Distributed query plan generation using multi-objective ant colony optimisation
by Rahul Singh; Amit Kumar; T.V. Vijay Kumar
International Journal of Artificial Intelligence and Soft Computing (IJAISC), Vol. 5, No. 3, 2016

Abstract: In distributed relational databases, relations are fragmented and replicated at multiple disparate sites. As a result, for a distributed relational query, the number of possible query plans increases exponentially with an increase in the number of sites containing these relations. This leads to a large search space from which effective and efficient query plans are to be computed. This problem has already been addressed as a single objective optimisation problem using ant colony optimisation. In this paper, this problem is addressed as a bi-objective optimisation problem and solved using multi-objective ant colony optimisation (MOACO). Accordingly, a MOACO-based distributed query plan generation (DQPG) algorithm is proposed herein that generates Top-K query plans for a distributed query. Experimental comparisons of the proposed MOACO-based DQPG algorithm with the existing ACO-based DQPG algorithm show that for higher numbers of relations, the former is able to generate, comparatively, cost-effective Top-K query plans.

Online publication date: Mon, 22-Aug-2016

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