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Article: Evolutionary algorithms for allocating data in distributed database systems

TitleEvolutionary algorithms for allocating data in distributed database systems
Authors
KeywordsData allocation
Distributed database systems
Genetic algorithm
Mean field annealing
Neighborhood search
Optimal allocation
Query processing
Simulated evolution
Issue Date2002
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0926-8782
Citation
Distributed And Parallel Databases, 2002, v. 11 n. 1, p. 5-32 How to Cite?
AbstractA major cost in executing queries in a distributed database system is the data transfer cost incurred in transferring relations (fragments) accessed by a query from different sites to the site where the query is initiated. The objective of a data allocation algorithm is to determine an assignment of fragments at different sites so as to minimize the total data transfer cost incurred in executing a set of queries. This is equivalent to minimizing the average query execution time, which is of primary importance in a wide class of distributed conventional as well as multimedia database systems. The data allocation problem, however, is NP-complete, and thus requires fast heuristics to generate efficient solutions. Furthermore, the optimal allocation of database objects highly depends on the query execution strategy employed by a distributed database system, and the given query execution strategy usually assumes an allocation of the fragments. We develop a site-independent fragment dependency graph representation to model the dependencies among the fragments accessed by a query, and use it to formulate and tackle data allocation problems for distributed database systems based on query-site and move-small query execution strategies. We have designed and evaluated evolutionary algorithms for data allocation for distributed database systems.
Persistent Identifierhttp://hdl.handle.net/10722/73757
ISSN
2015 Impact Factor: 0.8
2015 SCImago Journal Rankings: 0.593
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorAhmad, Ien_HK
dc.contributor.authorKarlapalem, Ken_HK
dc.contributor.authorKwok, YKen_HK
dc.contributor.authorSo, SKen_HK
dc.date.accessioned2010-09-06T06:54:28Z-
dc.date.available2010-09-06T06:54:28Z-
dc.date.issued2002en_HK
dc.identifier.citationDistributed And Parallel Databases, 2002, v. 11 n. 1, p. 5-32en_HK
dc.identifier.issn0926-8782en_HK
dc.identifier.urihttp://hdl.handle.net/10722/73757-
dc.description.abstractA major cost in executing queries in a distributed database system is the data transfer cost incurred in transferring relations (fragments) accessed by a query from different sites to the site where the query is initiated. The objective of a data allocation algorithm is to determine an assignment of fragments at different sites so as to minimize the total data transfer cost incurred in executing a set of queries. This is equivalent to minimizing the average query execution time, which is of primary importance in a wide class of distributed conventional as well as multimedia database systems. The data allocation problem, however, is NP-complete, and thus requires fast heuristics to generate efficient solutions. Furthermore, the optimal allocation of database objects highly depends on the query execution strategy employed by a distributed database system, and the given query execution strategy usually assumes an allocation of the fragments. We develop a site-independent fragment dependency graph representation to model the dependencies among the fragments accessed by a query, and use it to formulate and tackle data allocation problems for distributed database systems based on query-site and move-small query execution strategies. We have designed and evaluated evolutionary algorithms for data allocation for distributed database systems.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=0926-8782en_HK
dc.relation.ispartofDistributed and Parallel Databasesen_HK
dc.subjectData allocationen_HK
dc.subjectDistributed database systemsen_HK
dc.subjectGenetic algorithmen_HK
dc.subjectMean field annealingen_HK
dc.subjectNeighborhood searchen_HK
dc.subjectOptimal allocationen_HK
dc.subjectQuery processingen_HK
dc.subjectSimulated evolutionen_HK
dc.titleEvolutionary algorithms for allocating data in distributed database systemsen_HK
dc.typeArticleen_HK
dc.identifier.emailKwok, YK:ykwok@eee.hku.hken_HK
dc.identifier.authorityKwok, YK=rp00128en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1023/A:1013324605452en_HK
dc.identifier.scopuseid_2-s2.0-0036162986en_HK
dc.identifier.hkuros67359en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0036162986&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume11en_HK
dc.identifier.issue1en_HK
dc.identifier.spage5en_HK
dc.identifier.epage32en_HK
dc.identifier.isiWOS:000172926600001-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridAhmad, I=7201878459en_HK
dc.identifier.scopusauthoridKarlapalem, K=7004229176en_HK
dc.identifier.scopusauthoridKwok, YK=7101857718en_HK
dc.identifier.scopusauthoridSo, SK=36799951200en_HK

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