Abstract
Materialized view selection is a non-trivial task. Hence, its complexity must be reduced. A judicious choice of views must be cost-driven and influenced by the workload experienced by the system. In this paper, we propose a framework for materialized view selection that exploits a data mining technique (clustering), in order to determine clusters of similar queries. We also propose a view merging algorithm that builds a set of candidate views, as well as a greedy process for selecting a set of views to materialize. This selection is based on cost models that evaluate the cost of accessing data using views and the cost of storing these views. To validate our strategy, we executed a workload of decision-support queries on a test data warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when storage space is limited.
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References
Agrawal, S., Chaudhuri, S., Narasayya, V.R.: Automated selection of materialized views and indexes in SQL databases. In: 26th International Conference on Very Large Data Bases (VLDB 2000), Cairo, Egypt, pp. 496–505 (2000)
Baralis, E., Paraboschi, S., Teniente, E.: Materialized views selection in a multidimensional database. In: 23rd International Conference on Very Large Data Bases (VLDB 1997), Athens, Greece, pp. 156–165 (1997)
Baril, X., Bellahsene, Z.: Selection of materialized views: a cost-based approach. In: Eder, J., Missikoff, M. (eds.) CAiSE 2003. LNCS, vol. 2681, pp. 665–680. Springer, Heidelberg (2003)
Cardenas, A.F.: Analysis and performance of inverted data base structures. Communication in ACM 18(5), 253–263 (1975)
Chan, G.K.Y., Li, Q., Feng, L.: Design and selection of materialized views in a data warehousing environment: A case study. In: 2nd ACM international workshop on Data warehousing and OLAP (DOLAP 1999), Kansas City, USA, pp. 42–47 (1999)
Goldstein, J., Larson, P.: Optimizing queries using materialized views: A practical, scalable solution. In: ACM SIGMOD international conference on Management of data (SIGMOD 2001), Santa Barbara, USA, pp. 331–342 (2001)
Golfarelli, M., Rizzi, S.: A methodological framework for data warehouse design. In: 1st ACM international workshop on Data warehousing and OLAP (DOLAP 1998), New York, USA, pp. 3–9 (1998)
Gupta, H.: Selection of views to materialize in a data warehouse. In: Afrati, F.N., Kolaitis, P.G. (eds.) ICDT 1997. LNCS, vol. 1186, pp. 98–112. Springer, Heidelberg (1996)
Gupta, H., Mumick, I.S.: Selection of views to materialize in a data warehouse. IEEE Transactions on Knowledge and Data Engineering 17(1), 24–43 (2005)
Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing data cubes efficiently. In: ACM SIGMOD International Conference on Management of data (SIGMOD 1996), Montreal, Canada, pp. 205–216 (1996)
Jouve, P., Nicoloyannis, N.: KEROUAC: An algorithm for clustering categorical data sets with practical advantages. In: International Workshop on Data Mining for Actionable Knowledge (DMAK’2003, in conjunction with PAKDD 2003) (2003)
Jouve, P., Nicoloyannis, N.: A new method for combining partitions, applications for distributed clustering. In: International Workshop on Paralell and Distributed Machine Learning and Data Mining (ECML/PKDD 2003), pp. 35–46 (2003)
Kotidis, Y., Roussopoulos, N.: DynaMat: A dynamic view management system for data warehouses. In: ACM SIGMOD International Conference on Management of Data (SIGMOD 1999), Philadelphia, USA, pp. 371–382 (1999)
Nadeau, T.P., Teorey, T.J.: Achieving scalability in OLAP materialized view selection. In: 5th ACM International Workshop on Data Warehousing and OLAP (DOLAP 2002), McLean, USA (2002)
Rizzi, S., Saltarelli, E.: View materialization vs. indexing: Balancing space constraints in data warehouse design. In: Eder, J., Missikoff, M. (eds.) CAiSE 2003. LNCS, vol. 2681, pp. 502–519. Springer, Heidelberg (2003)
Shukla, A., Deshpande, P.M., Naughton, J.F.: Materialized view selection for multi-cube data models. In: Zaniolo, C., Grust, T., Scholl, M.H., Lockemann, P.C. (eds.) EDBT 2000. LNCS, vol. 1777, pp. 269–284. Springer, Heidelberg (2000)
Sismanis, Y., Deligiannakis, A., Roussopoulos, N., Kotidis, Y.: Dwarf: shrinking the petacube. In: ACM SIGMOD International Conference on Management of Data (SIGMOD 2002), Madison, USA, pp. 464–475 (2002)
Smith, J.R., Li, C.-S., Jhingran, A.: A wavelet framework for adapting data cube views for OLAP. IEEE Transactions on Knowledge and Data Engineering 16(5), 552–565 (2004)
Theodoratos, D., Xu, W.: Constructing search spaces for materialized view selection. In: 7th ACM international workshop on Data warehousing and OLAP (DOLAP 2004), Washington, USA (2004)
Transaction Processing Council. TPC Benchmark R Standard Specification (1999)
Uchiyama, H., Runapongsa, K., Teorey, T.J.: A progressive view materialization algorithm. In: 2nd ACM International Workshop on Data warehousing and OLAP (DOLAP 1999), Kansas City, USA, pp. 36–41 (1999)
Valluri, S.R., Vadapalli, S., Karlapalem, K.: View relevance driven materialized view selection in data warehousing environment. In: 30th Australasian conference on Database technologies, Melbourne, Australia, pp. 187–196 (2002)
Yao, S.B.: Approximating block accesses in database organizations. Communication in ACM 20(4), 260–261 (1977)
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Aouiche, K., Jouve, PE., Darmont, J. (2006). Clustering-Based Materialized View Selection in Data Warehouses. In: Manolopoulos, Y., Pokorný, J., Sellis, T.K. (eds) Advances in Databases and Information Systems. ADBIS 2006. Lecture Notes in Computer Science, vol 4152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11827252_9
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DOI: https://doi.org/10.1007/11827252_9
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