Abstract
Processing a distributed query entails accessing data from multiple sites. The inter site communication cost, being the dominant cost, needs to be reduced in order to improve the query response time. This would require the query optimizer to devise a distributed query processing strategy that would, for a given distributed query, generate query plans involving fewer number of sites in order to reduce the inter site communication cost. In this paper, a distributed query plan generation algorithm, based on the honey bee mating optimization (HBMO) technique that generates query plans for a distributed query involving less number of sites and higher relation concentration in the participating sites, is presented. Further, experimental comparison of the proposed HBMO based DQPG algorithm with the GA based DQPG algorithm shows that the former is able to generate distributed query plans at a comparatively lesser total query processing cost, which in turn would lead to efficient processing of a distributed query.
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Kumar, T.V.V., Arun, B., Kumar, L. (2013). Distributed Query Plan Generation Using HBMO. In: Ramanna, S., Lingras, P., Sombattheera, C., Krishna, A. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2013. Lecture Notes in Computer Science(), vol 8271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44949-9_27
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DOI: https://doi.org/10.1007/978-3-642-44949-9_27
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