Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/132900
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dc.titleLINP: Supporting similarity search in unstructured peer-to-peer networks
dc.contributor.authorCui, B.
dc.contributor.authorQian, W.
dc.contributor.authorXu, L.
dc.contributor.authorZhou, A.
dc.date.accessioned2016-12-13T05:37:52Z
dc.date.available2016-12-13T05:37:52Z
dc.date.issued2007
dc.identifier.citationCui, B., Qian, W., Xu, L., Zhou, A. (2007). LINP: Supporting similarity search in unstructured peer-to-peer networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4505 LNCS : 127-135. ScholarBank@NUS Repository.
dc.identifier.isbn9783540724834
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/132900
dc.description.abstractAn important problem that confronts peer-to-peer (P2P) systems is efficient support for content-based search. In this paper, we look at how similarity query in high-dimensional spaces can be supported in unstructured P2P systems. We design an efficient index mechanism, named Linking Identical Neighborly Partitions (LINP), which takes advantage of both space partitioning and routing indices techniques. We evaluate our proposed scheme over various data sets, and experimental results show the efficacy of our approach. © Springer-Verlag Berlin Heidelberg 2007.
dc.sourceScopus
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.volume4505 LNCS
dc.description.page127-135
dc.identifier.isiutNOT_IN_WOS
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