Abstract
Query Processing is a key determinant in the overall performance of distributed databases. It requires processing of data at their respective sites and transmission of the same between them. These together constitute a distributed query processing strategy (DQP). DQP aims to arrive at an efficient query processing strategy for a given query. This strategy involves generation of efficient query plans for a distributed query. In case of distributed relational queries, the number of possible query plans grows exponentially with an increase in the number of relations accessed by the query. This number increases further when the relations, accessed by the query, have replicas at different sites. Such a large search space renders it infeasible to find optimal query plans. This paper presents a query plan generation algorithm that attempts to generate optimal query plans, for a given query, using genetic algorithm. The query plans so generated involve fewer sites, thus leading to efficient query processing. Further, experimental results show that the proposed algorithm converges quickly towards optimal query plans for an observed crossover and mutation probability.
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References
Bennett, K., Ferris, M.C., Ioannidis, Y.E.: A Genetic Algorithm for Database Query Optimization. In: Proceedings of The Fourth International Conference on Genetic Algorithms, pp. 400–407 (1991)
Black, P., Luk, W.: A new heuristic for generating semi-join programs for distributed query processing. In: Proceedings of the IEEE 6th International Computer Software and Application Conference, Chicago, November 8-12, vol. Ill, pp. 581–588. IEEE, New York (1982)
Bodorik, P., Riordon, J.S.: “A threshold mechanism for distributed query processing,” Proceedings of the sixteenth annual conference on Computer science table of contents Atlanta, Georgia, United States, pp. 616 – 625, 1988
Ceri, S., Pelgatti, G.: Distributed Databases Principles & Systems, McGraw-Hill international edn., Computer Science Series (1985)
Chang, J.: A heuristic approach to distributed query processing. In: Proceedings of the 8th Internatmnal Conference on Very Large Data Bases, VLDB Endowment, Saratoga, California, pp. 54–61 (1982)
Chen, A.L.P., Brill, D., Templeton, M., Yu, C.T.: Distributed Query Processing in a Multiple Database System. IEEE Journal on Selected Areas in Communications I(3) (April 1989)
Chu, W.W., Hurley, P.: Optimal Query Processing for Distributed Database Systems. IEEE Transactions on Computers C-31(9) (september 1982)
Dong, H., Liang, Y.: Genetic Algorithms for Large Join Query Optimization. In: The Proceedings of GECCO 2007, London, UK, pp. 1211–1218 (2007)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in Genetic Algorithms. In: Foundations of Genetic Algorithms, pp. 69–93. Morgan Kaufman (1991)
Gregory, M.: Genetic Algorithm Optimization of Distributed Database Queries. In: Proceedings of International Conference on Evolutionary Computation, pp. 71–276 (1998)
Hevner, A.R., Yao, S.B.: Query processing in distributed database systems. IEEE Transactions on Software Engineering SE-5, 177–187 (1979)
Ioannidis, Y.E., Kang, Y.C.: Randomized Algorithms for Optimizing Large Join Queries. In: Proceedings of ACM-SIGMOD Conference on Management of Data, Atlantic City, NJ, pp. 312–321 (May 1990)
Ioannidis, Y.E., Kang, Y.C.: Query Optimization by Simulated Annealing. In: Proceedings of the 1987 ACM-SIGMOD Conference, San Franscisco, CA, pp. 9–22 (1987)
Kambayashi, Y., Yoshikawa, M.: Query processing utilizing dependencies and horizontal decomposition. In: Proceedings of the ACM-SIGMOD International Conference on Management of Data, San Jose, Calif., May 23-26, pp. 55–67. ACM, New York (1983)
Kambayashi, Y., Yoshikawa, M., Yajima, S.: Query processing for distributed databases using generalized semijoins. In: Proceedings of the ACM-SIGMOD International Conference on Management of Data, Orlando, Fla., June 2-4, pp. 151–160. ACM, New York (1982)
Kossmann, D.: The State of the Art in Distributed Query Processing. ACM Computing Surveys 32(4), 422–469 (2000)
Liu, C., Yu, C.: Performance issues in distributed query processing. IEEE Transactions on Parallel and Distributed System 4(8), 889–905 (1993)
Mitchell, M.: An Introduction to Genetic Algorithm. Prentice Hall of India (1998)
Rho, S., March, S.T.: Optimizing distributed join queries: A genetic algorithmic approach. Annals of Operations Research 71, 199–228 (1997)
Stiphane, L., Eugene, W.: A State Transition Model for Distributed Query Processing. ACM Transactions on Database Systems 11(3), Pages 2 (1986)
Vijay Kumar, T.V., Singh, V., Verma, A.K.: Distributed Query Processing Plans Generation using Genetic Algorithm. International Journal of Computer Theory and Engineering 3(1), 38–45 (2011)
Yu, C.T., Chang, C.C.: Distributed Query Processing. ACM Computing Surveys 16(4), 399–433 (1984)
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Vijay Kumar, T.V., Panicker, S. (2011). Generating Query Plans for Distributed Query Processing Using Genetic Algorithm. In: Liu, B., Chai, C. (eds) Information Computing and Applications. ICICA 2011. Lecture Notes in Computer Science, vol 7030. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25255-6_97
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DOI: https://doi.org/10.1007/978-3-642-25255-6_97
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