Abstract
The increase in database amount, number of tables, blocks in the database and the size of query make Multi Join Query Optimization (MJQO) garnered considerable attention in Database Management System research. Many applications often involve complex multiple queries which share a lot of common subexpressions (CSEs) to Identifying and exploiting the CSEs to improve query performance is essential in these applications. MJQO aimed to find the optimal Query Execution Plan (QEP) in lower cost and minimum query execution time. The first contribution of this paper we examine the optimal join order (OJO) problem, which is a fundamental query optimization task for SQL-like queries in RDBMS, second contribution we propose a Swarm Intelligent approach to solve the MJQO problem. Two phase Artificial Bee Colony Algorithm (ABC) is used to solve the MJQO problems by simulating and exploiting the foraging behavior of honey bees. Results from the experiments show that the performance of two phase ABC when compared to Particle Swarm Optimization (PSO), Ant colony optimization (ACO) in terms of computational time is very promising. This indicates that the two phase ABC can solve MJQO problems in less amount of time, lower cost and more efficient than that of PSO and ACO.
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This work is supported by the ministry of Higher Education and Scientific Research (MHESR) Iraq for Research, and University of Misan collage of science.
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Alsaedi, A.K.Z., Ghazali, R., Deris, M.M. (2015). An Efficient Multi Join Query Optimization for Relational Database Management System Using Two Phase Artificial Bess Colony Algorithm. In: Badioze Zaman, H., et al. Advances in Visual Informatics. IVIC 2015. Lecture Notes in Computer Science(), vol 9429. Springer, Cham. https://doi.org/10.1007/978-3-319-25939-0_19
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