Frequent itemsets mining for database auto-administration

With the wide development of databases in general and data warehouses in particular, it is important to reduce the tasks that a database administrator must perform manually. The aim of auto-administrative 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 existed for some years. How-ever, little research has been achieved. This idea neverthe-less 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 useful knowledge from stored data themselves to automatically apply performance opti-mization techniques, and more particularly indexing tech-niques. We have designed a tool that extracts frequent item-sets from a given workload to compute an index configu-ration that helps optimizing data access time. The experi-ments 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.