OpenRaiser/Intern-Atlas huggingface dataset:
Random web crawls
Authors: Toufik Bennouas, Fabien de Montgolfier
Venue: WWW
Year: 2007

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.
Comparing apples and oranges: normalized pagerank for evolving graphs
Authors: Klaus Berberich, Srikanta J. Bedathur, Gerhard Weikum, Michalis Vazirgiannis
Venue: WWW
Year: 2007

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.
A clustering method for web data with multi-type interrelated components
Authors: Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee Giles
Venue: WWW
Year: 2007

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.
The largest scholarly semantic network...ever
Authors: Johan Bollen, Marko A. Rodriguez, Herbert Van de Sompel, Lyudmila Balakireva, Aric A. Hagberg
Venue: WWW
Year: 2007

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.
Dynamics of bid optimization in online advertisement auctions
Authors: Christian Borgs, Jennifer T. Chayes, Nicole Immorlica, Kamal Jain, Omid Etesami, Mohammad Mahdian
Venue: WWW
Year: 2007

We consider the problem of online keyword advertising auctions among multiple bidders with limited budgets, and study a natural bidding heuristic in which advertisers attempt to optimize their utility by equalizing their return-on-investment across all keywords. We show that existing auction mechanisms combined with this heuristic can experience cycling (as has been observed in many current systems), and therefore propose a modified class of mechanisms with small random perturbations. This perturbation is reminiscent of the small time-dependent perturbations employed in the dynamical systems literature to convert many types of chaos into attracting motions. We show that the perturbed mechanism provably converges in the case of first-price auctions and experimentally converges in the case of second-price auctions. Moreover, the point of convergence has a natural economic interpretation as the unique market equilibrium in the case of first-price mechanisms. In the case of second-price auctions, we conjecture that it converges to the "supply-aware" market equilibrium. Thus, our results can be alternatively described as a tâtonnement process for convergence to market equilibriumin which prices are adjusted on the side of the buyers rather than the sellers. We also observe that perturbation in mechanism design is useful in a broader context: In general, it can allow bidders to "share" a particular item, leading to stable allocations and pricing for the bidders, and improved revenue for the auctioneer.
Exposing private information by timing web applications
Authors: Andrew Bortz, Dan Boneh
Venue: WWW
Year: 2007

We show that the time web sites take to respond to HTTP requests can leak private information, using two different types of attacks. The first, direct timing, directly measures response times from a web site to expose private information such as validity of an username at a secured site or the number of private photos in a publicly viewable gallery. The second, cross-site timing, enables a malicious web site to obtain information from the user's perspective at another site. For example, a malicious site can learn if the user is currently logged in at a victim site and, in some cases, the number of objects in the user's shopping cart. Our experiments suggest that these timing vulnerabilities are wide-spread. We explain in detail how and why these attacks work, and discuss methods for writing web application code that resists these attacks.
Explorations in the use of semantic web technologies for product information management
Authors: Jean-Sébastien Brunner, Li Ma, Chen Wang, Lei Zhang, Daniel C. Wolfson, Yue Pan, Kavitha Srinivas
Venue: WWW
Year: 2007

Master data refers to core business entities a company uses repeatedly across many business processes and systems (such as lists or hierarchies of customers, suppliers, accounts, products, or organizational units). Product information is the most important kind of master data and product information management (PIM) is becoming critical for modern enterprises because it provides a rich business context for various applications. Existing PIM systems are less flexible and scalable for on-demand business, as well as too weak to completely capture and use the semantics of master data. This paper explores how to use semantic web technologies to enhance a collaborative PIM system by simplifying modeling and representation while preserving enough dynamic flexibility. Furthermore, we build a semantic PIM system using one of the state-of-art ontology repositories and summarize the challenges we encountered based on our experimental results, especially on performance and scalability. We believe that our study and experiences are valuable for both semantic web community and master data management community.
Sliding window technique for the web log analysis
Authors: Nikolai Buzikashvili
Venue: WWW
Year: 2007

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.
Bayesian network based sentence retrieval model
Authors: Keke Cai, Jiajun Bu, Chun Chen, Kangmiao Liu, Wei Chen
Venue: WWW
Year: 2007

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.
Exploration of query context for information retrieval
Authors: Keke Cai, Chun Chen, Jiajun Bu, Peng Huang, Zhiming Kang
Venue: WWW
Year: 2007

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.
Page-level template detection via isotonic smoothing
Authors: Deepayan Chakrabarti, Ravi Kumar, Kunal Punera
Venue: WWW
Year: 2007

We develop a novel framework for the page-level template detection problem. Our framework is built on two main ideas. The first is theautomatic generation of training data for a classifier that, given apage, assigns a templateness score to every DOM node of the page. The second is the global smoothing of these per-node classifier scores bysolving a regularized isotonic regression problem; the latter follows from a simple yet powerful abstraction of templateness on a page. Our extensive experiments on human-labeled test data show that our approachdetects templates effectively.
Parallel crawling for online social networks
Authors: Duen Horng Chau, Shashank Pandit, Samuel Wang, Christos Faloutsos
Venue: WWW
Year: 2007

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.
GigaHash: scalable minimal perfect hashing for billions of urls
Authors: Kumar Chellapilla, Anton Mityagin, Denis Xavier Charles
Venue: WWW
Year: 2007

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.
Mirror site maintenance based on evolution associations of web directories
Authors: Ling Chen, Sourav S. Bhowmick, Wolfgang Nejdl
Venue: WWW
Year: 2007

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 directorystructures 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 skipsubdirectories 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.
Mining contiguous sequential patterns from web logs
Authors: Jinlin Chen, Terry Cook
Venue: WWW
Year: 2007

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.
Using d-gap patterns for index compression
Authors: Jinlin Chen, Terry Cook
Venue: WWW
Year: 2007

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.
GeoTV: navigating geocoded rss to create an iptv experience
Authors: Yih-Farn Chen, Giuseppe Di Fabbrizio, David C. Gibbon, Rittwik Jana, Serban Jora, Bernard Renger, Bin Wei
Venue: WWW
Year: 2007

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.
Life is sharable: mechanisms to support and sustain blogging life experience
Authors: Yun-Maw Cheng, Tzu-Chuan Chou, Wai Yu, Li-Chieh Chen, Ching-Long Yeh, Meng Chang Chen
Venue: WWW
Year: 2007

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.
A new suffix tree similarity measure for document clustering
Authors: Hung Chim, Xiaotie Deng
Venue: WWW
Year: 2007

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 the Web documents in a large forum community.
On ranking techniques for desktop search
Authors: Sara Cohen, Carmel Domshlak, Naama Zwerdling
Venue: WWW
Year: 2007

This paper addresses the desktop search problem by considering varioustechniques for ranking results of a search query over thefile system. First, basic ranking techniques, which are based ona 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 isalso shown to be empirically effective, even though it does notinvolve any learning.
Spam and popularity ratings for combating link spam
Authors: Mukesh Dalal
Venue: WWW
Year: 2007

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.
Personalized social & real-time collaborative search
Authors: Mukesh Dalal
Venue: WWW
Year: 2007

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.
The discoverability of the web
Authors: Anirban Dasgupta, Arpita Ghosh, Ravi Kumar, Christopher Olston, Sandeep Pandey, Andrew Tomkins
Venue: WWW
Year: 2007

Previous studies have highlighted the high arrival rate of new contenton the web. We study the extent to which this new content can beefficiently discovered by a crawler. Our study has two parts. First,we study the inherent difficulty of the discovery problem using amaximum cover formulation, under an assumption of perfect estimates oflikely sources of links to new content. Second, we relax thisassumption and study a more realistic setting in which algorithms mustuse historical statistics to estimate which pages are most likely toyield links to new content. We recommend a simple algorithm thatperforms comparably to all approaches we consider.We measure the emphoverhead of discovering new content, defined asthe average number of fetches required to discover one new page. Weshow first that with perfect foreknowledge of where to explore forlinks to new content, it is possible to discover 90% of all newcontent with under 3% overhead, and 100% of new content with 9%overhead. But actual algorithms, which do not have access to perfectforeknowledge, face a more difficult task: one quarter of new contentis simply not amenable to efficient discovery. Of the remaining threequarters, 80% of new content during a given week may be discoveredwith 160% overhead if content is recrawled fully on a monthly basis.
Web4CE: accessing web-based applications on consumer devices
Authors: Walter Dees, Paul Shrubsole
Venue: WWW
Year: 2007

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.