OLAP query processing for partitioned data warehouses

Abstract

On-line analytical processing (OLAP) queries can take hours or even days to execute on very large data warehouses. Therefore, there is a need to employ techniques that can facilitate efficient execution of these queries. The data partitioning concept that has been studied in the context of relational databases aims to reduce query execution time and facilitate the parallel execution of queries. In this paper, we develop a framework for applying the partitioning technique on DW schema (star schema) to reduce the total query execution cost. We develop an analytical cost model for executing a set of OLAP queries on a partitioned star schema. We conduct experiments to evaluate the utility of partitioning in efficiently executing OLAP queries. Finally, we show how partitioning can be used to facilitate parallel execution of OLAP queries.