Applying genetic algorithms in database partitioning

Vincent To Yee Ng, Dik Man Law, Narasimhaiah Gorla, Chi Kong Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)

Abstract

One popular technique used to enhance database performance is attribute partitioning. Attribute partitioning is the process of subdividing the attributes of a relation and then grouping them into fragments so as to minimize the number of disk access by all transactions. On the other hand, tuple clustering, which is the process of rearranging the order of tuples so. that frequently queried tuples are grouped into as few blocks as possible, is mostly ignored. In this paper, we address the need of considering the n-ary attribute partitioning and tuple clustering at the same time in a relational database. A new algorithm is proposed for mixed fragmentation design using genetic algorithm. Java programs have been developed to implement the genetic algorithm for mixed fragmentation and the results are promising. It provides an improvement over previous works which considered vertical partitioning and tuple clustering separately. Comparisons with exhaustive enumeration and random search are also presented.
Original languageEnglish
Title of host publicationProceedings of the ACM Symposium on Applied Computing
Pages544-549
Number of pages6
Publication statusPublished - 18 Jul 2003
EventProceedings of the 2003 ACM Symposium on Applied Computing - Melbourne, FL, United States
Duration: 9 Mar 200312 Mar 2003

Conference

ConferenceProceedings of the 2003 ACM Symposium on Applied Computing
Country/TerritoryUnited States
CityMelbourne, FL
Period9/03/0312/03/03

ASJC Scopus subject areas

  • Software

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