Complex Data Warehousing and Knowledge Discovery for by Tho Manh Nguyen

By Tho Manh Nguyen

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Dwarf: Shrinking the PetaCube. In Proceedings of the 2002 ACM SIGMOD Conference (pp. 464-475). , & Quass, D. (1997). Improved query performance with variant indexes. SIGMOD Record, 26(2), 38–49. , & Winter, M. (1995). Window query-optimal clustering of spatial objects. In Proceedings of the fourteenth ACM SIGACTSIGMOD-SIGART symposium on Principles of database systems (pp. 86-94). Peano, G. (1890). Sur une courbe, qui remplit toute une aire plane. Mathematische Annalen, 36(1), 157–160. , Haritsa, J.

The implementation and performance of compressed databases. SIGMOD Record, 29(3), 55–67. , & Buchmann, B. (1998). Encoded bitmap indexing for data warehouses. In Proceedings of the Fourteenth International Conference on Data Engineering (pp. 220-230). , & Lempel, A. (1977). A universal algorithm for sequential data compression. IEEE Transactions on Information Theory, 23(3), 337–343. 1055714 27 28 Chapter 2 Dynamic Workload for Schema Evolution in Data Warehouses A Performance Issue Fadila Bentayeb University of Lyon – ERIC Laboratory, France Cécile Favre University of Lyon – ERIC Laboratory, France Omar Boussaid University of Lyon – ERIC Laboratory, France ABStrAct A data warehouse allows the integration of heterogeneous data sources for identified analysis purposes.

1999b) dimensions are modeled as an acyclic graph, where nodes represent dimension attributes and arcs represent hierarchical links between these attributes. Evolution operators are then defined under the form of algebraic functions, which are able to modify the graph structure. Blaschka, M. (1999) and Blaschka, M. et al. (1999) propose elementary operators for schema evolution, which can be combined to define more complex operators. We note that these two methods support only one schema and do not keep the history of evolutions.

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