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Article: Optimization in data cube system design

TitleOptimization in data cube system design
Authors
KeywordsApproximate algorithm
Data cube system design
Data warehouse
OLAP
Optimization
Issue Date2004
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0925-9902
Citation
Journal Of Intelligent Information Systems, 2004, v. 23 n. 1, p. 17-45 How to Cite?
AbstractThe design of an OLAP system for supporting real-time queries is one of the major research issues. One approach is to use data cubes, which are materialized precompiled multidimensional views of data in a data warehouse. We can derive a set of data cubes to answer each frequently asked query directly. However, there are two practical problems: (1) the maintenance cost of the data cubes, and (2) the query cost to answer those queries. Maintaining a data cube requires disk storage and CPU computation, so the maintenance cost is related to the total size as well as the total number of data cubes materialized. In most cases, materializing all data cubes is impractical. The maintenance cost may be reduced by merging some data cubes. However, the resulting larger data cubes will increase the query cost of answering some queries. If the bounds on the maintenance cost and the query cost are too strict, we help the user decide which queries to be sacrificed and not taken into consideration. We have defined an optimization problem in data cube system design. Given a maintenance-cost bound, a query-cost bound and a set of frequently asked queries, it is necessary to determine a set of data cubes such that the system can answer a largest subset of the queries without violating the two bounds. This is an NP-hard problem. We propose approximate Greedy algorithms GR, 2GM and 2GMM, which are shown to be both effective and efficient by experiments done on a census data set and a forest-cover-type data set.
Persistent Identifierhttp://hdl.handle.net/10722/88999
ISSN
2015 Impact Factor: 1.0
2015 SCImago Journal Rankings: 0.691
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHung, Een_HK
dc.contributor.authorCheung, DWen_HK
dc.contributor.authorKao, Ben_HK
dc.date.accessioned2010-09-06T09:51:07Z-
dc.date.available2010-09-06T09:51:07Z-
dc.date.issued2004en_HK
dc.identifier.citationJournal Of Intelligent Information Systems, 2004, v. 23 n. 1, p. 17-45en_HK
dc.identifier.issn0925-9902en_HK
dc.identifier.urihttp://hdl.handle.net/10722/88999-
dc.description.abstractThe design of an OLAP system for supporting real-time queries is one of the major research issues. One approach is to use data cubes, which are materialized precompiled multidimensional views of data in a data warehouse. We can derive a set of data cubes to answer each frequently asked query directly. However, there are two practical problems: (1) the maintenance cost of the data cubes, and (2) the query cost to answer those queries. Maintaining a data cube requires disk storage and CPU computation, so the maintenance cost is related to the total size as well as the total number of data cubes materialized. In most cases, materializing all data cubes is impractical. The maintenance cost may be reduced by merging some data cubes. However, the resulting larger data cubes will increase the query cost of answering some queries. If the bounds on the maintenance cost and the query cost are too strict, we help the user decide which queries to be sacrificed and not taken into consideration. We have defined an optimization problem in data cube system design. Given a maintenance-cost bound, a query-cost bound and a set of frequently asked queries, it is necessary to determine a set of data cubes such that the system can answer a largest subset of the queries without violating the two bounds. This is an NP-hard problem. We propose approximate Greedy algorithms GR, 2GM and 2GMM, which are shown to be both effective and efficient by experiments done on a census data set and a forest-cover-type data set.en_HK
dc.languageengen_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0925-9902en_HK
dc.relation.ispartofJournal of Intelligent Information Systemsen_HK
dc.subjectApproximate algorithmen_HK
dc.subjectData cube system designen_HK
dc.subjectData warehouseen_HK
dc.subjectOLAPen_HK
dc.subjectOptimizationen_HK
dc.titleOptimization in data cube system designen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0925-9902&volume=23&issue=1&spage=17&epage=45&date=2004&atitle=Optimization+in+Data+Cube+System+Designen_HK
dc.identifier.emailCheung, DW:dcheung@cs.hku.hken_HK
dc.identifier.emailKao, B:kao@cs.hku.hken_HK
dc.identifier.authorityCheung, DW=rp00101en_HK
dc.identifier.authorityKao, B=rp00123en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1023/B:JIIS.0000029669.16825.54en_HK
dc.identifier.scopuseid_2-s2.0-3042834703en_HK
dc.identifier.hkuros93316en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-3042834703&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume23en_HK
dc.identifier.issue1en_HK
dc.identifier.spage17en_HK
dc.identifier.epage45en_HK
dc.identifier.isiWOS:000221745000002-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridHung, E=7004256336en_HK
dc.identifier.scopusauthoridCheung, DW=34567902600en_HK
dc.identifier.scopusauthoridKao, B=35221592600en_HK
dc.identifier.citeulike33451-

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