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- Scopus: eid_2-s2.0-0036162986
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Article: Evolutionary algorithms for allocating data in distributed database systems
Title | Evolutionary algorithms for allocating data in distributed database systems |
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Authors | |
Keywords | Data allocation Distributed database systems Genetic algorithm Mean field annealing Neighborhood search Optimal allocation Query processing Simulated evolution |
Issue Date | 2002 |
Publisher | Springer 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? |
Abstract | A 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 Identifier | http://hdl.handle.net/10722/73757 |
ISSN | 2023 Impact Factor: 1.5 2023 SCImago Journal Rankings: 0.442 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ahmad, I | en_HK |
dc.contributor.author | Karlapalem, K | en_HK |
dc.contributor.author | Kwok, YK | en_HK |
dc.contributor.author | So, SK | en_HK |
dc.date.accessioned | 2010-09-06T06:54:28Z | - |
dc.date.available | 2010-09-06T06:54:28Z | - |
dc.date.issued | 2002 | en_HK |
dc.identifier.citation | Distributed And Parallel Databases, 2002, v. 11 n. 1, p. 5-32 | en_HK |
dc.identifier.issn | 0926-8782 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/73757 | - |
dc.description.abstract | A 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.language | eng | en_HK |
dc.publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0926-8782 | en_HK |
dc.relation.ispartof | Distributed and Parallel Databases | en_HK |
dc.subject | Data allocation | en_HK |
dc.subject | Distributed database systems | en_HK |
dc.subject | Genetic algorithm | en_HK |
dc.subject | Mean field annealing | en_HK |
dc.subject | Neighborhood search | en_HK |
dc.subject | Optimal allocation | en_HK |
dc.subject | Query processing | en_HK |
dc.subject | Simulated evolution | en_HK |
dc.title | Evolutionary algorithms for allocating data in distributed database systems | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Kwok, YK:ykwok@eee.hku.hk | en_HK |
dc.identifier.authority | Kwok, YK=rp00128 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1023/A:1013324605452 | en_HK |
dc.identifier.scopus | eid_2-s2.0-0036162986 | en_HK |
dc.identifier.hkuros | 67359 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-0036162986&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 11 | en_HK |
dc.identifier.issue | 1 | en_HK |
dc.identifier.spage | 5 | en_HK |
dc.identifier.epage | 32 | en_HK |
dc.identifier.isi | WOS:000172926600001 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Ahmad, I=7201878459 | en_HK |
dc.identifier.scopusauthorid | Karlapalem, K=7004229176 | en_HK |
dc.identifier.scopusauthorid | Kwok, YK=7101857718 | en_HK |
dc.identifier.scopusauthorid | So, SK=36799951200 | en_HK |
dc.identifier.issnl | 0926-8782 | - |