives: relationships between pairs of terms and relationships between terms and term sets. Experiments have proven the efficiency of Bayesian network in the application of sentence retrieval. Particularly, retrieval result with consideration of the second kind of term relationship performs better in improving retrieval precision.”
“We investigate the effect of search engine brand (i.e., the identifying name or logo that distinguishes a product from its competitors) on evaluation of system performance. This research is motivated by the large amount of search traffic directed to a handful of Web search engines, even though most are of equal technical quality with similar interfaces. We conducted a laboratory study with 32 participants to measure the effect of four search engine brands while controlling for the quality of search engine results. There was a 25% difference between the most highly rated search engine and the lowest using average relevance ratings, even though search engine results were identical in both content and presentation. Qualitative analysis suggests branding affects user views of popularity, trust and specialization. We discuss implications for search engine marketing and the design of search engine quality studies.”
“In this paper, we study a new problem of mining causal relation of queries in search engine query logs. Causal relation between two queries means event on one query is the causation of some event on the other. We first detect events in query logs by efficient statistical frequency threshold. Then the causal relation of queries is mined by the geometric features of the events. Finally the Granger Causality Test (GCT) is utilized to further re-rank the causal relation of queries according to their GCT coefficients. In addition, we develop a 2-dimensional visualization tool to display the detected relationship of events in a more intuitive way. The experimental results on the MSN search engine query logs demonstrate that our approach can accurately detect the events in temporal query logs and the causal relation of queries is detected effectively.”
“In this paper, we present a novel method for the classification of Web sites. This method exploits both structure and content of Web sites in order to discern their functionality. It allows for distinguishing between eight of the most relevant functional classes of Web sites. We show that a pre-classification of Web sites utilizing structural properties considerably improves a subsequent textual classification with standard techniques. We evaluate this approach on a dataset comprising more than 16,000 Web sites with about 20 million crawled and 100 million known Web pages. Our approach achieves an accuracy of 92% for the coarse-grained classification of these Web sites.”
“PageRank is the best known technique for link-based importance ranking. The computed importance scores, however, are not directly comparable across different snapshots of an evolving graph. We present an efficiently computable normalization for PageRank scores that makes them comparable across graphs. Furthermore, we show that the normalized PageRank scores are robust to non-local changes in the graph, unlike the standard PageRank measure.”
“Due to resource constraints, Web archiving systems and search engines usually have difficulties keeping the entire local repository synchronized with the Web. We advance the state-of-art of the sampling-based synchronization techniques by answering a challenging question: Given a sampled webpage and its change status, which other webpages are also likely to change? We present a study of various downloading granularities and policies, and propose an adaptive model based on the update history and the popularity of the webpages. We run extensive experiments on a large dataset of approximately 300,000 webpages to demonstrate that it is most likely to find more updated webpages in the current or upper directories of the changed samples. Moreover, the adaptive strategies outperform the non-adaptive one in terms of detecting important changes.”
“Determining the user intent of Web searches is a difficult problem due to the sparse data available concerning the searcher. In this paper, we examine a method to determine the user intent underlying Web search engine queries. We qualitatively analyze samples of queries from seven transaction logs from three different Web search engines containing more than five million queries. From this analysis, we identified characteristics of user queries based on three broad classifications of user intent. The classifications of informational, navigational, and transactional represent the type of content destination the searcher desired as expressed by their query. We implemented our classification algorithm and automatically classified a separate Web search engine transaction log of over a million queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the classification to the results from our algorithm. This comparison showed that”
“In this paper, we propose a new system extracting potentially copyright infringement texts from the Web, called EPCI. EPCI extracts them in the following way: (1) generating a set of queries based on a given copyright reserved seed-text, (2) putting every query to search engine API, (3) gathering the search result Web pages from high ranking until the similarity between the given seed-text and the search result pages becomes less than a given threshold value, and (4) merging all the gathered pages, then re-ranking them in the order of their similarity. Our experimental result using 40 seed-texts shows that EPCI is able to extract 132 potentially copyright infringement Web pages per a given copyright reserved seed-text with 94% precision in average.”
“The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks. In this paper, we propose a Second Order Cone Programming (SOCP) based algorithm to train the model. We outline the theoretical derivatives of the biased classification model, and address the text classification tasks where negative training documents significantly outnumber the positive ones using the proposed strategy. We evaluated the learning scheme in comparison with traditional solutions on three different datasets. Empirical results have shown that our method is more effective and robust to handle imbalanced text classification problems.”
“The Netherlands had parliamentary elections on November 22, 2006. We built a system which helped voters to make an informed choice among the many participating parties. One of the most important pieces of information in the Dutch election and subsequent coalition government formation is the party program, a text document with an average length of 45 pages. Our system provides the voter with focused access to party programs, enabling her to make a topic-wise comparison of parties' viewpoints. We complemented this type of access ("What do the parties promise?") with access to news ("What happens around these topics?") and blogs ("What do people say about them?"). We describe the system, including design technical details, and user statistics.”
“A number of existing information retrieval systems propose the notion of query context to combine the knowledge of query and user into retrieval to reveal the most exact description of user's information needs. In this paper we interpret query context as a document consisting of sentences related to the current query. This kind of query context is used to re-estimate the relevance probabilities of top-ranked documents and then re-rank top-ranked documents. The experiments show that the proposed context-based approach for information retrieval can greatly improved relevance of search results.”
“This paper reports a new general framework of focused web crawling based on "relational subgroup discovery". Predicates are used explicitly to represent the relevance clues of those unvisited pages in the crawl frontier, and then first-order classification rules are induced using subgroup discovery technique. The learned relational rules with sufficient support and confidence will guide the crawling process afterwards. We present the many interesting features of our proposed first-order focused crawler, together with preliminary promising experimental results.”
“Given a document repository, search engine is very helpful to retrieve information. Currently, vertical search is a hot topic, and Google Scholar [4] is an example for academic search. However, most vertical search engines only return the flat ranked list without an efficient result exhibition for given users. We study this problem and designed a vertical search engine prototype Dolphin, where the flexible user-oriented templates can be defined and the survey-like results are presented according to the template.”
“Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or even share the same name with other people. In this paper, we present an efficient framework by using two novel topic-based models, extended from Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. Experiments indicate that our approach consistently outperforms other unsupervised methods including spectral and DBSCAN clustering. Scalability is addressed by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset.”
“A minimal perfect function maps a static set of n keys on to the range of integers {0,1,2,...,n - 1}. We present a scalable high performance algorithm based on random graphs for constructing minimal perfect hash functions (MPHFs). For a set of n keys, our algorithm outputs a description of h in expected time O(n). The evaluation of h(x) requires three memory accesses for any key x and the description of h takes up 0.89n bytes (7.13n bits). This is the best (most space efficient) known result to date. Using a simple heuristic and Huffman coding, the space requirement is further reduced to 0.79n bytes (6.86n bits). We present a high performance architecture that is easy to parallelize and scales well to very large data sets encountered in internet search applications. Experimental results on a one billion URL dataset obtained from Live Search crawl data, show that the proposed algorithm (a) finds an MPHF for one billion URLs in less than 4 minutes, and (b) requires only 6.86 bits/key for the description of h.”
“Current keyword-oriented search engines for the World Wide Web do not allow specifying the semantics of queries. We address this limitation with NAGA, a new semantic search engine. NAGA builds on a large semantic knowledge base of binary relationships (facts) derived from the Web. NAGA provides a simple, yet expressive query language to query this knowledge base. The results are then ranked with an intuitive scoring mechanism. We show the effectiveness and utility of NAGA by comparing its output with that of Googleon some interesting queries.”
“It is widely believed that some queries submitted to search engines are by nature ambiguous (e.g., java, apple). However, few studies have investigated the questions of "how many queries are ambiguous?" and "how can we automatically identify an ambiguous query?" This paper deals with these issues. First, we construct the taxonomy of query ambiguity, and ask human annotators to manually classify queries based upon it. From manually labeled results, we find that query ambiguity is to some extent predictable. We then use a supervised learning approach to automatically classify queries as being ambiguous or not. Experimental results show that we can correctly identify 87% of labeled queries. Finally, we estimate that about 16% of queries in a real search log are ambiguous.”
“Web pages are more than text and they contain much contextual and structural information, e.g., the title, the meta data, the anchor text, etc., each of which can be seen as a data source or are presentation. Due to the different dimensionality and different representing forms of these heterogeneous data sources, simply putting them together would not greatly enhance the classification performance. We observe that via a kernel function, different dimensions and types of data sources can be represented into a common format of kernel matrix, which can be seen as a generalized similarity measure between a pair of web pages. In this sense, a kernel learning approach is employed to fuse these heterogeneous data sources. The experimental results on a collection of the ODP database validate the advantages of the proposed method over traditional methods based on any single data source and the uniformly weighted combination of them.”
“Many text documents on the Web are not originally created but forwarded or copied from other source documents. The phenomenon of document forwarding or transmission between various web sites is denoted as Web information diffusion. This paper focuses on mining information diffusion processes for specific topics on the Web. A novel system called LIDPW is proposed to address this problem using matching learning techniques. The source site and source document of each document are identified and the diffusion process composed of a sequence of diffusion relationships is visually presented to users. The effectiveness of LIDPW is validated on a real data set. A preliminary user study is performed and the results show that LIDPW does benefit users to monitor the information diffusion process of a specific topic, and aid them to discover the diffusion start and diffusion center of the topic.”
“People are thirsty for medical information. Existing Web search engines cannot handle medical search well because they do not consider its special requirements. Often a medical information searcher is uncertain about his exact questions and unfamiliar with medical terminology. Therefore, he prefers to pose long queries, describing his symptoms and situation in plain English, and receive comprehensive, relevant information from search results. This paper presents MedSearch, a specialized medical Web search engine, to address these challenges. MedSearch can assist ordinary Internet users to search for medical information, by accepting queries of extended length, providing diversified search results, and suggesting related medical phrases.”
“Finding Contiguous Sequential Patterns (CSP) is an important problem in Web usage mining. In this paper we propose a new data structure, UpDown Tree, for CSP mining. An UpDown Tree combines suffix tree and prefix tree for efficient storage of all the sequences that contain a given item. The special structure of UpDown Tree ensures efficient detection of CSPs. Experiments show that UpDown Tree improves CSP mining in terms of both time and memory usage comparing to previous approaches.”
“In this paper, we describe a capture-recapture experiment conducted on Google's and MSN's cached directories. The anticipated outcome of this work was to monitor evolution rates in these web search services as well as measure their ability to index and maintain fresh and up-to-date results in their cached directories.”
“Search engines provide a small window to the vast repository of data they index and against which they search. They try their best to return the documents that are of relevance to the user but often a large number of results may be returned. Users struggle to manage this vast result set looking for the items of interest. Clustering search results is one way of alleviating this navigational pain. In this paper we describe a clustering system that enables clustering search results in an online marketplace search system.”
“This paper addresses the desktop search problem by considering various techniques for ranking results of a search query over the file system. First, basic ranking techniques, which are based on a single file feature (e.g., file name, file content, access date, etc.) are considered. Next, two learning-based ranking schemes are presented, and are shown to be significantly more effective than the basic ranking methods. Finally, a novel ranking technique, based on query selectiveness is considered, for use during the cold-start period of the system. This method is also shown to be empirically effective, even though it does not involve any learning.”
“We describe a query-driven indexing framework for scalable text retrieval over structured P2P networks. To cope with the bandwidth consumption problem that has been identified as the major obstacle for full-text retrieval in P2P networks, we truncate posting lists associated with indexing features to a constant size storing only top-k ranked document references. To compensate for the loss of information caused by the truncation, we extend the set of indexing features with carefully chosen term sets. Indexing term sets are selected based on the query statistics extracted from query logs, thus we index only such combinations that are a) frequently present in user queries and b) non-redundant w.r.t the rest of the index. The distributed index is compact and efficient as it constantly evolves adapting to the current query popularity distribution. Moreover, it is possible to control the tradeoff between the storage/bandwidth requirements and the quality of query answering by tuning the indexing parameters. Our theoretical analysis and experimental results indicate that we can indeed achieve scalable P2P text retrieval for very large document collections and deliver good retrieval performance.”
“In this paper, we show that most multiple term queries include more than one topic and users usually reformulate their queries by topics instead of terms. In order to provide empirical evidence on user's reformulation behavior and to help search engines better handle the query reformulation problem, we focus on detecting internal topics in the original query and analyzing users. reformulation to those topics. Particularly, we utilize the Interaction Information (II) to measure the degree of one sub-query being a topic based on the local search results. The experimental results on query log show that: most users reformulate query at the topical level; and our proposed II-based algorithm is a good method to detect topics from original queries.”
“It is now a common practice for e-commerce Web sites to enable their customers to write reviews of products that they have purchased. Such reviews provide valuable sources of information on these products. They are used by potential customers to find opinions of existing users before deciding to purchase a product. They are also used by product manufacturers to identify problems of their products and to find competitive intelligence information about their competitors. Unfortunately, this importance of reviews also gives good incentive for spam, which contains false positive or malicious negative opinions. In this paper, we make an attempt to study review spam and spam detection. To the best of our knowledge, there is still no reported study on this problem.”
“This paper presents a structured P2P overlay SCAN that augments CAN overlay with long links based on Kleinberg's small-world model in a d-dimensional Cartesian space. The construction of long links does not require the estimate of network size. Queries in multi-dimensional data space can achieve O(log n) hops by equipping each node with O(log n) long links and O(d) short links.”
“This paper presents SRing, a structured non DHT P2P overlay that efficiently supports exact and range queries on multiple attribute values. In SRing, all attribute values are interpreted as strings formed by a base alphabet and are published in the lexicographical order. Two virtual rings are built: N-ring is built in a skip-list way for range partition and queries; D-ring is built in a small-world way for the construction of N-ring. A leave-and-join based load balancing method is used to balance range overload in the network with heterogeneous nodes.”
“Researchers of commercial search engines often collect data using the application programming interface (API) or by "scraping" results from the web user interface (WUI), but anecdotal evidence suggests the interfaces produce different results. We provide the first in-depth quantitative analysis of the results produced by the Google, MSN and Yahoo API and WUI interfaces. After submitting a variety of queries to the interfaces for 5 months, we found significant discrepancies in several categories. Our findings suggest that the API indexes are not older, but they are probably smaller for Google and Yahoo. Researchers may use our findings to better understand the differences between the interfaces and choose the best API for their particular types of queries.”
“We present a new approach for propagating spam scores in web graphs, in order to combat link spam. The resulting spam rating is then used for propagating popularity scores like PageRank. Both propagations work even in presence of censure links that represent distrust. Initial testing using a C++ prototype on small examples show more reasonable results than other published approaches.”
“We conducted a series of experiments in which surveyed web search users answered questions about the quality of search results on the basis of the result summaries. Summaries shown to different groups of users were editorially constructed so that they differed in only one attribute, such as length. Some attributes had no effect on users' quality judgments, while in other cases, changing an attribute had a "halo effect" which caused seemingly unrelated dimensions of result quality to be rated higher by users.”
“In this paper, we describe an application, PubCloud that uses tag clouds for the summarization of results from queries over the PubMed database of biomedical literature. PubCloud responds to queries of this database with tag clouds generated from words extracted from the abstracts returned by the query. The results of a user study comparing the PubCloud tag-cloud summarization of query results with the standard result list provided by PubMed indicated that the tag cloud interface is advantageous in presenting descriptive information and in reducing user frustration but that it is less effective at the task of enabling users to discover relations between concepts.”
“In recent years, there has been a prevalence of search engines being employed to find useful information in the Web as they efficiently explore hyperlinks between web pages which define a natural graph structure that yields a good ranking. Unfortunately, current search engines cannot effectively rank those relational data, which exists on dynamic websites supported by online databases. In this study, to rank such structured data (i.e., find the "best" items), we propose an integrated online system consisting of compressed data structure to encode the dominant relationship of the relational data. Efficient querying strategies and updating scheme are devised to facilitate the ranking process. Extensive experiments illustrate the effectiveness and efficiency of our methods. As such, we believe the work in this poster can be complementary to traditional search engines.”
“Investigating whether one can view Web searching as a learning process, we examined the searching characteristics of 41 participants engaged in 246 searching tasks. We classified the searching tasks according an updated version of Bloom's taxonomy, a six level categorization of cognitive learning. Results show that Applying takes the most searching effort as measured by queries per session and specific topics searched per sessions. The lower level categories of Remembering and Understanding exhibit searching characteristics similar to the higher order learning of Evaluating and Creating. It appears that searchers rely primarily on their internal knowledge for Evaluating and Creating, using searching primarily as fact checking and verification. Implications are that the commonly held notion that Web searchers have simple information needs may not be correct. We discuss the implications for Web searching, including designing interfaces to support exploration.”
“Sequential patterns of d-gaps exist pervasively in inverted lists of Web document collection indices due to the cluster property. In this paper the information of d-gap sequential patterns is used as a new dimension for improving inverted index compression. We first detect d-gap sequential patterns using a novel data structure, UpDown Tree. Based on the detected patterns, we further substitute each pattern with its pattern Id in the inverted lists that contain it. The resulted inverted lists are then coded with an existing coding scheme. Experiments show that this approach can effectively improve the compression ratio of existing codes.”
“In this paper, we propose a new similarity measure to compute the pairwise similarity of text-based documents based on suffix tree document model. By applying the new suffix tree similarity measure in Group-average Agglomerative Hierarchical Clustering (GAHC) algorithm, we developed a new suffix tree document clustering algorithm (NSTC). Experimental results on two standard document clustering benchmark corpus OHSUMED and RCV1 indicate that the new clustering algorithm is a very effective document clustering algorithm. Comparing with the results of traditional word term weight tf-idf similarity measure in the same GAHC algorithm, NSTC achieved an improvement of 51% on the average of F-measure score. Furthermore, we apply the new clustering algorithm in analyzing the Web documents in online forum communities. A topic oriented clustering algorithm is developed to help people in assessing, classifying and searching the Web documents in a large forum community.”
“PageRank is known to be an efficient metric for computing general document importance in the Web. While commonly used as a one-size-fits-all measure, the ability to produce topically biased ranks has not yet been fully explored in detail. In particular, it was still unclear to what granularity of "topic" the computation of biased page ranks makes sense. In this paper we present the results of a thorough quantitative and qualitative analysis of biasing PageRank on Open Directory categories. We show that the MAP quality of Biased PageRank generally increases with the ODP level up to a certain point, thus sustaining the usage of more specialized categories to bias PageRank on, in order to improve topic specific search.”
“The results of the Web query log analysis may be significantly shifted depending on the fraction of agents (non-human clients), which are not excluded from the log. To detect and exclude agents the Web log studies use threshold values for a number of requests submitted by a client during the observation period. However, different studies use different observation periods, and a threshold assigned to one period is usually incomparable with the threshold assigned to the other period. We propose the uniform method equally working on the different observation periods. The method bases on the sliding window technique: a threshold is assigned to the sliding window rather than to the whole observation period. Besides, we determine the sub-optimal values of the parameters of the method: a window size and a threshold and recommend 5-7 unique queries as an upper bound of the threshold for 1-hour sliding window.”
“In this paper, we present a password stretching method using user specific salts. Our scheme takes similar time to stretch a password as recent password stretching algorithms, but the complexity of a pre-computation attack increases by 10^8 times and the storage required to store the pre-computation result increases by 10^8 times.”
“Automated email-based password reestablishment (EBPR) is an efficient, cost-effective means to deal with forgotten passwords. In this technique, email providers authenticate users on behalf of web sites. This method works because web sites trust email providers to deliver messages to their intended recipients. Simple Authentication for the Web (SAW) improves upon this basic approach to user authentication to create an alternative to password-based logins. SAW: 1) Removes the setup and management costs of passwords at sites that accept the risks of EBPR; 2) Provides single sign-on without a specialized identity provider; 3) Thwarts all passive attacks.”
“The unification of Semantic Web query languages under the SPARQL standard and the development of commercial-quality implementations are encouraging industries to use semantic technologies for managing information. Current implementations, however, lack the performance monitoring and management services that the industry expects. In this paper, we present a performance and management framework interface to a generic SPARQL web server. We leverage existing standards for instrumentation to make the system ready-to-manage through existing monitoring applications, and we provide a performance framework which has the distinct feature of providing measurement results through the same SPARQL interface used to query data, eliminating the need for special interfaces.”
“Service discovery is one of challenging issues in Service-Oriented computing. Currently, most of the existing service discovering and matching approaches are based on keywords-based strategy. However, this method is inefficient and time-consuming. In this paper, we present a novel approach for discovering web services. Based on the current dominating mechanisms of discovering and describing Web Services with UDDI and WSDL, the proposed approach utilizes Probabilistic Latent Semantic Analysis (PLSA) to capture semantic concepts hidden behind words in the query and advertisements in services so that services matching is expected to carry out at concept level. We also present related algorithms and preliminary experiments to evaluate the effectiveness of our approach.”
“We present a method for acquiring ontological knowledge using search query logs. We first use query logs to identify important contexts associated with terms belonging to a semantic category; we then use these contexts to harvest new words belonging to this category. Our evaluation on selected categories indicates that the method works very well to help harvesting terms, achieving 85% to 95% accuracy in categorizing newly acquired terms.”
“In this paper we extend the state-of-the-art in utilizing background knowledge for supervised classification by exploiting the semantic relationships between terms explicated in Ontologies. Preliminary evaluations indicate that the new approach generally improves precision and recall, more so for hard to classify cases and reveals patterns indicating the usefulness of such background knowledge.”
“This paper presents a semantic portal, SEMPort, which provides better user support with personalized views, semantic navigation, ontology-based search and three different kinds of semantic hyperlinks. Distributed content editing and provision is supplied for the maintenance of the contents in real-time. As a case study, SEMPort is tested on the Course Modules Web Page (CMWP) of the School of Electronics and Computer Science (ECS).”
“Figures in digital documents contain important information. Current digital libraries do not summarize and index information available within figures for document retrieval. We present our system on automatic categorization of figures and extraction of data from 2-D plots. A machine-learning based method is used to categorize figures into a set of predefined types based on image features. An automated algorithm is designed to extract data values from solid line curves in 2-D plots. The semantic type of figures and extracted data values from 2-D plots can be integrated with textual information within documents to provide more effective document retrieval services for digital library users. Experimental evaluation has demonstrated that our system can produce results suitable for real-world use.”
“This paper describes the development of a semantic web and ontology based local search system that can be used in wireless mobile communication services.”
“Most RDF query languages allow for graph structure search through a conjunction of triples which is typically processed using join operations. A key factor in optimizing joins is determining the join order which depends on the expected cardinality of intermediate results. This work proposes a pattern-based summarization framework for estimating the cardinality of RDF graph patterns. We present experiments on real world and synthetic datasets which confirm the feasibility of our approach.”
“Available methodologies for developing Sematic Web applications do not fully exploit the whole potential deriving from interaction with ontological data sources. Here we introduce an extension of the WebML modeling framework to fulfill most of the design requirements emerging for the new area of Semantic Web. We generalize the development process to support Semantic Web applications and we introduce a set of new primitives for ontology importing and querying.”
“In this paper, we propose a novel approach of image annotation by constructing a hierarchical mapping between low-level visual features and text features utilizing the relations within and across both visual features and text features. Moreover, we propose a novel annotation strategy that maximizes both the accuracy and the diversity of the generated annotation by generalizing or specifying the annotation in the corresponding annotation hierarchy. Experiments with 4500 scientific images from Royal Society of Chemistry journals show that the proposed annotation approach produces satisfactory results at different levels of annotations.”
“This paper presents a novel technique that significantly improves the quality of semantic Web service matching by (1) automatically generating ontologies based on Web service descriptions and (2) using these ontologies to guide the mapping between Web services.”
“We describe an approach designed to reduce the costs of ontology development through the use of untrained, volunteer knowledge engineers. Results are provided from an experiment in which volunteers were asked to judge the correctness of automatically inferred subsumption relationships in the biomedical domain. The experiment indicated that volunteers can be recruited fairly easily but that their attention is difficult to hold, that most do not understand the subsumption relationship without training, and that incorporating learned estimates of trust into voting systems is beneficial to aggregate performance.”
“Enriching Web applications with personalized data is of major interest for facilitating the user access to the published contents, and therefore, for guaranteeing successful user navigation. We propose a conceptual model for extracting personalized recommendations based on user profiling, ontological domain models, and semantic reasoning. The approach offers a high-level representation of the designed application based on a domain-specific metamodel for Web applications called WebML.”
“Scholarly entities, such as articles, journals, authors and institutions, are now mostly ranked according to expert opinion and citation data. The Andrew W. Mellon Foundation funded MESUR project at the Los Alamos National Laboratory is developing metrics of scholarly impact that can rank a wide range of scholarly entities on the basis of their usage. The MESUR project starts with the creation of a semantic network model of the scholarly community that integrates bibliographic, citation, and usage data collected from publishers and repositories world-wide. It is estimated that this scholarly semantic network will include approximately 50 million articles, 1 million authors, 10,000 journals and conference proceedings, 500 million citations, and 1 billion usage-related events; the largest scholarly semantic network ever created. The developed scholarly semantic network will then serve as a standardized platform for the definition and validation of new metrics of scholarly impact. This poster describes the MESUR project's data aggregation and processing techniques including the OWL scholarly ontology that was developed to model the scholarly communication process.”
“This paper describes a kernel based Web Services (abbreviated as service) matching mechanism for service discovery and integration. The matching mechanism tries to exploit the latent semantics by the structure of services. Using textual similarity and n-spectrum kernel values as features of low-level and mid-level, we build up a model to estimate the functional similarity between services, whose parameters are learned by a Ranking-SVM. The experiment results showed that several metrics for the retrieval of services have been improved by our approach.”
“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.”
“In current web service discovery and subscription, consumers must pay too much time on manually selection and cannot easily benefit from the wide QoS spectrum brought by the proliferating services. In our approach, we introduce the service pool as a "virtual service" grouping function identical services together and dispatching consumer requests to the proper service in terms of QoS requirements.”
“As Web services proliferate, size and magnitude of UDDI Business Registries (UBRs) are likely to increase. The ability to discover Web services of interest then across multiple UBRs becomes a major challenge specially when using primitive search methods provided by existing UDDI APIs. Clients do not have the time to endlessly search accessible UBRs for finding appropriate services particularly when operating via mobile devices. Finding services of interest should be time effective and highly productive. This paper addresses issues relating to the efficient access and discovery of Web services across multiple UBRs and introduces a novel exploration engine, the Web Service Crawler Engine (WSCE). WSCE is capable of crawling multiple UBRs, and enables for the establishment of a centralized Web services repository that can be used for discovering Web services much more efficiently. The paper presents experimental validation, results, and analysis of the proposed ideas.”
“Major research challenges in discovering Web services include, provisioning of services across multiple or heterogeneous registries, differentiating between services that share similar functionalities, improving end-to-end Quality of Service (QoS), and enabling clients to customize the discovery process. Proliferation and interoperability of this multitude of Web services have lead to the emergence of new standards on how services can be published, discovered, or used (i.e. UDDI, WSDL, SOAP). Such standards can potentially provide many of these features and much more, however, there are technical challenges associated with existing standards. One of these challenges is the client's ability to control the discovery process across accessible service registries for finding services of interest. This work proposes a solution to this problem and introduces the Web Service Relevancy Function (WsRF) used for measuring the relevancy ranking of a particular Web service based on QoS metrics and client preferences. We present experimental validation, results, and analysis of the presented ideas.”
“The goal of this poster is to describe our implementation of a new architecture enabling efficient integration between mobile phone applications and Web Services. Using this architecture, we have implemented a mobile shopping assistant described further. In order to build this architecture, we designed an innovative XML compression mechanism to facilitate data exchange between mobile phones and Web Services. We also designed a smart connection manager to control asynchronous communication for all possible channels of a mobile phone. In addition, we used diverse input modes in order to extend users' access to Web Services.”
“We develop a framework to compose services through discovery and orchestration for a given goal service. Tightening techniques are used in composition algorithms to achieve "completeness".”
“It is extremely hard for a global organization with services over multiple channels to capture a consistent and unified view of its data, services, and interactions. While SOA and web services are addressing integration and interoperability problems, it is painful for an operational organization with legacy systems to quickly switch to service-based methods. We need methods to combine advantages of traditional (i.e. web, desktop, or mobile) application development environments and service-based deployments. In this paper, we focus on the design and implementation of session management as a core service to support business processes and go beyond application-specific sessions and web sessions. We develop local session components for different platforms and complement them with a remote "session service" that is independent of applications and platforms. We aim to close the gap between the two worlds by combining their performance, availability and interoperability advantages.”
“This paper reports a safe regression test selection (RTS) approach that is designed for verifying Web services in an end-to-end manner. The Safe RTS technique has been integrated into a systematic method that monitors distributed code modifications and automates the RTS and RT processes.”
“The environment generated media (EGM) are defined here as being generated from a massive amount of and/or incomprehensible environmental data by compressing them into averages or representative values and/or by converting them into such user-friendly media as text, figures, charts, and animations. As an application of EGM, an object-participation-type weblog is introduced, where anthropomorphic indoor objects with sensor nodes post weblog entries and comments about what happened to them in a sensor networked environment.”
“We present BlogScope (www.blogscope.net), a system for analyzing the Blogosphere. BlogScope is an information discovery and text analysis system that offers a set of unique features. Such features include, spatio-temporal analysis of blogs, flexible navigation of the Blogosphere through information bursts, keyword correlations and burst synopsis, as well as enhanced ranking functions for improved query answer relevance. We describe the system, its design and the features of the current version of BlogScope.”
“In this paper, we present the design and implementation of our expertise oriented search system, EOS http://www.arnetminer.net. EOS is a researcher social network system. It has gathered information about a half-million computer science researchers from the Web and constructed a social network among the researchers through their co-authorship. In particular, the relationship in the social network information is used in both ranking experts for a given topic and searching for associations between researchers. Our experimental results demonstrate that the proposed methods for expert finding and association search in a social network are both more effective and efficient than the baseline methods.”
“It is now feasible to view media at home as easily as text-based pages were viewed when the World Wide Web (WWW) first emerged. This development has supported media sharing and search services providing hosting, indexing and access to large, online media repositories. Many of these sharing services also have a social aspect to them. This paper provides an initial analysis of the social interactions on a video sharing and search service (www.youtube.com). Results show that many users do not form social networks in the online community and a very small number do not appear to contribute to the wider community. However, it does seem those people who do use the available tools have much a greater tendency to form social connections.”
“Community analysis algorithm proposed by Clauset, Newman, and Moore (CNM algorithm) finds community structure in social networks. Unfortunately, CNM algorithm does not scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. We show that this inefficiency is caused from merging communities in unbalanced manner and that a simple heuristics that attempts to merge community structures in a balanced manner can dramatically improve community structure analysis. The proposed techniques are tested using data sets obtained from existing social networking service that hosts 5.5 million users. We have tested three three variations of the heuristics. The fastest method processes a SNS friendship network with 1 million users in 5 minutes (70 times faster than CNM) and another friendship network with 4 million users in 35 minutes, respectively. Another one processes a network with 500,000 nodes in 50 minutes (7 times faster than CNM), finds community structures that has improved modularity, and scales to a network with 5.5 million.”
“Recent trend in the development of mobile devices, wireless communications, sensor technologies, weblogs, and peer-to-peer communications have prompted a new design opportunity for enhancing social interactions. This paper introduces our preliminary experiences in designing a prototype utilizing the aforementioned technologies to share life experience. Users equipped with camera phones coupled with short-range communication technology, such as RFID, can capture life experience and share it as weblogs to other people. However, in reality, this is easier said than done. The success of weblogs relies on the active participation and willingness of people to contribute. To encourage active participations, a ranking system, AgreeRank, is specifically developed to get them motivated.”
“Learning Villages (LV) is an E-learning platform for people's online discussions and frequently citing postings of one another. In this paper, we propose a novel method to rank credit authors in the LV system. We first propose a k-EACM graph to describe the article citation structure in the LV system. And then we build a weighted graph model k-UCM graph to reveal the implicit relationship between authors hidden behind the citations among their articles. Furthermore, we design a graph-based ranking algorithm, the Credit Author Ranking (CAR) algorithm, which can be applied to rank nodes in a graph with negative edges. Finally, we perform experimental evaluations by simulations. The results of evaluations illustrate that the proposed method works pretty well on ranking the credibility of users in the LV system.”
“We propose a model for user purchase behavior in online stores that provide recommendation services. We model the purchase probability given recommendations for each user based on the maximum entropy principle using features that deal with recommendations and user interests. The proposed model enable us to measure the effect of recommendations on user purchase behavior, and the effect can be used to evaluate recommender systems. We show the validity of our model using the log data of an online cartoon distribution service, and measure the recommendation effects for evaluating the recommender system.”
“Given a huge online social network, how do we retrieve information from it through crawling? Even better, how do we improve the crawling performance by using parallel crawlers that work independently? In this paper, we present the framework of parallel crawlers for online social networks, utilizing a centralized queue. To show how this works in practice, we describe our implementation of the crawlers for an online auction website. The crawlers work independently, therefore the failing of one crawler does not affect the others at all. The framework ensures that no redundant crawling would occur. Using the crawlers that we built, we visited a total of approximately 11 million auction users, about 66,000 of which were completely crawled.”
“This paper presents Adaptive Web Search (AWS), a novel search technique that combines personalized, social, and real-time collaborative search. Preliminary empirical results from a small sample suggest that an AWS prototype built on WAMP platform using Yahoo! Web Search API generates more relevant results and allows faster discovery of information.”
“We address the problem of extracting semantics of tags -- short, unstructured text-labels assigned to resources on the Web -- based on each tag's metadata patterns. In particular, we describe an approach for extracting place and event semantics for tags that are assigned to photos on Flickr, a popular photo sharing website supporting time and location (latitude/longitude) metadata. The approach can be generalized to other domains where text terms can be extracted and associated with metadata patterns, such as geo-annotated web pages.”
“In a new model for answer retrieval, document collections are distilled offline into large repositories of facts. Each fact constitutes a potential direct answer to questions seeking a particular kind of entity or relation, such as questions asking about the date of particular events. Question answering becomes equivalent to online fact retrieval, which greatly simplifies the de-facto system architecture.”
“We present a load generator and performance measurement tool AutoPerf which requires minimal input and configuration from the user, and produces a comprehensive capacity analysis as well as server-side resource usage profile of a Web-based distributed system, in an automated fashion. The tool requires only the workload and deployment description of the distributed system, and automatically sets typical parameters that load generator programs need, such as maximum number of users to be emulated, number of users for each experiment, warm-up time, etc. The tool also does all the co-ordination required to generate a critical type of measure, namely, resource usage per transaction or per user for each software server. This is a necessary input for creating a performance model of a software system.”
“The success of many innovative Web applications is not based on the content they produce -- but on how they combine and link existing content. Older Web Engineering methods lack flexibility in a sense that they rely strongly on a-priori knowledge of existing content structures and do not take into account initially unknown content sources. We propose the adoption of principles that are also found in Component-based Software Engineering, to assemble highly extensible solutions from reusable artifacts. The main contribution of our work is a support system, consisting of a central service that manages n:m relationships between arbitrary Web resources, and of Web application components that realize navigation, presentation, and interaction for the linked content.”
“We propose a new system to mine visual knowledge on the Web. There are huge image data as well as text data on the Web. However, mining image data from the Web is paid less attention than mining text data, since treating semantics of images are much more difficult. In this paper, we propose introducing a latest image recognition technique, which is the bag-of-keypoints representation, into Web image-gathering task. By the experiments we show the proposed system outperforms our previous systems and Google Imagesearch greatly.”
“Mirroring Web sites is a well-known technique commonly used in the Web community. A mirror site should be updated frequently to ensure that it reflects the content of the original site. Existing mirroring tools apply page-level strategies to check each page of a site, which is inefficient and expensive. In this paper, we propose a novel site-level mirror maintenance strategy. Our approach studies the evolution of Web directory structures and mines association rules between ancestor-descendant Web directories. Discovered rules indicate the evolution correlations between Web directories. Thus, when maintaining the mirror of a Web site (directory), we can optimally skip subdirectories which are negatively correlated with it in undergoing significant changes. The preliminary experimental results show that our approach improves the efficiency of the mirror maintenance process significantly while sacrificing slightly in keeping the "freshness" of the mirrors.”
“We present an incremental algorithm for building a neighborhood graph from a set of documents. This algorithm is based on a population of artificial agents that imitate the way real ants build structures with self-assembly behaviors. We show that our method outperforms standard algorithms for building such neighborhood graphs (up to 2230 times faster on the tested databases with equal quality) and how the user may interactively explore the graph.”
“In a world where all devices will be interconnected, the boundaries between the different devices will start to disappear. Devices will be able to access each other's applications; sessions can be suspended on one device and resumed on another device; devices can serve as each other's input and output device, and all devices will be able to connect to the Internet. This will give true mobility to the user as he/she will not be restricted to the time and location where he/she accesses an application. Of course, we need a variety of different mechanisms and technologies to enable this, such as: Remote rendering of UIs on other devices in the network. Infrastructure for discovering client and servers in a network. Mechanisms to exchange capability information between devices, and to adapt the UI based on these capabilities. Mechanisms to deal with session migration. Support for a wide range of consumer devices, ranging from mobile phones to high-end TVs. This requires technologies that cross different domains, i.e. the PC domain, mobile domain, and TV domain. Several major companies within these different domains have decided to work together on these issues. One of the results is a framework for remote user interfaces for both UPnP" networks and the Internet. This framework is called Web4CE (a.k.a. CEA-2014) [1], and has been accepted as the baseline remote user interface technology within the Digital Living Network Alliance (DLNA) [2], which is a large industry-wide effort for creating true interoperability between network-enabled devices. This paper provides a short overview of the Web4CE framework, and some of the use cases that it enables.”
“The Web Mashup Scripting Language (WMSL) enables an end-user (you) working from his browser, e.g. not needing any other infrastructure, to quickly write mashups that integrate any two, or more, web services on the Web. The end-user accomplishes this by writing a web page that combines HTML, metadata in the form of mapping relations, and small piece of code, or script. The mapping relations enable not only the discovery and retrieval of the WMSL pages, but also affect a new programming paradigm that abstracts many programming complexities from the script writer. Furthermore, the WMSL Web pages or scripts that disparate end-users (you) write, can be harvested by Crawlers to automatically generate the concepts needed to build lightweight ontologies containing local semantics of a web service and its data model, to extend context ontologies or middle ontologies, and to develop links, or mappings, between these ontologies. This enables an open-source model of building ontologies based on the WMSL Web page or scripts that end users (you) write.”
“A SpeechWeb is a collection of hyperlinked applications, which are accessed remotely by speech browsers running on end-user devices. Links are activated through spoken commands. Despite the fact that protocols and technologies for creating and deploying speech applications have been readily available for several years, we have not seen the development of a Public-Domain SpeechWeb. In this paper, we show how freely available software and commonly used communication protocols can be used to change this situation.”
“In recent years, different commercial Weblog subscribing systems have been proposed to return stories from users. subscribed feeds. In this paper, we propose a novel clustering-based RSS aggregator called as RSS Clusgator System (RCS) for Weblog reading. Note that an RSS feed may have several different topics. A user may only be interested in a subset of these topics. In addition there could be many different stories from multiple RSS feeds, which discuss similar topic from different perspectives. A user may be interested in this topic but do not know how to collect all feeds related to this topic. In contrast to many previous works, we cluster all stories in RSS feeds into hierarchical structure to better serve the readers. Through this way, users can easily find all their interested stories. To make the system current, we propose a flexible time window for incremental clustering. RCS utilizes both link information and content information for efficient clustering. Experiments show the effectiveness of RCS.”
“Given a large collection of sparse vector data in a high dimensional space, we investigate the problem of finding all pairs of vectors whose similarity score (as determined by a function such as cosine distance) is above a given threshold. We propose a simple algorithm based on novel indexing and optimization strategies that solves this problem without relying on approximation methods or extensive parameter tuning. We show the approach efficiently handles a variety of datasets across a wide setting of similarity thresholds, with large speedups over previous state-of-the-art approaches.”
“Open information spaces have several unique characteristics such as their changeability, large size, complexity and diverse user base. These result in novel challenges during user navigation, information retrieval and data visualization in open information spaces. We propose a method of navigation in open information spaces based on an enhanced faceted browser with support for dynamic facet generation and adaptation based on user characteristics.”
“With the growth of social bookmarking a new approach for metadata creation called tagging has emerged. In this paper we evaluate the use of tag presentation techniques. The main goal of our evaluation is to investigate the effect of some of the different properties that can be utilized in presenting tags e.g. alphabetization, using larger fonts etc. We show that a number of these factors can affect the ease with which users can find tags and use the tools for presenting tags to users.”
“In this paper we propose the integration of intelligent components technologies (natural language and discourse management) in voice web interfaces to make them smarter. We describe how we have integrated reusable components of dialogue management and language processing in a multilingual voice system to improve its friendliness and portability. The dialogue management component deals with complex dialogue phenomena, such as user-initiative dialogues, and follows the information state-based theory. The resulting dialogue system supports friendly communication (through the telephone and the web) in several languages: English, Spanish, Catalan and Italian. The dialogue system has been adapted to guide the users to access online public administration services.”
“This paper describes our efforts to investigate factors in user's browsing behavior to automatically evaluate web pages that the user shows interest in. To evaluate web pages automatically, we developed a client-side logging/analyzing tool: the GINIS Framework. This work focuses primarily on client-side user behavior using a customized web browser and AJAX technologies. First, GINIS unobtrusively gathers logs of user behavior through the user's natural interaction with the web browser. Then it analyses the logs and extracts effective rules to evaluate web pages using C4.5 machine learning system. Eventually, GINIS becomes able to automatically evaluate web pages using these learned rules.”
“For many users with a disability it can be difficult or impossible to use a computer mouse to navigate the web. An alternative way to select elements on a web page is the label typing approach, in which users select elements by typing part of the label. In most cases, these labels are specified by the page authors, but some selectable elements do not have an obvious textual description, thus requiring that a label be generated. The set of element labels on a web page must be both efficient to select by text input and meaningful to the user. This paper discusses our approach to this problem, using page structural analysis and user history to determine important elements of a page, and then matching this information with the efficiency model of the input device.”
“This paper describes a comprehensive method for presenting mathematical equations and expressions using only pure HTML and CSS. This method renders the equations portable and editable and contrasts with previous procedures that represent equations as whole graphic objects. Methods for generating and documenting the equations using HTML and JavaScript are also described such that the equations can be interpreted and converted to or from other formats such as LaTex, MATHML, or linear representation.”
“The Web is rapidly moving towards a platform for mass collaboration in content production and consumption from three screens: computers, mobile phones, and TVs. While there has been a surge of interests in making Web content accessible from mobile devices, there is a significant lack of progress when it comes to making the web experience suitable for viewing on a television. Towards this end, we describe a novel concept, namely GeoTV, where we explore a framework by which web content can be presented or pushed in a meaningful manner to create an entertainment experience for the TV audience. Fresh content on a variety of topics, people, and places is being created and made available on the Web at breathtaking speed. Navigating fresh content effectively on TV demands a new browsing paradigm that requires fewer mouse clicks or user interactions from the remote control. Novel geospatial and temporal browsing techniques are provided in GeoTV that allow users the capability of aggregating and navigating RSS-enabled content in a timely, personalized and automatic manner for viewing in an IPTV environment. This poster is an extension of our previous work on GeoTracker that utilizes both a geospatial representation and a temporal (chronological) presentation to help users spot the most relevant updates quickly within the context of a Web-enabled environment. We demonstrate 1) the usability of such a tool that greatly enhances a user's ability in locating and browsing videos based on his or her geographical interests and 2) various innovative interface designs for showing RSS-enabled information in an IPTV environment.”
“The availability of map interfaces and location-aware devices makes a growing amount of unstructured, geo-referenced information available on the Web. In particular, over twelve million geo-referenced photos are now available on Flickr, a popular photo-sharing website. We show a method to analyze the Flickr data and generate aggregate knowledge in the form of "representative tags" for arbitrary areas in the world. We display these tags on a map interface in an interactive web application along with images associated with each tag. We then use the implicit feedback of the aggregate user interactions with the tags and images to learn which images best describe the area shown on the map.”
“We propose a system for reminding a user of information obtained through a web browsing experience. The system extracts keywords from the content of the web page currently being viewed and retrieves the context of past web browsing related to the keywords. We define the context as a sequence of web browsing when many web pages related to the keyword were viewed intensively because we assume that a lot of information connected to the current content was obtained in the sequence. The information is not only what pages you viewed but also how you found those pages and what knowledge you acquired from them. Specifically, when you browse web pages, this system automatically displays a list of the contexts judged to be important in relation to the current web page. If you select the context, details of the context are shown graphically with marks indicating characteristic activities.”
“The World Wide Web is a powerful platform for a wide range of information tasks. Dramatic advances in technology, such as improved search capabilities and the AJAX application model, have enabled entirely new web-based applications and usage patterns, making many tasks easier to perform than ever before. However, few tools have been developed to assist with sensemaking tasks: complex research behaviors in which users gather and comprehend information from many sources to answer potentially vague, non-procedural questions. Sensemaking tasks are common and include, for example, researching vacation destinations or deciding how to invest. This paper presents the ScratchPad, an extension to the standard browser interface that is designed to capture, organize, and exploit the information discovered while performing a sensemaking task.”
“Generally speaking, digital libraries have multiple granularities of semantic units: book, chapter, page, paragraph and word. However, there are two limitations of current eBook retrieval systems: (1) the granularity of retrievable units is either too big or too small, scales such as chapters, paragraphs are ignored; (2) the retrieval results should be grouped by facets to facilitate user's browsing and exploration. To overcome these limitations, we propose a multi-granularity multi-facet eBook retrieval approach.”
“We present DescribeX, a tool for exploring and visualizing the structural patterns present in collections of XML documents. DescribeX can be employed by developers to interactively discover those XPath expressions that will actually return elements present in a collection of XML files.”
“We describe an adaptive method for extracting records from web pages. Our algorithm combines a weighted tree matching metric with clustering for obtaining data extraction patterns. We compare our method experimentally to the state-of-the-art, and show that our approach is very competitive for rigidly-structured records (such as product descriptions) and far superior for loosely-structured records (such as entries on blogs).”
“In XML databases, materializing queries and their results into views in a semantic cache can improve the performance of query evaluation by reducing computational complexity and I/O cost. Although there are a number of proposals of semantic cache for XML queries, the issues of fast cache lookup and compensation query construction could be further studied. In this paper, based on sequential XPath queries, we propose fastCLU, a fast Cache LookUp algorithm and effiCQ, an efficient Compensation Query constructing algorithm to solve these two problems. Experimental results show that our algorithms outperform previous algorithms and can achieve good performance of query evaluation.”
“XML Schema documents are defined using an XML syntax, which means that the idea of generating schema documentation through standard XML technologies is intriguing. We present X2Doc, a framework for generating schema-documentation solely through XSLT. The framework uses SCX, an XML syntax for XML Schema components, as intermediate format and produces XML-based output formats. Using a modular set of XSLT stylesheets, X2Doc is highly configurable and carefully crafted towards extensibility. This proves especially useful for composite schemas, where additional schema information like Schematron rules are embedded into XML Schemas.”
“In XML databases, new schema versions may be released as frequently as once every two weeks. This poster describes a taxonomy of changes for XML schema evolution. It examines the impact of those changes on schema validation and query evaluation. Based on that study, it proposes guidelines for XML schema evolution and for writing queries in such a way that they continue to operate as expected across evolving schemas.”
“XML is increasingly being used as a typed data format, and therefore it becomes more important to gain access to the type system; very often this is an XML Schema. The XML Schema Path Language (SPath) presented in this paper provides access to XML Schema components by extending the well-known XPath language to also include the domain of XML Schemas. Using SPath, XML developers gain access to XML Schemas and thus can more easily develop software which is type- or schema-aware, and thus more robust.”
“Since conventional historical records have been written assuming human readers, they are not well-suited for computers to collect and process automatically. If computers could understand descriptions in historical records and process them automatically, it would be easy to analyze them from different perspectives. In this paper, we review a number of existing frameworks used to describe historical events, and make a comparative assessment of these frameworks in terms of usability, based on "deep cases" of Fillmore's core grammar. Based on this assessment, we propose a new description framework, and have created a microformat vocabulary set suitable for that framework.”
“In this paper, we describe a system that can extract record structures from web pages with no direct human supervision. Records are commonly occurring HTML-embedded data tuples that describe people, offered courses, products, company profiles, etc. We present a simplified framework for studying the problem of unsupervised record extraction. one which separates the algorithms from the feature engineering. Our system, U-REST formalizes an approach to the problem of unsupervised record extraction using a simple two-stage machine learning framework. The first stage involves clustering, where structurally similar regions are discovered, and the second stage involves classification, where discovered groupings (clusters of regions) are ranked by their likelihood of being records. In our work, we describe, and summarize the results of an extensive survey of features for both stages. We conclude by comparing U-REST to related systems. The results of our empirical evaluation show encouraging improvements in extraction accuracy.”
“In this paper, we consider a way to represent contact center applications as a set of multiple XML documents written in different markups including VoiceXML and CCXML. Applications can comprise a dialog with IVR, call routing and agent scripting functionalities. We also consider ways how such applications can be executed in run-time contact center environment.”
“XML Schema's abstract data model consists of components, which are the structures that eventually define a schema as a whole. XML Schema's XML syntax, on the other hand, is not a direct representation of the schema components, and it proves to be surprisingly hard to derive a schema's components from the XML syntax. The Schema Component XML Syntax (SCX) is a representation which attempts to map schema components as faithfully as possible to XML structures. SCX serves as the starting point for applications which need access to schema components and want to do so using standardized and widely available XML technologies.”
“Near-duplicate web documents are abundant. Two such documents differ from each other in a very small portion that displays advertisements, for example. Such differences are irrelevant for web search. So the quality of a web crawler increases if it can assess whether a newly crawled web page is a near-duplicate of a previously crawled web page or not. In the course of developing a near-duplicate detection system for a multi-billion page repository, we make two research contributions. First, we demonstrate that Charikar's fingerprinting technique is appropriate for this goal. Second, we present an algorithmic technique for identifying existing f-bit fingerprints that differ from a given fingerprint in at most k bit-positions, for small k. Our technique is useful for both online queries (single fingerprints) and all batch queries (multiple fingerprints). Experimental evaluation over real data confirms the practicality of our design.”
“Demographic information plays an important role in personalized web applications. However, it is usually not easy to obtain this kind of personal data such as age and gender. In this paper, we made a first approach to predict users' gender and age from their Web browsing behaviors, in which the Webpage view information is treated as a hidden variable to propagate demographic information between different users. There are three main steps in our approach: First, learning from the Webpage click-though data, Webpages are associated with users' (known) age and gender tendency through a discriminative model; Second, users' (unknown) age and gender are predicted from the demographic information of the associated Webpages through a Bayesian framework; Third, based on the fact that Webpages visited by similar users may be associated with similar demographic tendency, and users with similar demographic information would visit similar Webpages, a smoothing component is employed to overcome the data sparseness of web click-though log. Experiments are conducted on a real web click-through log to demonstrate the effectiveness of the proposed approach. The experimental results show that the proposed algorithm can achieve up to 30.4% improvements on gender prediction and 50.3% on age prediction in terms of macro F1, compared to baseline algorithms.”
“The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper, we take a first step at achieving this goal. We present a large scale study correlating the behaviors of Internet users on multiple systems ranging in size from 27 million queries to 14 million blog posts to 20,000 news articles. We formalize a model for events in these time-varying datasets and study their correlation. We have created an interface for analyzing the datasets, which includes a novel visual artifact, the DTWRadar, for summarizing differences between time series. Using our tool we identify a number of behavioral properties that allow us to understand the predictive power of patterns of use.”
“In this paper, we define the problem of topic-sentiment analysis on Weblogs and propose a novel probabilistic model to capture the mixture of topics and sentiments simultaneously. The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments. It could also provide general sentiment models that are applicable to any ad hoc topics. With a specifically designed HMM structure, the sentiment models and topic models estimated with TSM can be utilized to extract topic life cycles and sentiment dynamics. Empirical experiments on different Weblog datasets show that this approach is effective for modeling the topic facets and sentiments and extracting their dynamics from Weblog collections. The TSM model is quite general; it can be applied to any text collections with a mixture of topics and sentiments, thus has many potential applications, such as search result summarization, opinion tracking, and user behavior prediction.”
“In a social network, nodes correspond to people or other social entities, and edges correspond to social links between them. In an effort to preserve privacy, the practice of anonymization replaces names with meaningless unique identifiers. We describe a family of attacks such that even from a single anonymized copy of a social network, it is possible for an adversary to learn whether edges exist or not between specific targeted pairs of nodes.”
“Information flows in a network where individuals influence each other. The diffusion rate captures how efficiently the information can diffuse among the users in the network. We propose an information flow model that leverages diffusion rates for: (1) prediction . identify where information should flow to, and (2) ranking . identify who will most quickly receive the information. For prediction, we measure how likely information will propagate from a specific sender to a specific receiver during a certain time period. Accordingly a rate-based recommendation algorithm is proposed that predicts who will most likely receive the information during a limited time period. For ranking, we estimate the expected time for information diffusion to reach a specific user in a network. Subsequently, a DiffusionRank algorithm is proposed that ranks users based on how quickly information will flow to them. Experiments on two datasets demonstrate the effectiveness of the proposed algorithms to both improve the recommendation performance and rank users by the efficiency of information flow.”
“Given a large online network of online auction users and their histories of transactions, how can we spot anomalies and auction fraud? This paper describes the design and implementation of NetProbe, a system that we propose for solving this problem. NetProbe models auction users and transactions as a Markov Random Field tuned to detect the suspicious patterns that fraudsters create, and employs a Belief Propagation mechanism to detect likely fraudsters. Our experiments show that NetProbe is both efficient and effective for fraud detection. We report experiments on synthetic graphs with as many as 7,000 nodes and 30,000 edges, where NetProbe was able to spot fraudulent nodes with over 90% precision and recall, within a matter of seconds. We also report experiments on a real dataset crawled from eBay, with nearly 700,000 transactions between more than 66,000users, where NetProbe was highly effective at unearthing hidden networks of fraudsters, within a realistic response time of about 6 minutes. For scenarios where the underlying data is dynamic in nature, we propose IncrementalNetProbe, which is an approximate, but fast, variant of NetProbe. Our experiments prove that Incremental NetProbe executes nearly doubly fast as compared to NetProbe, while retaining over 99% of its accuracy.”
“Understanding the extent to which people's search behaviors differ in terms of the interaction flow and information targeted is important in designing interfaces to help World Wide Web users search more effectively. In this paper we describe a longitudinal log-based study that investigated variability in people's interaction behavior when engaged in search-related activities on the Web. We analyze the search interactions of more than two thousand volunteer users over a five-month period, with the aim of characterizing differences in their interaction styles. The findings of our study suggest that there are dramatic differences in variability in key aspects of the interaction within and between users, and within and between the search queries they submit. Our findings also suggest two classes of extreme user. navigators and explorers. whose search interaction is highly consistent or highly variable. Lessons learned from these users can inform the design of tools to support effective Web-search interactions for everyone.”
“The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including whether coherent categorization schemes can emerge from unsupervised tagging by users. This paper uses data from the social bookmarking site delicio. us to examine the dynamics of collaborative tagging systems. In particular, we examine whether the distribution of the frequency of use of tags for "popular" sites with a long history (many tags and many users) can be described by a power law distribution, often characteristic of what are considered complex systems. We produce a generative model of collaborative tagging in order to understand the basic dynamics behind tagging, including how a power law distribution of tags could arise. We empirically examine the tagging history of sites in order to determine how this distribution arises over time and to determine the patterns prior to a stable distribution. Lastly, by focusing on the high-frequency tags of a site where the distribution of tags is a stabilized power law, we show how tag co-occurrence networks for a sample domain of tags can be used to analyze the meaning of particular tags given their relationship to other tags.”
“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.”
“Enterprise and web data processing and content aggregation systems often require extensive use of human-reviewed data (e.g. for training and monitoring machine learning-based applications). Today these needs are often met by in-house efforts or out-sourced offshore contracting. Emerging applications attempt to provide automated collection of human-reviewed data at Internet-scale. We conduct extensive experiments to study the effectiveness of one such application. We also study the feasibility of using Yahoo! Answers, a general question-answering forum, for human-reviewed data collection.”
“Click fraud is jeopardizing the industry of Internet advertising. Internet advertising is crucial for the thriving of the entire Internet, since it allows producers to advertise their products, and hence contributes to the well being of e-commerce. Moreover, advertising supports the intellectual value of the Internet by covering the running expenses of publishing content. Some content publishers are dishonest, and use automation to generate traffic to defraud the advertisers. Similarly, some advertisers automate clicks on the advertisements of their competitors to deplete their competitors' advertising budgets. This paper describes the advertising network model, and focuses on the most sophisticated type of fraud, which involves coalitions among fraudsters. We build on several published theoretical results to devise the Similarity-Seeker algorithm that discovers coalitions made by pairs of fraudsters. We then generalize the solution to coalitions of arbitrary sizes. Before deploying our system on a real network, we conducted comprehensive experiments on data samples for proof of concept. The results were very accurate. We detected several coalitions, formed using various techniques, and spanning numerous sites. This reveals the generality of our model and approach.”
“Often scientists seek to search for articles on the Web related to a particular chemical. When a scientist searches for a chemical formula using a search engine today, she gets articles where the exact keyword string expressing the chemical formula is found. Searching for the exact occurrence of keywords during searching results in two problems for this domain: a) if the author searches for CH4 and the article has H4C, the article is not returned, and b) ambiguous searches like "He" return all documents where Helium is mentioned as well as documents where the pronoun "he" occurs. To remedy these deficiencies, we propose a chemical formula search engine. To build a chemical formula search engine, we must solve the following problems: 1) extract chemical formulae from text documents, 2) index chemical formulae, and 3) design ranking functions for the chemical formulae. Furthermore, query models are introduced for formula search, and for each a scoring scheme based on features of partial formulae is proposed to measure the relevance of chemical formulae and queries. We evaluate algorithms for identifying chemical formulae in documents using classification methods based on Support Vector Machines (SVM), and a probabilistic model based on conditional random fields (CRF). Different methods for SVM and CRF to tune the trade-off between recall and precision for imbalanced data are proposed to improve the overall performance. A feature selection method based on frequency and discrimination is used to remove uninformative and redundant features. Experiments show that our approaches to chemical formula extraction work well, especially after trade-off tuning. The results also demonstrate that feature selection can reduce the index size without changing ranked query results much.”
“Several approaches to collaborative filtering have been studied but seldom have studies been reported for large (several million users and items) and dynamic (the underlying item set is continually changing) settings. In this paper we describe our approach to collaborative filtering for generating personalized recommendations for users of Google News. We generate recommendations using three approaches: collaborative filtering using MinHash clustering, Probabilistic Latent Semantic Indexing (PLSI), and covisitation counts. We combine recommendations from different algorithms using a linear model. Our approach is content agnostic and consequently domain independent, making it easily adaptable for other applications and languages with minimal effort. This paper will describe our algorithms and system setup in detail, and report results of running the recommendations engine on Google News.”
“Weblogs have become a prevalent source of information for people to express themselves. In general, there are two genres of contents in weblogs. The first kind is about the webloggers' personal feelings, thoughts or emotions. We call this kind of weblogs affective articles. The second kind of weblogs is about technologies and different kinds of informative news. In this paper, we present a machine learning method for classifying informative and affective articles among weblogs. We consider this problem as a binary classification problem. By using machine learning approaches, we achieve about 92% on information retrieval performance measures including precision, recall and F1. We set up three studies on the applications of above classification approach in both research and industrial fields. The above classification approach is used to improve the performance of classification of emotions from weblog articles. We also develop an intent-driven weblog-search engine based on the classification techniques to improve the satisfaction of Web users. Finally, our approach is applied to search for weblogs with a great deal of informative articles.”
“Spammers use questionable search engine optimization (SEO) techniques to promote their spam links into top search results. In this paper, we focus on one prevalent type of spam -- redirection spam -- where one can identify spam pages by the third-party domains that these pages redirect traffic to. We propose a five-layer, double-funnel model for describing end-to-end redirection spam, present a methodology for analyzing the layers, and identify prominent domains on each layer using two sets of commercial keywords. one targeting spammers and the other targeting advertisers. The methodology and findings are useful for search engines to strengthen their ranking algorithms against spam, for legitimate website owners to locate and remove spam doorway pages, and for legitimate advertisers to identify unscrupulous syndicators who serve ads on spam pages.”
“Generic database replication algorithms do not scale linearly in throughput as all update, deletion and insertion (UDI) queries must be applied to every database replica. The throughput is therefore limited to the point where the number of UDI queries alone is sufficient to overload one server. In such scenarios, partial replication of a database can help, as UDI queries are executed only by a subset of all servers. In this paper we propose GlobeTP, a system that employs partial replication to improve database throughput. GlobeTP exploits the fact that a Web application's query workload is composed of a small set of read and write templates. Using knowledge of these templates and their respective execution costs, GlobeTP provides database table placements that produce significant improvements in database throughput. We demonstrate the efficiency of this technique using two different industry standard benchmarks. In our experiments, GlobeTP increases the throughput by 57% to 150% compared to full replication, while using identical hardware configuration. Furthermore, adding a single query cache improves the throughput by another 30% to 60%.”
“Web sites are designed for graphical mode of interaction. Sighted users can "cut to the chase" and quickly identify relevant information in Web pages. On the contrary, individuals with visual disabilities have to use screen-readers to browse the Web. As screen-readers process pages sequentially and read through everything, Web browsing can become strenuous and time-consuming. Although, the use of shortcuts and searching offers some improvements, the problem still remains. In this paper, we address the problem of information overload in non-visual Web access using the notion of context. Our prototype system, CSurf, embodying our approach, provides the usual features of a screen-reader. However, when a user follows a link, CSurf captures the context of the link using a simple topic-boundary detection technique, and uses it to identify relevant information on the next page with the help of a Support Vector Machine, a statistical machine-learning model. Then, CSurf reads the Web page starting from the most relevant section, identified by the model. We conducted a series experiments to evaluate the performance of CSurf against the state-of-the-art screen-reader, JAWS. Our results show that the use of context can potentially save browsing time and substantially improve browsing experience of visually disabled people.”
“With the growing use of dynamic web content generated from relational databases, traditional caching solutions for through put and latency improvements are ineffective. We describe a middleware layer called Ganesh that reduces the volume of data transmitted without semantic interpretation of queries or results. It achieves this reduction through the use of cryptographic hashing to detect similarities with previous results. These benefits do not require any compromise of the strict consistency semantics provided by the back-end database. Further, Ganesh does not require modifications to applications, web servers, or database servers, and works with closed-source applications and databases. Using two bench marks representative of dynamic web sites, measurements of our prototype show that it can increase end-to-end throughput by as much as two fold for non-data intensive applications and by as much as ten fold for data intensive ones.”
“Continuous queries are used to monitor changes to time varying data and to provide results useful for online decision making. Typically a user desires to obtain the value of some aggregation function over distributed data items, for example, to know (a) the average of temperatures sensed by a set of sensors (b) the value of index of mid-cap stocks. In these queries a client specifies a coherency requirement as part of the query. In this paper we present a low-cost, scalable technique to answer continuous aggregation queries using a content distribution network of dynamic data items. In such a network of data aggregators, each data aggregator serves a set of data items at specific coherencies. Just as various fragments of a dynamic web-page are served by one or more nodes of a content distribution network, our technique involves decomposing a client query into sub-queries and executing sub-queries on judiciously chosen data aggregators with their individual sub-query incoherency bounds. We provide a technique of getting the optimal query plan (i.e., set of sub-queries and their chosen data aggregators) which satisfies client query's coherency requirement with least cost, measured in terms of the number of refresh messages sent from aggregators to the client. For estimating query execution cost, we build a continuous query cost model which can be used to estimate the number of messages required to satisfy the client specified incoherency bound. Performance results using real-world traces show that our cost based query planning leads to queries being executed using less than one third the number of messages required by existing schemes.”
“Given a set of machines and a set of Web applications with dynamically changing demands, an online application placement controller decides how many instances to run for each application and where to put them, while observing all kinds of resource constraints. This NP hard problem has real usage in commercial middleware products. Existing approximation algorithms for this problem can scale to at most a few hundred machines, and may produce placement solutions that are far from optimal when system resources are tight. In this paper, we propose a new algorithm that can produce within 30seconds high-quality solutions for hard placement problems with thousands of machines and thousands of applications. This scalability is crucial for dynamic resource provisioning in large-scale enterprise data centers. Our algorithm allows multiple applications to share a single machine, and strives to maximize the total satisfied application demand, to minimize the number of application starts and stops, and to balance the load across machines. Compared with existing state-of-the-art algorithms, for systems with 100 machines or less, our algorithm is up to 134 times faster, reduces application starts and stops”
“Data-driven web applications are usually structured in three tiers with different programming models at each tier. This division forces developers to manually partition application functionality across the tiers, resulting in complex logic, suboptimal partitioning, and expensive re-partitioning of applications. In this paper, we introduce a unified platform for automatic partitioning of data-driven web applications. Our approach is based on Hilda[41, 46], a high-level declarative programming language with a unified data and programming model for all the layers of the application. Based on run-time properties of the application, Hilda's run time system automatically partitions the application between the tiers to improve response time while adhering to memory and/or processing constraints at the clients. We evaluate our methodology with traces from a real application and with TPC-W, and our results show that automatic partitioning outperforms manual partitioning without the associated development overhead.”
“This paper presents the architecture and the preliminary evaluation of a request routing DNS server that decouples server selection from the rest of DNS functionality. Our DNS server, which we refer to as MyXDNS, exposes well-defined APIs for uploading an externally computed server selection policy and for interacting with an external network proximity service. With MyXDNS, researchers can explore their own network proximity metrics and request routing algorithms without having to worry about DNS internals. Furthermore, MyXDNS is based onopen-source MyDNS and is available to public. Stress-testing of MyXDNS indicated that it achieves its flexibility at an acceptable cost: a single MyXDNS running on a low-level server can process 3000 req/sec with sub-millisecond response even in the presence of continuous updates to server selection policy.”
“The demand of browsing information from general Web pages using a mobile phone is increasing. However, since the majority of Web pages on the Internet are optimized for browsing from PCs, it is difficult for mobile phone users to obtain sufficient information from the Web. Therefore, a method to reconstruct PC-optimized Web pages for mobile phone users is essential. An example approach is to segment the Web page based on its structure, and utilize the hierarchy of the content element to regenerate a page suitable for mobile phone browsing. In our previous work, we have examined a robust automatic Web page segmentation scheme which uses the distance between content elements based on the relative HTML tag hierarchy, i.e., the number and depth of HTML tags in Web pages. However, this scheme has a problem that the content-distance based on the order of HTML tags does not always correspond to the intuitional distance between content elements on the actual layout of a Web page. In this paper, we propose a hybrid segmentation method which segments Web pages based on both the content-distance calculated by the previous scheme, and a novel approach which utilizes Web page layout information. Experiments conducted to evaluate the accuracy of Web page segmentation results prove that the proposed method can segment Web pages more accurately than conventional methods. Furthermore, implementation and evaluation of our system on the mobile phone prove that our method can realize superior usability compared to commercial Web browsers.”
“Nowadays, mobile users with global positioning devices can access Location Based Services (LBS) and query about points of interest in their proximity. For such applications to succeed, privacy and confidentiality are essential. Encryption alone is not adequate; although it safeguards the system against eavesdroppers, the queries themselves may disclose the location and identity of the user. Recently, there have been proposed centralized architectures based on K-anonymity, which utilize an intermediate anonymizer between the mobile users and the LBS. However, the anonymizer must be updated continuously with the current locations of all users. Moreover, the complete knowledge of the entire system poses a security threat, if the anonymizer is compromised. In this paper we address two issues: (i) We show that existing approaches may fail to provide spatial anonymity for some distributions of user locations and describe a novel technique which solves this problem. (ii) We propose Prive, a decentralized architecture for preserving the anonymity of users issuing spatial queries to LBS. Mobile users self-organizein to an overlay network with good fault tolerance and load balancing properties. Prive avoids the bottleneck caused by centralized techniques both in terms of anonymization and location updates. Moreover, the system state is distributed in numerous users, rendering Prive resilient to attacks. Extensive experimental studies suggest that Prive is applicable to real-life scenarios with large populations of mobile users.”
“In this paper we present an application framework that leverages geospatial content on the World Wide Web by enabling innovative modes of interaction and novel types of user interfaces on advanced mobile phones and PDAs. We discuss the current development steps involved in building mobile geospatial Web applications and derive three technological pre-requisites for our framework: spatial query operations based on visibility and field of view, a 2.5D environment model, and a presentation-independent data exchange format for geospatial query results. We propose the Local Visibility Model as a suitable XML-based candidate and present a prototype implementation.”
“Users searching for information in hypermedia environments often perform querying followed by manual navigation. Yet, the conventional text/hypertext retrieval paradigm does not explicity take post-query navigation into account. This paper proposes a new retrieval paradigm, called navigation-aided retrieval (NAR), which treats both querying and navigation as first-class activities. In the NAR paradigm, querying is seen as a means to identify starting points for navigation, and navigation is guided based on information supplied in the query. NAR is a generalization of the conventional probabilistic information retrieval paradigm, which implicitly assumes no navigation takes place. This paper presents a formal model for navigation-aided retrieval, and reports empirical results that point to the real-world applicability of the model. The experiments were performed over a large Web corpus provided by TREC, using human judgments on a new rating scale developed for navigation-aided retrieval. In the case of ambiguous queries, the new retrieval model identifies good starting points for post-query navigation. For less ambiguous queries that need not be paired with navigation, the output closely matches that of a conventional retrieval system.”
“We address the problem of measuring global quality metrics of search engines, like corpus size, index freshness, and density of duplicates in the corpus. The recently proposed estimators for such metrics [2, 6] suffer from significant bias and/or poor performance, due to inaccurate approximation of the so called "document degrees". We present two new estimators that are able to overcome the bias introduced by approximate degrees. Our estimators are based on a careful implementation of an approximate importance sampling procedure. Comprehensive theoretical and empirical analysis of the estimators demonstrates that they have essentially no bias even in situations where document degrees are poorly approximated. Building on an idea from [6], we discuss Rao Blackwellization as a generic method for reducing variance in search engine estimators. We show that Rao-Blackwellizing our estimators results in significant performance improvements, while not compromising accuracy.”
“The Web is rapidly moving towards a platform for mass collaboration in content production and consumption. Fresh content on a variety of topics, people, and places is being created and made available on the Web at breathtaking speed. Navigating the content effectively not only requires techniques such as aggregating various RSS-enabled feeds, but it also demands a new browsing paradigm. In this paper, we present novel geospatial and temporal browsing techniques that provide users with the capability of aggregating and navigating RSS-enabled content in a timely, personalized and automatic manner. In particular, we describe a system called GeoTracker that utilizes both a geospatial representation and a temporal (chronological) presentation to help users spot the most relevant updates quickly. Within the context of this work, we provide a middleware engine that supports intelligent aggregation and dissemination of RSS feeds with personalization to desktops and mobile devices. We study the navigation capabilities of this system on two kinds of data sets, namely, 2006 World Cup soccer data collected over two months and breaking news items that occur every day. We also demonstrate that the application of such technologies to the video search results returned by YouTube and Google greatly enhances a user's ability in locating and browsing videos based on his or her geographical interests. Finally, we demonstrate that the location inference performance of GeoTracker compares well against machine learning techniques used in the natural language processing/information retrieval community. Despite its algorithm simplicity, it preserves high recall percentages.”
“Current web search engines focus on searching only the most recent snapshot of the web. In some cases, however, it would be desirable to search over collections that include many different crawls and versions of each page. One important example of such a collection is the Internet Archive, though there are many others. Since the data size of such an archive is multiple times that of a single snapshot, this presents us with significant performance challenges. Current engines use various techniques for index compression and optimized query execution, but these techniques do not exploit the significant similarities between different versions of a page, or between different pages. In this paper, we propose a general framework for indexing and query processing of archival collections and, more generally, any collections with a sufficient amount of redundancy. Our approach results in significant reductions in index size and query processing costs on such collections, and it is orthogonal to and can be combined with the existing techniques. It also supports highly efficient updates, both locally and over a network. Within this framework, we describe and evaluate different implementations that trade off index size versus CPU cost and other factors, and discuss applications ranging from archival web search to local search of web sites, email archives, or file systems. We present experimental results based on search engine query log and a large collection consisting of multiple crawls.”
“Previous studies have highlighted the high arrival rate of new content on the web. We study the extent to which this new content can be efficiently discovered by a crawler. Our study has two parts. First, we study the inherent difficulty of the discovery problem using a maximum cover formulation, under an assumption of perfect estimates of likely sources of links to new content. Second, we relax this assumption and study a more realistic setting in which algorithms must use historical statistics to estimate which pages are most likely to yield links to new content. We recommend a simple algorithm that performs comparably to all approaches we consider. We measure the overhead of discovering new content, defined as the average number of fetches required to discover one new page. We show first that with perfect foreknowledge of where to explore for links to new content, it is possible to discover 90% of all new content with under 3% overhead, and 100% of new content with 9% overhead. But actual algorithms, which do not have access to perfect foreknowledge, face a more difficult task: one quarter of new content is simply not amenable to efficient discovery. Of the remaining three quarters, 80% of new content during a given week may be discovered with 160% overhead if content is recrawled fully on a monthly basis.”
“We address the problem of identifying the domain of online databases. More precisely, given a set F of Web forms automatically gathered by a focused crawler and an online database domain D, our goal is to select from F only the forms that are entry points to databases in D. Having a set of Webforms that serve as entry points to similar online databases is a requirement for many applications and techniques that aim to extract and integrate hidden-Web information, such as meta-searchers, online database directories, hidden-Web crawlers, and form-schema matching and merging. We propose a new strategy that automatically and accurately classifies online databases based on features that can be easily extracted from Web forms. By judiciously partitioning the space of form features, this strategy allows the use of simpler classifiers that can be constructed using learning techniques that are better suited for the features of each partition. Experiments using real Web data in a representative set of domains show that the use of different classifiers leads to high accuracy, precision and recall. This indicates that our modular classifier composition provides an effective and scalable solution for classifying online databases.”
“In this paper we describe new adaptive crawling strategies to efficiently locate the entry points to hidden-Web sources. The fact that hidden-Web sources are very sparsely distributed makes the problem of locating them especially challenging. We deal with this problem by using the contents of pages to focus the crawl on a topic; by prioritizing promising links within the topic; and by also following links that may not lead to immediate benefit. We propose a new framework whereby crawlers automatically learn patterns of promising links and adapt their focus as the crawl progresses, thus greatly reducing the amount of required manual setup and tuning. Our experiments over real Web pages in a representative set of domains indicate that online learning leads to significant gains in harvest rates -- the adaptive crawlers retrieve up to three times as many forms as crawlers that use a fixed focus strategy.”
“This paper proposes a random Web crawl model. A Web crawl is a (biased and partial) image of the Web. This paper deals with the hyperlink structure, i.e. a Web crawl is a graph, whose vertices are the pages and whose edges are the hypertextual links. Of course a Web crawl has a very special structure; we recall some known results about it. We then propose a model generating similar structures. Our model simply simulates a crawling, i.e. builds and crawls the graph at the same time. The graphs generated have lot of known properties of Web crawls. Our model is simpler than most random Web graph models, but captures the sames properties. Notice that it models the crawling process instead of the page writing process of Web graph models.”
“The World Wide Web (WWW) is rapidly becoming important for society as a medium for sharing data, information and services, and there is a growing interest in tools for understanding collective behaviors and emerging phenomena in the WWW. In this paper we focus on the problem of searching and classifying communities in the web. Loosely speaking a community is a group of pages related to a common interest. More formally communities have been associated in the computer science literature with the existence of a locally dense sub-graph of the web-graph (where web pages are nodes and hyper-links are arcs of the web-graph). The core of our contribution is a new scalable algorithm for finding relatively dense subgraphs in massive graphs. We apply our algorithm on web-graphs built on three publicly available large crawls of the web (with raw sizes up to 120M nodes and 1G arcs). The effectiveness of our algorithm in finding dense subgraphs is demonstrated experimentally by embedding artificial communities in the web-graph and counting how many of these are blindly found. Effectiveness increases with the size and density of the communities: it is close to 100% for communities of a thirty nodes or more (even at low density). It is still about 80% even for communities of twenty nodes with density over 50% of the arcs present. At the lower extremes the algorithm catches 35% of dense communities made of ten nodes. We complete our Community Watch system by clustering the communities found in the web-graph into homogeneous groups by topic and labelling each group by representative keywords.”
“Graphical relationships among Web pages have been exploited in methods for ranking search results. To date, specific graphical properties have been used in these analyses. We introduce a Web Projection methodology that generalizes prior efforts of graphical relationships of the web in several ways. With the approach, we create subgraphs by projecting sets of pages and domains onto the larger web graph, and then use machine learning to construct predictive models that consider graphical properties as evidence. We describe the method and then present experiments that illustrate the construction of predictive models of search result quality and user query reformulation.”
