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Article: View selection using randomized search
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TitleView selection using randomized search
 
AuthorsKalnis, P2
Mamoulis, N1
Papadias, D2
 
KeywordsData warehouse
On-line analytical processing
View selection
 
Issue Date2002
 
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/datak
 
CitationData And Knowledge Engineering, 2002, v. 42 n. 1, p. 89-111 [How to Cite?]
DOI: http://dx.doi.org/10.1016/S0169-023X(02)00045-9
 
AbstractAn important issue in data warehouse development is the selection of a set of views to materialize in order to accelerate On-line analytical processing queries, given certain space and maintenance time constraints. Existing methods provide good results but their high execution cost limits their applicability for large problems. In this paper, we explore the application of randomized, local search algorithms to the view selection problem. The efficiency of the proposed techniques is evaluated using synthetic datasets, which cover a wide range of data and query distributions. The results show that randomized search methods provide near-optimal solutions in limited time, being robust to data and query skew. Furthermore, they can be easily adapted for various versions of the problem, including the simultaneous existence of size and time constraints, and view selection in dynamic environments. The proposed heuristics scale well with the problem size, and are therefore particularly useful for real life warehouses, which need to be analyzed by numerous business perspectives. © 2002 Elsevier Science B.V. All rights reserved.
 
ISSN0169-023X
2013 Impact Factor: 1.489
2013 SCImago Journal Rankings: 1.327
 
DOIhttp://dx.doi.org/10.1016/S0169-023X(02)00045-9
 
ISI Accession Number IDWOS:000176141000004
 
ReferencesReferences in Scopus
 
DC FieldValue
dc.contributor.authorKalnis, P
 
dc.contributor.authorMamoulis, N
 
dc.contributor.authorPapadias, D
 
dc.date.accessioned2010-09-06T09:50:35Z
 
dc.date.available2010-09-06T09:50:35Z
 
dc.date.issued2002
 
dc.description.abstractAn important issue in data warehouse development is the selection of a set of views to materialize in order to accelerate On-line analytical processing queries, given certain space and maintenance time constraints. Existing methods provide good results but their high execution cost limits their applicability for large problems. In this paper, we explore the application of randomized, local search algorithms to the view selection problem. The efficiency of the proposed techniques is evaluated using synthetic datasets, which cover a wide range of data and query distributions. The results show that randomized search methods provide near-optimal solutions in limited time, being robust to data and query skew. Furthermore, they can be easily adapted for various versions of the problem, including the simultaneous existence of size and time constraints, and view selection in dynamic environments. The proposed heuristics scale well with the problem size, and are therefore particularly useful for real life warehouses, which need to be analyzed by numerous business perspectives. © 2002 Elsevier Science B.V. All rights reserved.
 
dc.description.naturelink_to_subscribed_fulltext
 
dc.identifier.citationData And Knowledge Engineering, 2002, v. 42 n. 1, p. 89-111 [How to Cite?]
DOI: http://dx.doi.org/10.1016/S0169-023X(02)00045-9
 
dc.identifier.citeulike1082617
 
dc.identifier.doihttp://dx.doi.org/10.1016/S0169-023X(02)00045-9
 
dc.identifier.epage111
 
dc.identifier.hkuros74874
 
dc.identifier.isiWOS:000176141000004
 
dc.identifier.issn0169-023X
2013 Impact Factor: 1.489
2013 SCImago Journal Rankings: 1.327
 
dc.identifier.issue1
 
dc.identifier.openurl
 
dc.identifier.scopuseid_2-s2.0-0036642672
 
dc.identifier.spage89
 
dc.identifier.urihttp://hdl.handle.net/10722/88956
 
dc.identifier.volume42
 
dc.languageeng
 
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/datak
 
dc.publisher.placeNetherlands
 
dc.relation.ispartofData and Knowledge Engineering
 
dc.relation.referencesReferences in Scopus
 
dc.rightsData & Knowledge Engineering. Copyright © Elsevier BV.
 
dc.subjectData warehouse
 
dc.subjectOn-line analytical processing
 
dc.subjectView selection
 
dc.titleView selection using randomized search
 
dc.typeArticle
 
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Author Affiliations
  1. The University of Hong Kong
  2. Hong Kong University of Science and Technology