A distributed maximal frequent itemset mining with multi agents system on bitmap join indexes selection

The amount of information in a data warehouse tends to be extremely large and queries may involve several complex join and aggregate operations at the same time. By using the right indices, the database administrator can speed up these OLAP queries and dramatically shorten processing times. However, selection of an optimal set of indices is a very hard task because of the exponential number of attribute candidates that can be used in the selection process. Addressing this problem, we propose a new approach with two main phases. The first involves pruning the search space to reduce the number of indices candidates. To that end, we use a distributed maximal itemsets mining approach based on a multi agent system that can significantly reduce the complexity of the selection process. We also incorporate a convertible anti-monotone constraint that contains information on the profit of index. The second phase uses also a multi agent's architecture to select final indices using a subset of attribute candidates. This final configuration will provide benefit to OLAP queries, but will also respect the disk space constraint. We validate our proposed approach using an experimental evaluation.