Automatic Selection of Bitmap Join Indexes in Data Warehouses

Kamel Aouiche (1) , Jérôme Darmont (1) , Omar Boussaïd (1) , Fadila Bentayeb (1)

1 ERIC - Equipe de Recherche en Ingénierie des Connaissances

The queries defined on data warehouses are complex and use several join operations that induce an expensive computational cost. This cost becomes even more prohibitive when queries access very large volumes of data. To improve response time, data warehouse administrators generally use indexing techniques such as star join indexes or bitmap join indexes. This task is nevertheless complex and fastidious. Our solution lies in the field of data warehouse auto-administration. In this framework, we propose an automatic index selection strategy. We exploit a data mining technique ; more precisely frequent itemset mining, in order to determine a set of candidate indexes from a given workload. Then, we propose several cost models allowing to create an index configuration composed by the indexes providing the best profit. These models evaluate the cost of accessing data using bitmap join indexes, and the cost of updating and storing these indexes.

Mots clés: Data warehouses; auto-administration; index selection; frequent itemsets; cost models; bitmap join indexes

Domaines: Base de données [cs.DB]

Kamel Aouiche, Jérôme Darmont, Omar Boussaïd, Fadila Bentayeb. Automatic Selection of Bitmap Join Indexes in Data Warehouses. 7th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 05), Aug 2005, Copenhagen, Denmark. pp.64-73. ⟨hal-00145458⟩