Improving database performance with a mixed fragmentation design

Narasimhaiah Gorla, Vincent To Yee Ng, Dik Man Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)559-576
Number of pages18
JournalJournal of Intelligent Information Systems
Volume39
Issue number3
DOIs
Publication statusPublished - 1 Dec 2012

Keywords

  • Attribute partitioning
  • Data Mining
  • Database performance
  • Genetic algorithms
  • Mixed fragmentation
  • Tuple clustering

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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