Experimental evaluation of a new distributed partitioning technique for data warehouses

Since data warehousing has become a major field of research there has been a lot of interest in reducing the response time of complex queries posed over the very large databases. The problem is that data warehouses store large amounts of data for decision support, requiring a high level of query performance and scalability to the database engines. A novel round-robin data partitioning approach especially designed for relational data warehouse environments is proposed and experimentally evaluated. This approach is specific to data warehouses implemented over relational repositories using the star schema, as it takes advantage of the specific characteristics of star schemas and typical data warehouse query profiles. The proposed approach guarantees optimal load balancing of query execution and assures high scalability. The experimental evaluation presented in the paper, using a comprehensive set of typical queries from the APB-I benchmark running over Oracle 8, shows that an optimal speedup can be obtained with this technique. The proposed technique constitutes an effective and practical way of coping with very large data warehouses and can be applied to existing database technology