L'interaction au service de l'optimisation à grande échelle des entrepôts de données relationnels

The database technology is an adequate environment for the interaction. It may concern severalcomponents of the DBMS: (a) the data, (b) the queries, (c) the optimization techniques and (d) the devices. Atthe data level, correlations between attributes are extremely common in the real world relational data, and havebeen exploited to define materialized views and indexes. At the query level, interaction has been massivelystudied under the problem of multi-query optimization. The data warehouses with their star join queriesincrease the rate of the interaction. The query interaction has been used for selecting optimization techniquessuch as indexes. The interaction also contributes in selecting multiple optimization techniques such asmaterialized views, indexes, data partitioning and the clustering. In existing studies, the interaction concernsonly one component. In this thesis, we consider the multi-component interaction, with three optimizationtechniques, where each one concerns one component: the query scheduling (query level), the horizontal datapartitioning (data level) and the buffer management (device level). The query scheduling (QS) consists indefining an optimal order of executing queries to allow some queries to get benefit from already processed data.The horizontal data partitioning (HDP) divides the instances of each relation into disjoint subsets. The buffermanagement (BM) consists in allocating and replacing data in the buffer pool to lower the cost of queries.Usually, these problems are treated either in isolation or pairwise such as BM and QS. However, these problemsare similar and complementary. A deep formalization for off-line and online scenario of these problems is givenand advanced algorithms inspired from natural bees behavior are proposed. Our proposal has been validatedusing a simulator and real DBMS (Oracle) using a large scale of star schema benchmark.