Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/10490
Title: Improving database performance with a mixed fragmentation design
Authors: Gorla, N
Ng, V 
Law, DM
Keywords: Attribute partitioning
Data Mining
Database performance
Genetic algorithms
Mixed fragmentation
Tuple clustering
Issue Date: 2012
Publisher: Springer
Source: Journal of intelligent information systems, 2012, v. 39, no. 3, p. 559-576 How to cite?
Journal: Journal of Intelligent Information Systems 
Abstract: The performance of database operations can be enhanced with an efficient storage structure design using attribute partitioning and/or tuple clustering. Previous research deals mostly with attribute partitioning. We address here the combined problem of attribute partitioning and tuple clustering. We propose a novel approach for this mixed fragmentation problem by applying a genetic algorithm iteratively to attribute partitioning and tuple clustering sub-problems. We compared our results to attribute-only partitioning and random search solution, resulting in a database access cost reduction of upto 70% and 67% respectively. We analyzed the effect of varying genetic parameters on the optimal solution through experimentation.
URI: http://hdl.handle.net/10397/10490
ISSN: 0925-9902
DOI: 10.1007/s10844-012-0203-x
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