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Titolo:
Semantic information-based alternative plan generation for multiple query optimization
Autore:
Polat, F; Cosar, A; Alhajj, R;
Indirizzi:
Middle E Tech Univ, Dept Comp Engn, TR-06531 Ankara, Turkey Middle E Tech Univ Ankara Turkey TR-06531 Engn, TR-06531 Ankara, Turkey American Univ, Dept Math & Comp Sci, Sharjah, U Arab Emirates American Univ Sharjah U Arab Emirates omp Sci, Sharjah, U Arab Emirates
Titolo Testata:
INFORMATION SCIENCES
fascicolo: 1-4, volume: 137, anno: 2001,
pagine: 103 - 133
SICI:
0020-0255(200109)137:1-4<103:SIAPGF>2.0.ZU;2-R
Fonte:
ISI
Lingua:
ENG
Keywords:
alternative plan generation; common tasks; multiple query optimization; performance analysis; relational databases; semantic information;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
32
Recensione:
Indirizzi per estratti:
Indirizzo: Alhajj, R Univ Calgary, Dept Comp Sci, Adv Database Syst & Applicat Lab, 2500 Univ Dr NW, Calgary, AB T1N 2N2, Canada Univ Calgary 2500 Univ Dr NW Calgary AB Canada T1N 2N2 2, Canada
Citazione:
F. Polat et al., "Semantic information-based alternative plan generation for multiple query optimization", INF SCI, 137(1-4), 2001, pp. 103-133

Abstract

This paper addresses the impact of semantic information about queries on alternative plan generation (APG) for multiple query optimization (MQO). MQOcovers optimizing the execution of a set of queries together where each query in the set to be optimized has several alternative execution plans. A multiple query optimizer selects an alternative plan for each query to obtain an optimal global execution plan. Our approach uses information such as common relations, common possible joins and common conditions to investigatefactors that provide a good estimation of shared tasks between queries. Itgenerates alternative plans for queries having more common tasks. The amount of possible sharing between the queries is determined and used to obtaina fewer number of high quality alternative plans. While doing this, we tryto preserve the optimum global execution cost obtained as the result of MQO. Finally, the proposed approach is compared with the other APG approachesdescribed in the literature. The obtained results show that a near optimalsolution can be obtained with our technique in less time. (C) 2001 Elsevier Science Inc. All rights reserved.

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Documento generato il 21/09/20 alle ore 08:12:33