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dc.contributor.authorJagadish, HV
dc.contributor.authorOoi, BC
dc.contributor.authorTan, KL
dc.contributor.authorYu, C
dc.contributor.authorZhang, R
dc.date.available2014-05-22T00:17:53Z
dc.date.issued2005-06-01
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000230623300002&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c
dc.identifier.citationJagadish, H. V., Ooi, B. C., Tan, K. L., Yu, C. & Zhang, R. (2005). iDistance: An adaptive B+-tree based indexing method for nearest neighbor search. ACM TRANSACTIONS ON DATABASE SYSTEMS, 30 (2), pp.364-397. https://doi.org/10.1145/1071610.1071612.
dc.identifier.issn0362-5915
dc.identifier.urihttp://hdl.handle.net/11343/30084
dc.description.abstract<jats:p> In this article, we present an efficient B <jats:sup>+</jats:sup> -tree based indexing method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional metric space. iDistance partitions the data based on a space- or data-partitioning strategy, and selects a reference point for each partition. The data points in each partition are transformed into a single dimensional value based on their similarity with respect to the reference point. This allows the points to be indexed using a B <jats:sup>+</jats:sup> -tree structure and KNN search to be performed using one-dimensional range search. The choice of partition and reference points adapts the index structure to the data distribution.We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its effectiveness. We also present a cost model for iDistance KNN search, which can be exploited in query optimization. </jats:p>
dc.languageEnglish
dc.publisherASSOC COMPUTING MACHINERY
dc.subjectInformation Systems
dc.titleiDistance: An adaptive B+-tree based indexing method for nearest neighbor search
dc.typeJournal Article
dc.identifier.doi10.1145/1071610.1071612
melbourne.peerreviewPeer Reviewed
melbourne.affiliationThe University of Melbourne
melbourne.affiliation.departmentComputer Science and Software Engineering
melbourne.source.titleACM Transactions on Database Systems
melbourne.source.volume30
melbourne.source.issue2
melbourne.source.pages364-397
dc.description.pagestart364
melbourne.publicationid80104
melbourne.elementsid290216
melbourne.contributor.authorZhang, Rui
melbourne.internal.ingestnoteAbstract bulk upload (2017-07-24)
dc.identifier.eissn1557-4644
melbourne.accessrightsThis item is currently not available from this repository


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