A Hybrid Approach To Solving The View Selection Problem In Data Warehouse

Authors

  • Alaoui ME Modelling and Scientific Computing Laboratory, University Sidi Mohammed Ben Abdellah, Fez, Morocco
  • Moutaouakil KE Modelling and Scientific Computing Laboratory, University Sidi Mohammed Ben Abdellah, Fez, Morocco
  • Ettaouil M National school of applied sciences Al-Hoceima (ENSAH) BP 03, Al-Hoceima, Morocco

DOI:

https://doi.org/10.26438/ijcse/v6i9.270275

Keywords:

Data warehouse, view selection problem, constraint satisfaction and optimization problem, hybrid approach, exact method

Abstract

A data warehouse is a centralized repository of information from one or more data sources. The amount of big data that arrives in data warehouse typically comes from transactional systems and other relational databases. Often the data is stored in the form of materialized views in order to improve the performance of query execution in data warehouse. One of the most important techniques for improving query optimization performance is the selection of views to materialize. In this paper, the views selection problem is modelled as constraint satisfaction and optimization problem. The exact method standard may take a considerable amount of time in order to find an optimal solution. To address this limitation of the exact method, we proposed an approach based on consistency techniques and systematic search techniques to select an optimal set of views for materialization. This proposed approach improves the quality of execution time for selecting an optimal set of views to materialize.

References

[1] H. Gupta and I.S. Mumick, “Selection of Views to Materialize Under a Maintenance Cost Constraint”, Proc. 7th Int. Conf. Database Theory, vol. 13, pp. 453–470, 1999.

[2] H. Gupta and I.S. Mumick, “Selection of views to materialize in a data warehouse”, IEEE Trans. Knowl. Data Eng., vol. 17, no. 1, pp. 24–43, 2005.

[3] D. Yang, M. Huang, and M. Hung, “Efficient Utilization of Materialized Views in a Data Warehouse”, PAKDD 2002 Adv. Knowl. Discov. Data Min., pp. 393–404, 2002.

[4] G. Gou, J.X. Yu, and H. Lu, “A* search: An efficient and flexible approach to materialized view selection”, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 36, no. 3, pp. 411–425, 2006.

[5] T.V.V. Kumar and S. Kumar, “Materialized View Selection Using Simulated Annealing”, Int. Conf. Big Data Anal., pp. 168–179, 2012.

[6] C.S. Park, M.H. Kim, and Y.J. Lee, “Finding an efficient rewriting of OLAP queries using materialized views in data warehouses”, Decis. Support Syst., vol. 32, no. 4, pp. 379–399, 2002.

[7] J. Chang and S. Lee, “Extended conditions for answering an aggregate query using materialized views”, Inf. Process. Lett., vol. 72, pp. 205–212, 1999.

[8] I. Mami, R. Coletta, and Z. Bellahsene, “Modeling view selection as a constraint satisfaction problem”, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6861 LNCS, no. PART 2, pp. 396–410, 2011.

[9] D. Theodoratos, “Detecting redundant materialized views in data warehouse evolution”, Inf. Syst., vol. 26, no. 5, pp. 363–381, 2001.

[10] T.V.V. Kumar and S. Kumar, “Materialised view selection using differential evolution”, Int. J. Innov. Comput. Appl., vol. 6, no. 2, pp. 102–113, 2014.

[11] M. El Alaoui, K. El moutaouakil, and M. Ettaouil, “Weighted constraint satisfaction and genetic algorithm to solve the view selection problem”, International Journal of Database Management Systems (IJDMS), Vol.9, No.4, August 2017.

[12] R. Derakhshan and F. Dehne, “Simulated Annealing for Materialized View Selection in Data Warehousing Environment”, 24th IASTED Int. Conf. Database Appl., pp. 89–94, 2006.

[13] K. Aouiche and J. Darmont, “Data mining-based materialized view and index selection in data warehouses”, J. Intell. Inf. Syst., vol. 33, no. 1, pp. 65–93, 2009.

[14] K. Aouiche, P.-E. Jouve, and J. Darmont, “Clustering-Based Materialized View Selection in Data Warehouses”, Lect. Notes Comput. Sci. Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma., vol. 4152 LNCS, no. 1, pp. 81–95, 2007.

[15] A. Gosain and Heena, “Materialized Cube Selection Using Particle Swarm Optimization Algorithm” Procedia Comput. Sci., vol. 79, pp. 2–7, 2016.

[16] M. Ettaouil, “A 0-1 Quadratic Knapsack Problem for Modelizing and Solving the Constraint Satisfaction Problems”, Prog. Artif. Intell., vol. 1323, pp. 61–72, 1997.

[17] E.C. Freuder, “A Sufficient Condition for Backtrack-Free Search”, J. ACM, vol. 29, no. 1, pp. 24–32, 1982.

[18] S. Chakraborty, J. Doshi,"Deriving Aggregate Results with Incremental Data using Materialized Queries",International Journal of Computer Sciences and Engineering,Vol.-6, Issue-5, May 2018

[19] R. Barták, M.A. Salido, and F. Rossi, “Constraint satisfaction techniques in planning and scheduling”, J. Intell. Manuf., vol. 21, no. 1, pp. 5–15, 2010.

[20] K.S. Joo, T. Bose, and G.F. Xu, “Image Restoration Using a Conjugate Gradient-Based Adaptive Filtering Algorithm *”, vol. 16, no. 2, pp. 197–206, 1997.

[21] O. Lhomme, “Consistency techniques for numeric CSPs”, Ijcai, pp. 232–238, 1993.

[22] F. Manya and C. Gomes, “Solution Techniques for Constraint Satisfaction Problems”, Intel. Artif., vol. 7, no. 19, pp. 243–267, 2003.

[23] P.O. Neil, B.O. Neil, and X. Chen, “Star Schema Benchmark - Revision 3”, Tech. rep., 2009.

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Published

2018-09-30
CITATION
DOI: 10.26438/ijcse/v6i9.270275
Published: 2018-09-30

How to Cite

[1]
M. E. Alaoui, K. E. Moutaouakil, and M. Ettaouil, “A Hybrid Approach To Solving The View Selection Problem In Data Warehouse”, Int. J. Comp. Sci. Eng., vol. 6, no. 9, pp. 270–275, Sep. 2018.

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Section

Research Article