Genetic algorithm for database query optimization

Jorng Tzong Horng, Cheng Yan Kao, Baw Jhiune Liu

Research output: Contribution to conferencePaperpeer-review

8 Scopus citations

Abstract

Numerous decision support applications have been modeled as set covering and partitioning problems. We propose an extension to the database query language SQL to enable applications of these problems to be stated and solved directly by the database system. This will lead to the benefits of improved data independence, increased productivity and better performance. Six operators, namely cover, mincover, sumcover, partition, minpartition, and sumpartition are extended. In this paper, we presented genetic algorithms for the implementation of access routines for the proposed operators. We found that our genetic algorithm approach for extended operations and query optimization performed well both on the computational effort and the quality of the solutions through a variety of test problems. This approach makes it possible for a DBMS to respond to queries involving the proposed operators in a predicate restricted amount of time.

Original languageEnglish
Pages350-355
Number of pages6
StatePublished - 1994
EventProceedings of the 1st IEEE Conference on Evolutionary Computation. Part 1 (of 2) - Orlando, FL, USA
Duration: 27 Jun 199429 Jun 1994

Conference

ConferenceProceedings of the 1st IEEE Conference on Evolutionary Computation. Part 1 (of 2)
CityOrlando, FL, USA
Period27/06/9429/06/94

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