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Distributed Query Plan Generation using Cuckoo Search Algorithm

Distributed Query Plan Generation using Cuckoo Search Algorithm

Monika Yadav, T. V. Vijay Kumar
Copyright: © 2017 |Volume: 6 |Issue: 1 |Pages: 15
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781522515210|DOI: 10.4018/IJEOE.2017010105
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MLA

Yadav, Monika, and T. V. Vijay Kumar. "Distributed Query Plan Generation using Cuckoo Search Algorithm." IJEOE vol.6, no.1 2017: pp.86-100. http://doi.org/10.4018/IJEOE.2017010105

APA

Yadav, M. & Kumar, T. V. (2017). Distributed Query Plan Generation using Cuckoo Search Algorithm. International Journal of Energy Optimization and Engineering (IJEOE), 6(1), 86-100. http://doi.org/10.4018/IJEOE.2017010105

Chicago

Yadav, Monika, and T. V. Vijay Kumar. "Distributed Query Plan Generation using Cuckoo Search Algorithm," International Journal of Energy Optimization and Engineering (IJEOE) 6, no.1: 86-100. http://doi.org/10.4018/IJEOE.2017010105

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Abstract

Query processing in distributed databases involves data transmission amongst sites capable of providing answers to a distributed query. For this, a distributed query processing strategy, which generates efficient query processing plans for a given distributed query, needs to be devised. Since in distributed databases, the data is fragmented and replicated at multiple sites, the number of query plans increases exponentially with increase in the number of sites capable of providing answers to a distributed query. As a result, generating efficient query processing plans, from amongst all possible query plans, becomes a complex problem. This distributed query plan generation (DQPG) problem has been addressed using the Cuckoo Search Algorithm (CSA) in this paper. Accordingly, a CSA based DQPG algorithm (DQPGCSA) that aims to generate Top-K query plans having minimum cost of processing a distributed query has been proposed. Experimental based comparison of DQPGCSA with the existing GA based DQPG algorithm shows that the former is able to generate Top-K query plans that have a comparatively lower query processing cost. This, in turn, reduces the query response time resulting in efficient decision making.

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