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Article: Ranking spatial data by quality preferences

TitleRanking spatial data by quality preferences
Authors
KeywordsQuery processing
spatial databases
Issue Date2011
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tkde
Citation
Ieee Transactions On Knowledge And Data Engineering, 2011, v. 23 n. 3, p. 433-446 How to Cite?
AbstractA spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/138038
ISSN
2015 Impact Factor: 2.476
2015 SCImago Journal Rankings: 2.087
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong RGCHKU 715509E
Funding Information:

This work was supported by grant HKU 715509E from Hong Kong RGC.

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorYiu, MLen_HK
dc.contributor.authorLu, Hen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.contributor.authorVaitis, Men_HK
dc.date.accessioned2011-08-26T14:39:03Z-
dc.date.available2011-08-26T14:39:03Z-
dc.date.issued2011en_HK
dc.identifier.citationIeee Transactions On Knowledge And Data Engineering, 2011, v. 23 n. 3, p. 433-446en_HK
dc.identifier.issn1041-4347en_HK
dc.identifier.urihttp://hdl.handle.net/10722/138038-
dc.description.abstractA spatial preference query ranks objects based on the qualities of features in their spatial neighborhood. For example, using a real estate agency database of flats for lease, a customer may want to rank the flats with respect to the appropriateness of their location, defined after aggregating the qualities of other features (e.g., restaurants, cafes, hospital, market, etc.) within their spatial neighborhood. Such a neighborhood concept can be specified by the user via different functions. It can be an explicit circular region within a given distance from the flat. Another intuitive definition is to assign higher weights to the features based on their proximity to the flat. In this paper, we formally define spatial preference queries and propose appropriate indexing techniques and search algorithms for them. Extensive evaluation of our methods on both real and synthetic data reveals that an optimized branch-and-bound solution is efficient and robust with respect to different parameters. © 2006 IEEE.en_HK
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tkdeen_HK
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineeringen_HK
dc.rightsIEEE Transactions on Knowledge & Data Engineering. Copyright © IEEE.-
dc.rights©2011 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.subjectQuery processingen_HK
dc.subjectspatial databasesen_HK
dc.titleRanking spatial data by quality preferencesen_HK
dc.typeArticleen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TKDE.2010.119en_HK
dc.identifier.scopuseid_2-s2.0-79251513599en_HK
dc.identifier.hkuros190926en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79251513599&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume23en_HK
dc.identifier.issue3en_HK
dc.identifier.spage433en_HK
dc.identifier.epage446en_HK
dc.identifier.isiWOS:000286207900009-
dc.publisher.placeUnited Statesen_HK
dc.relation.projectAlgorithms for Large-Scale Matching Problems-
dc.identifier.scopusauthoridYiu, ML=8589889600en_HK
dc.identifier.scopusauthoridLu, H=26642953300en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.scopusauthoridVaitis, M=16240387400en_HK

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