Distributed Query Plan Generation Using HBMO

Abstract: Distributed query processing entails accessing data from multiple sites. In addition to the usual disk IO and CPU costs, the cost due to transmission of data between different sites, referred to as the site-to-site communication cost, also exists. This cost, being the major cost, needs to be reduced in order to improve the response time for distributed queries. One way to reduce this communication cost is by devising a distributed query processing strategy that involves fewer number of sites for answering the distributed queries. In this paper, a distributed query plan generation (DQPG) algorithm based on bee colony optimisation (BCO), which generates distributed query plans that involves less number of sites and have higher relation concentration in the participating sites, is presented. Additionally, the experimental comparison of the BCO-based DQPG algorithm with the GA-based DQPG algorithm exhibits that the former is able to generate comparatively better quality top-K query plans for a given distributed query.