Abstract
In spatial join processing, a common method to minimize the I/O cost is to partition the spatial objects into clusters and then to schedule the processing of the clusters such that the number of times the same objects to be fetched into memory can be minimized. The key issue of cluster scheduling is how to produce a better sequence of clusters to guide the scheduling. This paper describes strategies that apply the ant colony optimization (ACO) algorithm to produce cluster scheduling sequence. Since the structure of the ACO is highly suitable for parallelization, parallel algorithms are also developed to improve the performance of the algorithms. We evaluated and illustrated that that the scheduling sequence produced by the new method is much better than existing approaches.
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References
Samet, H., Aref, W.: Spatial Data Models and Query Processing. In: Modern Database Systems. Addison-Wesley Publishing Company, Inc, Reading (1995)
Xiao, J., Zhang, Y., Jia, X.: Clustering Non-uniform- Sized Spatial Objects to Reduce I/O Cost for Spatial Join Processing. The Computer Journal 44(5) (2001)
Xiao, J., Zhang, Y., Jia, X., Zhou, X.: A Schedule of Join Operations to Reduce I/O Cost in Spatial Database Systems. Data & Knowledge Engineering 35, 299–317 (2000)
Gambardella, L.M., Dorigo, M.: An Ant Colony System Hybridized with a New Local Search for the Sequential Ordering Problem. INFORMS Journal on Computing 12(3), 237–255 (2000)
Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B 26(1), 29–41 (1996)
Bullnheimer, B., Kotsis, G., Strauss, C.: Parallelization Strategies for the ANT System. In: De Leone, R., Murli, A., Pardalos, P.M., Toraldo, G. (eds.) High Performance Algorithms and Software in Nonlinear Optimization. Kluwer International Series in Applied Optimization, vol. 24, Kluwer Academic Publishers, Dordrecht (1998)
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© 2004 Springer-Verlag Berlin Heidelberg
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Xiao, J., Li, H. (2004). Sequential and Parallel Ant Colony Strategies for Cluster Scheduling in Spatial Databases. In: Cao, J., Yang, L.T., Guo, M., Lau, F. (eds) Parallel and Distributed Processing and Applications. ISPA 2004. Lecture Notes in Computer Science, vol 3358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30566-8_77
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DOI: https://doi.org/10.1007/978-3-540-30566-8_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24128-7
Online ISBN: 978-3-540-30566-8
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