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- item-1 hasContent hongwen-kang.
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- item-2 hasContent kuansan-wang.
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- 213 creator james-hendler.
- 213 creator medha-atre.
- 213 creator mohammed-zaki.
- 213 creator vineet-chaoji.
- 213 type InProceedings.
- 213 label "Matrix "Bit"loaded: A scalable lightweight join query processor for RDF data".
- 213 sameAs 213.
- 213 abstract "The Semantic Web community, until now, has used traditional database systems for the storage and querying of RDF data. The SPARQL query language also closely follows SQL syntax. As a natural consequence, most of the SPARQL query processing techniques are based on database query processing and optimization techniques. For SPARQL join query optimization, previous works like RDF-3X and Hexastore have proposed to use 6-way indexes on the RDF data. Although these indexes speed up merge-joins by orders of magnitude, for complex join queries generating large intermediate join results, the scalability of the query processor still remains a challenge. In this paper, we introduce (i) BitMat -- a compressed bit-matrix structure for storing huge RDF graphs, and (ii) a novel, light-weight SPARQL join query processing method that employs an initial pruning technique, followed by a variable-binding-matching algorithm on BitMats to produce the final results. Our query processing method does not build intermediate join tables and works directly on the compressed data. We have demonstrated our method against RDF graphs of upto 1.33 billion triples -- the largest among results published until now (single-node, non-parallel systems), and have compared our method with the state-of-the-art RDF stores -- RDF-3X and MonetDB. Our results show that the competing methods are most effective with highly selective queries. On the other hand, BitMat can deliver 2-3 orders of magnitude better performance on complex, low-selectivity queries over massive data.".
- 213 hasAuthorList authorList.
- 213 isPartOf proceedings.
- 213 keyword "Querying".
- 213 keyword "distributed approaches".
- 213 keyword "including combinations with statistics".
- 213 keyword "searching the Semantic/Data Web".
- 213 keyword "soft computing".
- 213 title "Matrix "Bit"loaded: A scalable lightweight join query processor for RDF data".
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- item-1 hasContent medha-atre.
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- item-2 hasContent vineet-chaoji.
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- 221 creator deepayan-chakrabarti.
- 221 creator rupesh-mehta.
- 221 type InProceedings.
- 221 label "The Paths More Taken: Matching DOM Trees to Search Logs for Accurate Webpage Clustering".
- 221 sameAs 221.
- 221 abstract "An unsupervised clustering of the webpages on a website is a primary requirement for most wrapper induction and automated data extraction methods. Since page content can vary drastically across pages of one cluster (e.g., all product pages on \url{amazon.com}), traditional clustering methods typically use some distance function between the DOM trees representing a pair of webpages. However, without knowing which portions of the DOM tree are ``important,'' such distance functions might discriminate between similar pages based on trivial features (e.g., differing number of reviews on two product pages), or club together distinct types of pages based on superficial features present in the DOM trees of both (e.g., matching footer/copyright), leading to poor clustering performance. We propose using search logs to automatically find paths in the DOM trees that mark out important portions of pages, e.g., the product title in a product page. Such paths are identified via a {\em global} analysis of the entire website, whereby search data for popular pages can be used to infer good paths even for other pages that receive little or no search traffic. The webpages on the website are then clustered using these ``key'' paths. Our algorithm only requires information on search queries, and the webpages clicked in response to them; there is no need for human input, and it does not need to be told which portion of a webpage the user found interesting. The resulting clusterings achieve an adjusted RAND score of over 0.9 on half of the websites (a score of 1 indicating a perfect clustering), and $59\%$ better scores on average than competing algorithms. Besides leading to refined clusterings, these key paths can be useful in the wrapper induction process itself, as shown by the high degree of match between the key paths and the manually identified paths used in existing wrappers for these sites ($90\%$ average precision).".
- 221 hasAuthorList authorList.
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- 221 keyword "Text".
- 221 keyword "classification".
- 221 keyword "metadata clustering".
- 221 keyword "web page".
- 221 title "The Paths More Taken: Matching DOM Trees to Search Logs for Accurate Webpage Clustering".
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- item-1 hasContent deepayan-chakrabarti.
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- 23 creator yingwu-zhu.
- 23 type InProceedings.
- 23 label "Measurement and Analysis of an Online Content Voting Network: A Case Study of Digg".
- 23 sameAs 23.
- 23 abstract "In online content voting networks, aggregate user activities (e.g., submitting and rating content) makes high-quality content thrive through the unprecedented scale, high dynamics and divergent quality of user generated content (UGC). To better understand the nature and impact of online content voting networks, we have analyzed Digg, a popular online social news aggregator and rating website. Based on a large amount of data collected, we provide an in-depth study of Digg. We study structural properties of Digg social network, revealing some strikingly distinct properties such as low link symmetry and the power-law distribution of node outdegree with truncated tails. We explore impact of the social network on user digging activities, and investigate the issues of content promotion, content filtering, vote spam and content censorship, which are inherent to content rating networks. We also provide insight into design of content promotion algorithms and recommendation-assisted content discovery. Overall, we believe that the results presented in this paper are crucial in understanding online content rating networks.".
- 23 hasAuthorList authorList.
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- 23 keyword "Social network analysis".
- 23 title "Measurement and Analysis of an Online Content Voting Network: A Case Study of Digg".
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- item-1 hasContent yingwu-zhu.
- 237 creator edward-sobiesk.
- 237 creator gregory-conti.
- 237 type InProceedings.
- 237 label "Malicious Interface Design: Exploiting the User".
- 237 sameAs 237.