Publications of Jérôme Darmont
Reference (inproceedings)
K. Aouiche, J. Darmont, L. Gruenwald, "Frequent itemsets mining for database auto-administration", 7th International Database Engineering and Application Symposium (IDEAS 03), Hong Kong, China, July 2003, 98-103; IEEE Computer Society, Washington, DC.
Abstract
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. However, little
research has been achieved. This idea 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 useful knowledge from stored data themselves 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 experiments 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.
Keywords
Databases, Data warehouses, Auto-indexing, Data mining, Frequent itemsets
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