A novel algorithm with IM-LSI index for incremental maintenance of materialized view


  • K. Rangarajan Dept of CSE, Bharath University, India
  • A. Kumaravel Dept of CSE, Bharath University, India
  • T. Nalini Dept of CSE, Bharath University, India


I-mine item set index, FP growth, LSI index, Materialization view, Data warehousing, Selection cost


The ability to afford decision makers with both accurate and timely consolidated information as well as rapid query response times is the fundamental requirement for the success of a Data Warehouse. To provide fast access, a data warehouse stores materialized views of the sources of its data. As a result, a data warehouse needs to be maintained to keep its contents consistent with the contents of its data sources. Incremental maintenance is generally regarded as a more efficient way to maintain materialized views in a data warehouse The view has to be maintained to reflect the updates done against the base relations stored at the various distributed data sources. The proposed approach contains two modules namely, materialized view selection(MVS) and maintenance of materialized view. (MMV). In recent times, several algorithms have been proposed for keeping the views up-to-date in response to the changes in the source data. Therefore, we present an improved algorithm for MVS and MMV using IM-LSI(Itemset Mining using Latent Semantic Index) algorithm. selection of views to materialize using the IM(Itemset Mining) algorithm method to overcome the problem resulting from conventional view selection algorithms and then we consider the maintenance of materialized views using LSI. For the justification of the proposed algorithm, we reveal the experimental results in which both time and space costs better than conventional algorithms.


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[1] Y. Zhuge, H. Garcia-Molina, J. Hammer, and J. Widom, "View Maintenance in a Warehousing Environment." In Proceedings of the ACM SIGMOD Conference, San Jose, California, May 1995.
[2] C. Zhang, X. Yao, and J. Yang. An evolutionary Approach to Materialized View Selection in a Data Warehouse Environment. IEEE Transactions on Systems, Man and Cybernetics, vol. 31, no.3, pp. 282-293, 2001.
[3] H. Gupta, I.S. Mumick,” Selection of views to materialize under a maintenance cost constraint”, In Proc. 7 th International Conference on Database Theory (ICDT'99), Jerusalem, Israel, pp. 453-470, 1999.
[4] V. Harinarayan, A. Rajaraman, and J. Ullman. “Implementing data cubes efficiently”. Proceedings of ACM SIGMOD 1996 International Conference on Management of Data, Montreal, Canada, pages 205--216, 1996.
[5] 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.
[6] S. Agrawal, S. Chaudhuri, and V. Narasayya, “Automated Selection of Materialized Views and Indexes in SQL Databases,” Proceedings of International Conference on Very Large Database Systems, 2000.
[7] P. Kalnis, N. Mamoulis, and D. Papadias, “View Selection Using Randomized Search,” Data and Knowledge Eng., vol. 42, no. 1, 2002.
[8] Gupta, H. & Mumick, I., Selection of Views to Materialize in a Data Warehouse. IEEE Transactions on Knowledge and Data Engineering, 17(1), 24-43, 2005.
[9] Elena Baralis, Tania Cerquitelli, and Silvia Chiusano,” I-Mine: Index Support for Item Set Mining” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 4, april 2009
[10] B.Ashadevi, R.Balasubramanian,” Cost Effective Approach for Materialized Views Selection in Data Warehousing Environment”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.10, October 2008
[11]T.Nalini,Dr.A.Kumaravel,Dr.K.Rangarajan ,” An Efficient I-Mine Algorithm For Materialized Views In A Data Warehouse Environment”, Ijcsi International Journal Of Computer Science Issues, Vol. 8, Issue 5, No 1, September 2011 Issn (Online): 1694-0814
[12] M. Lee and J. Hammer, Speeding up materialized view selection in data warehouses using a randomized algorithm, International Journal of Cooperative Information Systems, 10(3): 327–353, 2001.
[13] Gang Gou; Yu, J.X.; Hongjun Lu., "A* search: an efficient and flexible approach to materialized view selection Systems," IEEE Transactions on Man, and Cybernetics, Part C: applications and Reviews, Vol. 36, no. 3, May 2006 pp: 411 -425.
[14] 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.
[15] T.Nalini, S.K.Srivatsa ,K.Rangarajan” Method of anking in indexes on materialized view for database workload” , International Journal of Advanced Research in Computer Engineering(IJARCE), vol.4, No.1,pp 157-162
[16] T.Nalini,S.K.Srivatsa,K.Rangarajan,” International journal of computer science, systems engineering and information technology(IJCSSEIT),” Efficient methods for selecting materialized views in a data warehouse”Vol.3,No.2, pp 305-310
[17] R. Agrawal and R. Srikant, “Fast Algorithm for Mining
Association Rules,” Proc. 20th Int’l Conf. Very Large Data
Bases (VLDB ’94), Sept. 1994.
[18]A. Savasere, E. Omiecinski, and S.B. Navathe, “An Efficient Algorithm for Mining Association Rules in Large Databases,” Proc. 21st Int’l Conf. Very Large Data Bases (VLDB ’95), pp. 432-444, 1995.
[19] M. El-Hajj and O.R. Zaiane, “Inverted Matrix: Efficient Discovery of Frequent Items in Large Datasets in the Context of Interactive Mining,” Proc. Ninth ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (SIGKDD), 2003.
[20] Frequent Pattern Growth (FP-Growth) Algorithm An Introduction,Florian Verhein ,January 2008
[21] Jian Yang, Kamalakar Karlapalem, Qing Li, "Algorithms for Materialized View Design in Data Warehousing Environment", in Proceedingof the 23rd International Conference on Very Large Data Bases, San Francisco, CA, 1997.
[22] Yousri, N.A.R., Ahmed, K.M., El-Makky, N.M., "Algorithms for selecting materialized views in a data warehouse", in proceedings of 3rd ACS/IEEE International Conference on Computer Systems and Applications, 2005.




How to Cite

Rangarajan, K., Kumaravel, A., & Nalini, T. (2012). A novel algorithm with IM-LSI index for incremental maintenance of materialized view. Journal of Computer Science and Technology, 12(01), p. 32–38. Retrieved from https://journal.info.unlp.edu.ar/JCST/article/view/665



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