Extraction de motifs fréquents pour l'auto-administration des bases de données

With the development of databases in general and data warehouses in particular, it is now very important to reduce the database administration function. The aim of autoadministrative systems is to administrate and adapt themselves automatically, without loss (or even with a gain) in performance. The idea of using data mining techniques to extract useful knowledge for administration from the data themselves has been in the air for some years. However, no research has ever been achieved as far as we know. This nevertheless remains a very promising approach, notably in the field of data warehousing, where queries are very heterogeneous and cannot be interpreted easily. The aim of this study is to search for a way of extracting from stored data themselves useful knowledge to automatically apply performance optimization techniques, and more particularly indexing techniques. We have designed a tool that extracts frequent itemsets from a given workload to compute an index configuration that helps optimizing data access time. The experimentations we performed showed that the index configurations generated by our tool allowed performance gains of 15 to 25% on a test database and a test data warehouse. MOTS-CLÉS : Bases de données, Entrepôts de données, Indexation automatique, Fouille de données, Motifs fréquents.