Subquery allocations in distributed databases using genetic algorithms

Autores
Gorla, Narasimhaiah; Song, Suk-Kyu
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Minimization of query execution time is an important performance objective in distributed databases design. While total time is to be minimized for On Line Transaction Processing (OLTP) type queries, response time has to be minimized in Decision Support type queries. Thus different allocations of subqueries to sites and their execution plans are optimal based on the query type. We formulate the subquery allocation problem and provide analytical cost models for these two objective functions. Since the problem is NP-hard, we solve the problem using genetic algorithm (GA). Our results indicate query execution plans with total minimization objective are inefficient for response time objective and vice versa. The GA procedure is tested with simulation experiments using complex queries of up to 20 joins. Comparison of results with exhaustive enumeration indicates that GA produced optimal solutions in all cases in much less time.
Facultad de Informática
Materia
Ciencias Informáticas
Distributed databases
response time minimization
physical database design
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9665
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/9665
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Subquery allocations in distributed databases using genetic algorithmsGorla, NarasimhaiahSong, Suk-KyuCiencias InformáticasDistributed databasesresponse time minimizationphysical database designMinimization of query execution time is an important performance objective in distributed databases design. While total time is to be minimized for On Line Transaction Processing (OLTP) type queries, response time has to be minimized in Decision Support type queries. Thus different allocations of subqueries to sites and their execution plans are optimal based on the query type. We formulate the subquery allocation problem and provide analytical cost models for these two objective functions. Since the problem is NP-hard, we solve the problem using genetic algorithm (GA). Our results indicate query execution plans with total minimization objective are inefficient for response time objective and vice versa. The GA procedure is tested with simulation experiments using complex queries of up to 20 joins. Comparison of results with exhaustive enumeration indicates that GA produced optimal solutions in all cases in much less time.Facultad de Informática2010-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf31-37http://sedici.unlp.edu.ar/handle/10915/9665enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr10-6.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:50:44Zoai:sedici.unlp.edu.ar:10915/9665Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:50:45.254SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Subquery allocations in distributed databases using genetic algorithms
title Subquery allocations in distributed databases using genetic algorithms
spellingShingle Subquery allocations in distributed databases using genetic algorithms
Gorla, Narasimhaiah
Ciencias Informáticas
Distributed databases
response time minimization
physical database design
title_short Subquery allocations in distributed databases using genetic algorithms
title_full Subquery allocations in distributed databases using genetic algorithms
title_fullStr Subquery allocations in distributed databases using genetic algorithms
title_full_unstemmed Subquery allocations in distributed databases using genetic algorithms
title_sort Subquery allocations in distributed databases using genetic algorithms
dc.creator.none.fl_str_mv Gorla, Narasimhaiah
Song, Suk-Kyu
author Gorla, Narasimhaiah
author_facet Gorla, Narasimhaiah
Song, Suk-Kyu
author_role author
author2 Song, Suk-Kyu
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Distributed databases
response time minimization
physical database design
topic Ciencias Informáticas
Distributed databases
response time minimization
physical database design
dc.description.none.fl_txt_mv Minimization of query execution time is an important performance objective in distributed databases design. While total time is to be minimized for On Line Transaction Processing (OLTP) type queries, response time has to be minimized in Decision Support type queries. Thus different allocations of subqueries to sites and their execution plans are optimal based on the query type. We formulate the subquery allocation problem and provide analytical cost models for these two objective functions. Since the problem is NP-hard, we solve the problem using genetic algorithm (GA). Our results indicate query execution plans with total minimization objective are inefficient for response time objective and vice versa. The GA procedure is tested with simulation experiments using complex queries of up to 20 joins. Comparison of results with exhaustive enumeration indicates that GA produced optimal solutions in all cases in much less time.
Facultad de Informática
description Minimization of query execution time is an important performance objective in distributed databases design. While total time is to be minimized for On Line Transaction Processing (OLTP) type queries, response time has to be minimized in Decision Support type queries. Thus different allocations of subqueries to sites and their execution plans are optimal based on the query type. We formulate the subquery allocation problem and provide analytical cost models for these two objective functions. Since the problem is NP-hard, we solve the problem using genetic algorithm (GA). Our results indicate query execution plans with total minimization objective are inefficient for response time objective and vice versa. The GA procedure is tested with simulation experiments using complex queries of up to 20 joins. Comparison of results with exhaustive enumeration indicates that GA produced optimal solutions in all cases in much less time.
publishDate 2010
dc.date.none.fl_str_mv 2010-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9665
url http://sedici.unlp.edu.ar/handle/10915/9665
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr10-6.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
31-37
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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