Horizontal partitioning of very-large data warehouses under dynamically-changing query workloads via incremental algorithms

With the explosion of the size of data warehousing applications, the horizontal data partitioning is well adapted to reduce the cost of complex OLAP queries and the warehouse manageability. It is considered as a non redundant optimization technique. Selecting a fragmentation schema for a given data warehouse is NP-hard problem. Several studies exist and propose heuristics to select near optimal solutions. Most of these heuristics are static, since they assume the existence of a priori known set of queries. Note that in real life applications, queries may change dynamically and fragmentation heuristics need to integrate these changes. In this paper, we propose an incremental selection of fragmentation schemes using on genetic algorithms. Intensive experiments are conducted to validate our proposal.