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Conference Paper: Efficient aggregation of ranked inputs

TitleEfficient aggregation of ranked inputs
Authors
Issue Date2006
Citation
Proceedings - International Conference On Data Engineering, 2006, v. 2006, p. 72 How to Cite?
AbstractA top-k query combines different rankings of the same set of objects and returns the k objects with the highest combined score according to an aggregate function. We bring to light some key observations, which Impose two phases that any top-k algorithm, based on sorted accesses, should go through. Based on them, we propose a new algorithm, which Is designed to minimize the number of object accesses, the computational cost, and the memory requirements of top-k search. Adaptations of our algorithm for search variants (exact scores, on-line and Incremental search, top-k joins, other aggregate functions, etc.) are also provided. Extensive experiments with synthetic and real data show that, compared to previous techniques, our method accesses fewer objects, while being orders of magnitude faster. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/93188
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorMamoulis, Nen_HK
dc.contributor.authorCheng, KHen_HK
dc.contributor.authorYiu, MLen_HK
dc.contributor.authorCheung, DWen_HK
dc.date.accessioned2010-09-25T14:53:33Z-
dc.date.available2010-09-25T14:53:33Z-
dc.date.issued2006en_HK
dc.identifier.citationProceedings - International Conference On Data Engineering, 2006, v. 2006, p. 72en_HK
dc.identifier.issn1084-4627en_HK
dc.identifier.urihttp://hdl.handle.net/10722/93188-
dc.description.abstractA top-k query combines different rankings of the same set of objects and returns the k objects with the highest combined score according to an aggregate function. We bring to light some key observations, which Impose two phases that any top-k algorithm, based on sorted accesses, should go through. Based on them, we propose a new algorithm, which Is designed to minimize the number of object accesses, the computational cost, and the memory requirements of top-k search. Adaptations of our algorithm for search variants (exact scores, on-line and Incremental search, top-k joins, other aggregate functions, etc.) are also provided. Extensive experiments with synthetic and real data show that, compared to previous techniques, our method accesses fewer objects, while being orders of magnitude faster. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.relation.ispartofProceedings - International Conference on Data Engineeringen_HK
dc.titleEfficient aggregation of ranked inputsen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ICDE.2006.54en_HK
dc.identifier.scopuseid_2-s2.0-33749624867en_HK
dc.identifier.hkuros122084en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33749624867&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume2006en_HK
dc.identifier.spage72en_HK
dc.identifier.epage72en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.scopusauthoridCheng, KH=34467513500en_HK
dc.identifier.scopusauthoridYiu, ML=8589889600en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK

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