Heuristic optimization of OLAP queries in multidimensionally hierarchically clustered databases

Abstract

On-Line Analytical Processing (OLAP) is a technology that encompasses applications requiring a multidimensional and hierarchical view of data. OLAP applications often require fast response time to complex grouping/aggregation queries on enormous quantities of data. Commercial relational database management systems use mainly multiple one-dimensional indexes to process OLAP queries that restrict multiple dimensions. However, in many cases, multidimensional access methods outperform one-dimensional indexing methods. We present an architecture for multidimensional databases that are clustered with respect to multiple hierarchical dimensions. It is based on the star schema and is called CSB star. Then, we focus on heuristically optimizing OLAP queries over this schema using multidimensional access methods. Users can still formulate their queries over a traditional star schema, which are then rewritten by the query processor over the CSB star. We exploit the different clustering features of the CSB star to efficiently process a class of typical OLAP queries. We detect special cases where the construction of an evaluation plan can be simplified and we discuss improvements of our technique.