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dc.titleiDISQUE: Tuning high-dimensional similarity queries in DHT networks
dc.contributor.authorZhang, X.
dc.contributor.authorShou, L.
dc.contributor.authorTan, K.-L.
dc.contributor.authorChen, G.
dc.identifier.citationZhang, X.,Shou, L.,Tan, K.-L.,Chen, G. (2010). iDISQUE: Tuning high-dimensional similarity queries in DHT networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5981 LNCS (PART 1) : 19-33. ScholarBank@NUS Repository. <a href="" target="_blank"></a>
dc.description.abstractIn this paper, we propose a fully decentralized framework called iDISQUE to support tunable approximate similarity query of high dimensional data in DHT networks. The iDISQUE framework utilizes a distributed indexing scheme to organize data summary structures called iDisques, which describe the cluster information of the data on each peer. The publishing process of iDisques employs a locality-preserving mapping scheme. Approximate similarity queries can be resolved using the distributed index. The accuracy of query results can be tuned both with the publishing and query costs. We employ a multi-probe technique to reduce the index size without compromising the effectiveness of queries. We also propose an effective load-balancing technique based on multi-probing. Experiments on real and synthetic datasets confirm the effectiveness and efficiency of iDISQUE. © Springer-Verlag Berlin Heidelberg 2010.
dc.typeConference Paper
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5981 LNCS
dc.description.issuePART 1
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