Comparison of Different Solutions for Solving the Optimization Problem of Large Join Queries

The article explores the optimization of queries using genetic algorithms and compares it with the conventional query optimization component. Genetic algorithms (GAs), as a data mining technique, have been shown to be a promising technique in solving the ordering of join operations in large join que...

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Bibliographic Details
Published in2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications pp. 51 - 55
Main Author Petkovic, Dusan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2010
Subjects
Algebra
Biological cells
Biological processes
Data mining
Database systems
Genetic algorithms
Genetic mutations
Large join queries
PostgreSQL
Query optimization
Query processing
Relational databases
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ISBN9781424460816
1424460816
DOI10.1109/DBKDA.2010.1

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Summary:The article explores the optimization of queries using genetic algorithms and compares it with the conventional query optimization component. Genetic algorithms (GAs), as a data mining technique, have been shown to be a promising technique in solving the ordering of join operations in large join queries. In practice, a genetic algorithm has been implemented in the PostgreSQL database system. Using this implementation, we compare the conventional component for an exhaustive search with the corresponding module based on a genetic algorithm. Our results show that the use of a genetic algorithm is a viable solution for optimization of large join queries, i.e., that the use of such a module outperforms the conventional query optimization component for queries with more than 12 join operations.
ISBN:9781424460816
1424460816
DOI:10.1109/DBKDA.2010.1