Parallel database query optimization using genetic algorithm

Pao Yue-kong Library Electronic Theses Database

Parallel database query optimization using genetic algorithm

 

Author: Chung, Shu-ning
Title: Parallel database query optimization using genetic algorithm
Degree: M.Sc.
Year: 2000
Subject: Relational databases
Database searching
Genetic algorithms
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Applied Mathematics
Pages: ii, 115 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1523574
URI: http://theses.lib.polyu.edu.hk/handle/200/1849
Abstract: Database query optimization has long been one of the difficult problems in the area of database system researches. For a query submitted against a database, a large number of different ways, or so called query execution plans, can be used to retrieve the data from the database. The objective of query optimization is to find the plan that minimizes the execution time or the usage of system resources. It has been known that the search space of query execution plans is growing exponentially when number of tables involved is increasing. In parallel database system, the consideration of processors allocation further complicates the problem. For this kind of combinatorial optimization problem, exhaustive search is not a practical solution because it requires huge amount of time to find the optimal plan. Instead, different heuristic methods have been developed by researchers to tackle the problems. In view of the growing popularity and success of applying genetic algorithm (GA) to solve combinatorial optimization problems in different areas, this dissertation explores the way to use GA to solve the parallel database query optimization problem. Integrated approach is used in this dissertation such that the two subproblems in parallel database query optimization, join ordering and processors allocation, are considered simultaneously. Detail implementation of the algorithm like what data structure is used to represent the population of chromosomes and what kinds of genetic operators are used to introduce diversity into the population are presented in the dissertation such that reader will get a clear picture of how GA can be applied to solve the query optimization problem. The results of a number of simulation tests are also presented at the end to demonstrate that GA has superior performance to some heuristic algorithms in optimizing different kinds of queries.

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