Abstract—Large amount of information available in distributed databases needs to be exploited by organizations in order to be competitive in the market. In order to exploit this information, queries are posed thereupon. These queries require efficient processing, which mandates devising of optimal query processing strategies that generate efficient query processing plans for a given distributed query. The number of possible query processing plans grows rapidly with increase in the number of sites used, and relations accessed, by the query. There is a need to generate efficient query processing plans from among all possible query plans. The proposed approach attempts to generate such query processing plans using genetic algorithm. The approach generates query plans based on the closeness of data required to answer the user query. The query plans having the required data residing in fewer sites, are considered more efficient, and are thus preferred, over query plans having data spread across a large number of sites. The query plans so generated involve minimum number of sites for answering the user query leading to efficient query processing. Further, experimental results show that the GA based approach converges quickly towards the optimal query processing plans for an observed crossover and mutation rate.
Index Terms—Distributed Query Processing, Genetic Algorithm
T. V. Vijay Kumar is presently an Assistant Professor at School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India (Email: tvvijaykumar@hotmail.com).
Vikram Singh is presently an Assistant Manager, AG3 Department, IT Division at Maruti Suzuki India Limited, Gurgaon, Haryana, India (Email: vikramsdream@gmail.com).
Ajay Kumar Verma is presently pursuing his Ph.D. from School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, India (Email: ajayverma81@gmail.com).
Cite: T. V. Vijay Kumar, Vikram Singh and Ajay Kumar Verma, "Distributed Query Processing Plans Generation using Genetic Algorithm," International Journal of Computer Theory and Engineering vol. 3, no. 1, pp. 38-45, 2011.
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