Multiobjective Differential Evolution Algorithm Using Binary Encoded Data in Selecting Views for Materializing in Data Warehouse

  • Rajib Goswami
  • Dhruba Kumar Bhattacharyya
  • Malayananda Dutta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)


In this paper, we define the view selection process for materializing in data warehouse as a multiobjective optimization problem. We have implemented multiobjective Differential Evolution (DE) algorithm for binary encoded data to solve this problem. In our approach, to control population in intermediate generations of the differential evolution process by maintaining diversity in solution space with necessary elitism, the solutions of intermediate generations are first ranked according to their pareto dominance levels and then the diversity among solution vectors in solution space is measured. The algorithm is found to be suitable in selecting significant representitive solutions from a large number of nondominating solutions of the view selection problem.


Data warehouse View materialization Differential evolution algorithm Multiobjective optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Harinarayan, V., Rajaraman, A., Ullman, J.: Implementing data cubes efficiently. In: Proceedings of ACM SIGMOD International Conference on Management of Data, ACM SIGMOD, Montreal, Canada, pp. 205–216 (1996)Google Scholar
  2. 2.
    Gupta, H., Harinarayan, V., Rajaraman, A., Ullman, J.: Index selection for olap. In: Proceedings of the Thirteenth International Conference on Data Engineering, ICDE 1997, pp. 208–219. IEEE Computer Society, Washington, DC (1997)Google Scholar
  3. 3.
    Gupta, H., Mumick, I.S.: Selection of views to materialize under a maintenance cost constraint. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 453–470. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  4. 4.
    Nadeua, T., Teorey, T.: Achieving scalability in olap materialized view selection. In: Proceedings of ACM Fifth International Workshop on Data Warehousing and OLAP, DOLAP 2002, Virginia, USA, pp. 28–34. ACM (2002)Google Scholar
  5. 5.
    Serna-Encinas, M.T., Hoya-Montano, J.A.: Algorithm for selection of materialized views: based on a costs model. In: Proceedings of Eighth International Conference on Current Trends in Computer Science, Morella, Mexico, pp. 18–24 (September 2007)Google Scholar
  6. 6.
    Gupta, A., Mumick, I.: Maintenance of materialized views: Problems, techniques, and applications. IEEE Data Engineering Bulletin 18(2), 3–18 (1995)Google Scholar
  7. 7.
    Gupta, H.: Selection and maintenance of views in a data warehouse. Ph.d. thesis (1999)Google Scholar
  8. 8.
    Derakhshan, R., Stantic, B., Korn, O., Dehne, F.: Parallel simulated annealing for materialized view selection in data warehousing environments. In: Bourgeois, A.G., Zheng, S.Q. (eds.) ICA3PP 2008. LNCS, vol. 5022, pp. 121–132. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Loureiro, J., Belo, O.: An evolutionary approach to the selection and allocation of distributed cubes. In: Proceedings of Database Engineering and Applications Symposium, IDEAS 2006, Delhi, India, pp. 243–248. IEEE (2006)Google Scholar
  10. 10.
    Sun, X., Wang, Z.: An efficient materialized views selection algorithm based on PSO. In: Proceedings of International Workshop on Intelligent Systems and Applications 2009, Wuhan, China, pp. 1–4 (2009)Google Scholar
  11. 11.
    Qingzhou, Z., Xia, S., Ziqiang, W.: An efficient ma-based materialized views selection algorithm. In: Proceedings of the 2009 IITA International Conference on Control, Automation and Systems Engineering, Zhangjiajie, China, pp. 315–318 (2009)Google Scholar
  12. 12.
    Goswami, R., Bhattacharyya, D.K., Dutta, M.: Selection of views for materializing in data warehouse using MOSA and AMOSA. In: Wyld, D.C., Zizka, J., Nagamalai, D. (eds.) Advances in Computer Science, Engineering & Applications. AISC, vol. 166, pp. 619–628. Springer, Heidelberg (2012)Google Scholar
  13. 13.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)CrossRefGoogle Scholar
  14. 14.
    Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)CrossRefGoogle Scholar
  15. 15.
    Zhou, A., Qu, B.Y., Li, H., Zhao, S.Z., Suganthan, P.N., Zhang, Q.: Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation 1(1), 32–49 (2011)CrossRefGoogle Scholar
  16. 16.
    Sengupta, S., Das, S., Nasir, M., Suganthan, P.N.: Risk minimization in biometric sensor networks an evolutionary multi-objective optimization approach. Soft Computing 17(1), 133–144 (2013)CrossRefGoogle Scholar
  17. 17.
    Yang, J., Karlapalem, K., Li, Q.: Algorithm for materialized view design in data warehousing environment. In: Proceedings of VLDB 1997, Athens, Greece, pp. 136–145 (1997)Google Scholar
  18. 18.
    Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  19. 19.
    Gong, T., Tuson, A.: Differential evolution for binary encoding. In: 11th Online World Conference on Soft Computing in Industrial Applications, WSC11 (2006)Google Scholar
  20. 20.
    Radcliffe, N.J.: Equivalenc class analysis of genetic algorithms. Complex Systems, 183–205 (1991)Google Scholar
  21. 21.
    Radcliffe, N.J.: Forma analysis and random respectful recombination. In: Proceedings of the International Conference on Genetic Algorithms - ICGA 1991, pp. 222–229. Morgan Kaufmann, San Francisco (1991)Google Scholar
  22. 22.
    Sokal, R., Michener, C.: A statistical method for evaluating systematic relationships. University of Kansas Science Bulletin 38, 1409–1438 (1958)Google Scholar
  23. 23.
    Benson, H.P., Sayin, S.: Towards finding global representations of the efficient set in multiple objective mathematical programming. Naval Research Logistics (NRL) 44(1), 47–67 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  24. 24.
    Transaction Processing Council: TPC-H benchmark specification (2008), Published at
  25. 25.
    Lawrence, M.: Multiobjective genetic algorithms for materialized view selection in olap data warehouses. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 699–706. ACM (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Rajib Goswami
    • 1
  • Dhruba Kumar Bhattacharyya
    • 1
  • Malayananda Dutta
    • 1
  1. 1.Department of Computer Science and EngineeringTezpur UniversityTezpurIndia

Personalised recommendations