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Distributed Query Plan Generation Using Firefly Algorithm

Distributed Query Plan Generation Using Firefly Algorithm

Neha Singh, Jay Prakash, T.V. Vijay Kumar
Copyright: © 2016 |Volume: 6 |Issue: 1 |Pages: 22
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781466691247|DOI: 10.4018/IJOCI.2016010103
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MLA

Singh, Neha, et al. "Distributed Query Plan Generation Using Firefly Algorithm." IJOCI vol.6, no.1 2016: pp.29-50. http://doi.org/10.4018/IJOCI.2016010103

APA

Singh, N., Prakash, J., & Kumar, T. V. (2016). Distributed Query Plan Generation Using Firefly Algorithm. International Journal of Organizational and Collective Intelligence (IJOCI), 6(1), 29-50. http://doi.org/10.4018/IJOCI.2016010103

Chicago

Singh, Neha, Jay Prakash, and T.V. Vijay Kumar. "Distributed Query Plan Generation Using Firefly Algorithm," International Journal of Organizational and Collective Intelligence (IJOCI) 6, no.1: 29-50. http://doi.org/10.4018/IJOCI.2016010103

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Abstract

A large number of queries are posed daily against the distributed databases spread across the globe. Query processing strategies are used to generate efficient query plans for these queries. The number of such query plans increases exponentially with the increase in the number of involved sites and relations accessed by the query. Further, this complexity increases if the data is fragmented and replicated across multiple sites. This problem, referred to as the distributed query plan generation (DQPG) problem, is a combinatorial optimization problem. An attempt has been made in this paper to solve this DQPG problem using the Firefly Algorithm (FA), which is inspired by the flashing behaviour of fireflies in nature. The proposed FA based DQPG algorithm (DQPGFA), aims to generate distributed query plans incurring minimum Query Proximity Cost (QPC) value. The experimental results show that DQPGFA, in comparison to the GA based DQPG algorithm (DQPGGA), was able to select Top-K query plans that had a comparatively lesser average QPC value. Such generated query plans would, most likely, lead to an improvement in the query response time and thereby would result in effective and efficient decision making.

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