Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/39796
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dc.titleDynamic clustering-based query answering in peer-to-peer systems
dc.contributor.authorQian, W.
dc.contributor.authorZhou, S.
dc.contributor.authorRen, Y.
dc.contributor.authorZhou, A.
dc.contributor.authorOoi, B.C.
dc.contributor.authorTan, K.-L.
dc.date.accessioned2013-07-04T07:49:46Z
dc.date.available2013-07-04T07:49:46Z
dc.date.issued2003
dc.identifier.citationQian, W.,Zhou, S.,Ren, Y.,Zhou, A.,Ooi, B.C.,Tan, K.-L. (2003). Dynamic clustering-based query answering in peer-to-peer systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2762 : 306-313. ScholarBank@NUS Repository.
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39796
dc.description.abstractIn this paper, we propose a new query answering model for P2P applications, which is termed as clustering-based query answering (CBQA). CBQA will retrieve the data objects that are in the same cluster of the query from the global dataset distributed over peers of a P2P system. We first present a framework that support clustering based query answering. Then we give three concrete algorithms for different clustering criteria, namely k-nearest-neighbor, distance-based, and density-based clustering, along with detailed analyses. Finally, implementation issues, especially dynamic neighbors selection to enable the scalability are addressed. Theoretical analysis shows that our method can guarantee to find desirable objects in the interested cluster with modest overhead. © Springer-Verlag Berlin Heidelberg 2003.
dc.sourceScopus
dc.typeArticle
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.volume2762
dc.description.page306-313
dc.identifier.isiutNOT_IN_WOS
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