Journal of Computers, Vol 7, No 7 (2012), 1591-1598, Jul 2012
doi:10.4304/jcp.7.7.1591-1598

An Improved Approach for Materialized View Selection Based on Genetic Algorithm

Lijuan Zhou, Xiaoxu He, Kang Li

Abstract


This paper presents an improved genetic algorithm to solve the materialized view selection problem under query cost constraints. The algorithm dynamically changes the crossover probability and mutation probability in the process of genetic. In this way, it can not only maintain the population diversity, but also ensure the convergence of the genetic algorithm. So it effectively improves the optimization ability of genetic algorithm, thus avoiding the "evolutionary stagnation" problems. Meanwhile, the improved genetic algorithm increases the processing of invalid solution to avoid the "evolutionary stagnation" problems generated by invalid cycle, thereby the efficiency of materialized view selection is greatly improved.


Keywords


data warehouse; materialized view selection; genetic algorithms; evolutionary stagnation; invalid solution

References


 

[1] Shaojun Yang, Jincun Fan, Qingzhong Li. Materialized View Selection in Data Warehouse [J]. Applications of Computer, 2003, 22(9):58-60.

[2]

[3] H. Gupta. “Selection of Views to Materialize in a Data Warehouse”. Proceedings of the 23rd VLDB Conference, Athens, Greece 1997.

[4] H. Gupta, I. S. Mumick. Selection of views to materialize under a maintenance cost constraint. In Proc. Of the 7th Intl. Conf. On Database Theory, 1999: p453–470.
http://dx.doi.org/10.1007/3-540-49257-7_28

[5] Satyanarayana R Valluri, Soujanya Vadapalli, Kamalakar Karlapalem. View Relevance Driven Materialized View Selection in Data Warehousing Environment. The 13th Australasian Database Conference (ADC2002), Melbourne, Australia. Conferences in Research and Practice in Information Technology, 2002, v5.

[6] Chun Zhang, Xin Yao and Jian Yang. An Evolutionary Approach to Materialize Views Selection in a Data Warehouse Environment. IEEE Trans. On Systems, Man and Cybernetics, Part C, SEPT. 2001, v31 (3).

[7] S.Ligoudistianos, D.Theodoratos, T.Shllis. Experimental Evaluation of Data Warehouse Configuration Algorithms. Proceedings of the 9th Interational Workshop on Database and Expert Systems Applications. 1998: p218-223.

[8] A. Shukla, P. Deshpande, and J. F. Naughton, “Materialized view selection for multidimensional datasets,” in Proc. 24th Int. Conf. Very Large Data Bases, 1998, pp. 488–499.

[9] P. Kalnis, N. Mamoulis, and D. Papadias, “View Selection Using Randomized Search,” Data and Knowledge Eng., vol .42, no. 1, 2002.

[10] Lijuan Zhou. Materialized view selection based on query cost in data warehouse. Proceedings of SPIE,Data Mining and Knowledge Discovery:Theoty,Tools,And Technology VI, v5433, April 1-4,2004,Orland, USA.

[11] Lijuan Zhou. Selecting materialized views using random algorithm. Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2007, v 6570.

[12] Lijuan Zhou,Selecting materialized views in a data warehouse.IS&T/SPIE’s 15th Annual Symposium, Storage and Retrieval for Media Databass Vol.5021, 2003,Jan. California,USA.

[13] C. Zhang and J. Yang, “Genetic algorithm for materialized view selection in data warehouse environments,” Proceedings of the International Conference on Data Warehousing and Knowledge Discovery, LNCS, vol. 1676,pp. 116–125, 1999.

[14] J.Yang, K. Karlapalem, and Q. Li, “A framework for designing materialized views in data warehousing environment”. Proceedings of 17th IEEE International conference on Distributed Computing Systems, Maryland,U.S.A., May 1997.

[15] Rada Chirkova, Chen Li, Materializing Views with Minimal Size To Answer Queries. Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium. Principles of database systems,pp. 38-48,2003.

[16] journal or conference publications. Her primary research interests are in OLAP, data mining, and data warehouse.


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