Abstract
The problem of selecting an optimal fragmentation schema of a data warehouse is more challenging compared to that in relational and object databases. This challenge is due to the several choices of partitioning star or snowflake schemas. Data partitioning is beneficial if and only if the fact table is fragmented based on the partitioning schemas of dimension tables. This may increase the number of fragments of the fact tables dramatically and makes their maintenance very costly. Therefore, the right selection of fragmenting schemas is important for better performance of OLAP queries. In this paper, we present a genetic algorithm for schema partitioning selection problem. The proposed algorithm gives better solutions since the search space is constrained by the schema partitioning. We conduct several experimental studies using the APB-1 release II benchmark for validating the proposed algorithm.
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
Bäck, T.: Evolutionnary algorithms in theory and practice. Oxford University Press, New York (1995)
Bellatreche, L., Karlapalem, K., Mohania, M.: What can partitioning do for your data warehouses and data marts. In: Proceedings of the International Database Engineering and Application Symposium (IDEAS 2000), September 2000, pp. 437–445 (2000)
Bellatreche, L., Schneider, M., Lorinquer, H., Mohania, M.: Bringing together partitioning, materialized views and indexes to optimize performance of relational data warehouses. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2004. LNCS, vol. 3181, pp. 15–25. Springer, Heidelberg (2004)
Bennett, K.P., Ferris, M.C., Ioannidis, Y.E.: A genetic algorithm for database query optimization. In: Proceedings of the 4th International Conference on Genetic Algorithms, July 1991, pp. 400–407 (1991)
OLAP Council. Apb-1 olap benchmark, release ii (1998), http://www.olapcouncil.org/research/bmarkly.htm
Frieder, O., Siegelmann, H.T.: Multiprocessor document allocation: A genetic algorithm approach. IEEE Transactions on Knowledge and Data Engineering 9(4), 640–642 (1997)
Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Kalnis, P., Papadias, D.: Proxy-server architecture for olap. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (2001)
Loukopoulos, T., Ahmad, I.: Static and adaptive distributed data replication using genetic algorithms. Journal of Parallel and Distributed Computing 64(11), 1270–1285 (2004)
Özsu, M.T., Valduriez, P.: Principles of Distributed Database Systems, 2nd edn. Prentice Hall, Englewood Cliffs (1999)
Sanjay, A., Narasayya, V.R., Yang, B.: Integrating vertical and horizontal partitioning into automated physical database design. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, June 2004, pp. 359–370 (2004)
Sanjay, A., Surajit, C., Narasayya, V.R.: Automated selection of materialized views and indexes in microsoft sql server. In: Proceedings of the International Conference on Very Large Databases, September 2000, pp. 496–505 (2000)
Stöhr, T., Märtens, H., Rahm, E.: Multi-dimensional database allocation for parallel data warehouses. In: Proceedings of the International Conference on Very Large Databases, pp. 273–284 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bellatreche, L., Boukhalfa, K. (2005). An Evolutionary Approach to Schema Partitioning Selection in a Data Warehouse. In: Tjoa, A.M., Trujillo, J. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2005. Lecture Notes in Computer Science, vol 3589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11546849_12
Download citation
DOI: https://doi.org/10.1007/11546849_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28558-8
Online ISBN: 978-3-540-31732-6
eBook Packages: Computer ScienceComputer Science (R0)