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Conference Paper: Reverse nearest neighbors search in ad-hoc subspaces

TitleReverse nearest neighbors search in ad-hoc subspaces
Authors
Issue Date2006
Citation
Proceedings - International Conference On Data Engineering, 2006, v. 2006, p. 76 How to Cite?
AbstractGiven an object q, modeled by a multidimensional point, a reverse nearest neighbors (RNN) query returns the set of objects in the database that have q as their nearest neighbor. In this paper, we study an interesting generalization of the RNN query, where not all dimensions are considered, but only an ad-hoc subset thereof. The rationale is that (i) the dimensionality might be too high for the result of a regular RNN query to be useful, (ii) missing values may implicitly define a meaningful subspace for RNN retrieval, and (iii) analysts may be interested in the query results only for a set of (ad-hoc) problem dimensions (i.e., object attributes). We consider a suitable storage scheme and develop appropriate algorithms for projected RNN queries, without relying on multidimensional indexes. Our methods are experimentally evaluated with real and synthetic data. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/93411
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorYiu, MLen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.date.accessioned2010-09-25T15:00:20Z-
dc.date.available2010-09-25T15:00:20Z-
dc.date.issued2006en_HK
dc.identifier.citationProceedings - International Conference On Data Engineering, 2006, v. 2006, p. 76en_HK
dc.identifier.issn1084-4627en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93411-
dc.description.abstractGiven an object q, modeled by a multidimensional point, a reverse nearest neighbors (RNN) query returns the set of objects in the database that have q as their nearest neighbor. In this paper, we study an interesting generalization of the RNN query, where not all dimensions are considered, but only an ad-hoc subset thereof. The rationale is that (i) the dimensionality might be too high for the result of a regular RNN query to be useful, (ii) missing values may implicitly define a meaningful subspace for RNN retrieval, and (iii) analysts may be interested in the query results only for a set of (ad-hoc) problem dimensions (i.e., object attributes). We consider a suitable storage scheme and develop appropriate algorithms for projected RNN queries, without relying on multidimensional indexes. Our methods are experimentally evaluated with real and synthetic data. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.relation.ispartofProceedings - International Conference on Data Engineeringen_HK
dc.titleReverse nearest neighbors search in ad-hoc subspacesen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICDE.2006.129en_HK
dc.identifier.scopuseid_2-s2.0-33749620348en_HK
dc.identifier.hkuros122082en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33749620348&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume2006en_HK
dc.identifier.spage76en_HK
dc.identifier.epage76en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridYiu, ML=8589889600en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK

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