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Improving database performance with a mixed fragmentation design

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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.

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Notes

  1. The authors are thankful to the reviewer that provided this feedback.

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Authors and Affiliations

  1. American University of Sharjah, PO Box 26666, Sharjah, UAE

    Narasimhaiah Gorla

  2. Hong Kong Polytechnic University, Hong Kong, China

    Vincent Ng & Dik Man Law

Authors
  1. Narasimhaiah Gorla
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  2. Vincent Ng
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  3. Dik Man Law
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Corresponding author

Correspondence to Narasimhaiah Gorla.

Additional information

An earlier version of this paper has been presented at the 2003 ACM Symposium on Applied Computing (SAC)

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Gorla, N., Ng, V. & Law, D.M. Improving database performance with a mixed fragmentation design. J Intell Inf Syst 39, 559–576 (2012). https://doi.org/10.1007/s10844-012-0203-x

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  • DOI: https://doi.org/10.1007/s10844-012-0203-x

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