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Conference Paper: All-nearest-neighbors queries in spatial databases
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TitleAll-nearest-neighbors queries in spatial databases
 
AuthorsZhang, J
Mamoulis, N
Papadias, D
Tao, Y
 
KeywordsComputers
Software metrology and standardization
 
Issue Date2004
 
PublisherIEEE, Computer Society.
 
CitationThe 16th International Conference on Scientific and Statistical Database Management Proceedings, Santorini Island, Greece, 21-23 June 2004, p. 297-306 [How to Cite?]
DOI: http://dx.doi.org/10.1109/SSDM.2004.1311221
 
AbstractGiven two sets A and B of multidimensional objects, the all-nearest-neighbors (ANN) query retrieves for each object in A its nearest neighbor in B. Although this operation is common in several applications, it has not received much attention in the database literature. In this paper we study alternative methods for processing ANN queries depending on whether A and B are indexed: Our algorithms are evaluated through extensive experimentation using synthetic and real datasets. The performance studies show that they are an order of magnitude faster than a previous approach based on closest-pairs query processing.
 
ISSN1551-6393
 
DOIhttp://dx.doi.org/10.1109/SSDM.2004.1311221
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorZhang, J
 
dc.contributor.authorMamoulis, N
 
dc.contributor.authorPapadias, D
 
dc.contributor.authorTao, Y
 
dc.date.accessioned2007-10-30T06:28:35Z
 
dc.date.available2007-10-30T06:28:35Z
 
dc.date.issued2004
 
dc.description.abstractGiven two sets A and B of multidimensional objects, the all-nearest-neighbors (ANN) query retrieves for each object in A its nearest neighbor in B. Although this operation is common in several applications, it has not received much attention in the database literature. In this paper we study alternative methods for processing ANN queries depending on whether A and B are indexed: Our algorithms are evaluated through extensive experimentation using synthetic and real datasets. The performance studies show that they are an order of magnitude faster than a previous approach based on closest-pairs query processing.
 
dc.description.naturepublished_or_final_version
 
dc.format.extent365889 bytes
 
dc.format.extent4295 bytes
 
dc.format.mimetypeapplication/pdf
 
dc.format.mimetypetext/plain
 
dc.identifier.citationThe 16th International Conference on Scientific and Statistical Database Management Proceedings, Santorini Island, Greece, 21-23 June 2004, p. 297-306 [How to Cite?]
DOI: http://dx.doi.org/10.1109/SSDM.2004.1311221
 
dc.identifier.doihttp://dx.doi.org/10.1109/SSDM.2004.1311221
 
dc.identifier.hkuros103375
 
dc.identifier.issn1551-6393
 
dc.identifier.openurl
 
dc.identifier.scopuseid_2-s2.0-5444228951
 
dc.identifier.urihttp://hdl.handle.net/10722/45531
 
dc.languageeng
 
dc.publisherIEEE, Computer Society.
 
dc.relation.referencesReferences in Scopus
 
dc.rights©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
 
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License
 
dc.subjectComputers
 
dc.subjectSoftware metrology and standardization
 
dc.titleAll-nearest-neighbors queries in spatial databases
 
dc.typeConference_Paper
 
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Author Affiliations
  1. The University of Hong Kong
  2. City University of Hong Kong
  3. Nanyang Technological University School of Computer Engineering
  4. Hong Kong University of Science and Technology