Paper Info
Title
Improving quality and convergence of genetic query optimizers
Abstract
The application of genetic programming strategies to query optimization has been proposed as a feasible way to solve the large join query problem. However, previous literature shows that the potentiality of evolutionary strategies has not been completely exploited in terms of convergence and quality of the returned query execution plans (QEP). In this paper, we propose two alternatives to improve the performance of a genetic optimizer and the quality of the resulting QEPs. First, we present a new method called Weighted Election that proposes a criterion to choose the QEPs to be crossed and mutated during the optimization time. Second, we show that the use of heuristics in order to create the initial population benefits the speed of convergence and the quality of the results. Moreover, we show that the combination of both proposals out-performs previous randomized algorithms, in the best cases, by several orders of magnitude for very large join queries.
Year
DOI
Venue
2007
10.1007/978-3-540-71703-4_3
DASFAA
Keywords
Field
DocType
evolutionary strategy,weighted election,genetic programming strategy,best case,query problem,genetic query optimizers,genetic optimizer,improving quality,previous literature,query execution plan,optimization time,out-performs previous randomized algorithm,randomized algorithm,genetics,query optimization
Convergence (routing),Query optimization,Population,Data mining,Randomized algorithm,Mathematical optimization,Fact table,Computer science,Database schema,Genetic programming,Heuristics,Database
Conference
Volume
ISSN
Citations 
4443
0302-9743
0
PageRank 
References 
Authors
0.34
17
4
Name
Order
Citations
PageRank
Victor Muntés-Mulero120422.79
Néstor Lafón-Gracia200.34
Josep Aguilar-Saborit3868.01
Josep-L. Larriba-Pey416217.44