Abstract
Multi-join query optimization (MJQO) is an important technique for designing and implementing database manage system. It is a crucial factor that affects the capability of database. This paper proposes a new algorithm to solve the problem of MJQO based on ant colony optimization (ACO). In this paper, details of the algorithm used to solve MJQO problem have been interpreted, including how to define heuristic information, how to implement local pheromone update and global pheromone update and how to design state transition rule. After repeated iteration, a reasonable solution is obtained. Compared with genetic algorithm, the simulation result indicates that ACO is more effective and efficient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Shekita, E., Young, H., Tan, K.L.: Multi-join optimization for symmetric multiprocessors. In: Proc. Of the Conf. on Very Large Data Bases (VLDB), Dublin, Ireland, pp. 479–492 (1993)
Krishnamurthy, W.R., Boral, H., Zaniolo, C.: Optimization of nonrecursive queries. In: Proc. Of the Conf. On Very Large Data Base (sVLDB), Kyoto, Japan, pp. 128–137 (1986)
Cao, Y., Fang, Q.: Parallel Query Optimization Techniques for Multi-Join Expressions Based on Genetic Algorithmspline. Journal of Software 13(2), 250–256 (2002)
Swami, A., Iyer, B.: A polynomial time algorithm for optimizing join queries. In: Proc. IEEE Conf. on Data Engineering, Vienna, Austria, pp. 345–354 (1993)
Dorigom, M., Gambardella, L.M.: Ant colonies for the traveling salesman problem. BioSystems 43(2), 73–81 (1997)
Maniezzo, V., Dorigo, M., Colorni, A.: The ant system applied to the quadratic assignmentproblem, IRIDIA/94-28. Universite de Bruxelles, Belgium (1994)
Colorni, A., Dorigo, M., Maniezzo, V., et al.: Ant system for job-shop scheduling. Belgian Journal of Operations Research, Statistics and Computer Science 4(1), 39–53 (1994)
Bullnheimer, B., Hartl, R.F., Strauss, C.: Applying the ant system to the vehicle routing problem. In: Osman, I.H., Vo, S., Martello, S., Roucairol, C. (eds.) Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pp. 109–120. Kluwer Academics, Dordrecht (1998)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics–Part B 26(1), 29–41 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, N., Liu, Y., Dong, Y., Gu, J. (2008). Application of Ant Colony Optimization Algorithm to Multi-Join Query Optimization. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-92137-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92136-3
Online ISBN: 978-3-540-92137-0
eBook Packages: Computer ScienceComputer Science (R0)