Towards the building of a dense-region-based OLAP system

Abstract

On-line analytical processing (OLAP) has become a very useful tool in decision support systems built on data warehouses. Relational OLAP (ROLAP) and multidimensional OLAP (MOLAP) are two popular approaches for building OLAP systems. These two approaches have very different performance characteristics: MOLAP has good query performance but bad space efficiency, while ROLAP can be built on mature RDBMS technology but it needs sizable indices to support it. Many data warehouses contain many small clustered multidimensional data ( dense regions), with sparse points scattered around in the rest of the space. For these databases, we propose that the dense regions be located and separated from the sparse points. The dense regions can subsequently be represented by small MOLAPs, while the sparse points are put in a ROLAP table. Thus the MOLAP and ROLAP approaches can be integrated in one structure to build a high performance and space efficient dense-region-based data cube. In this paper, we define the dense region location problem as an optimization problem and develop a chunk scanning algorithm to compute dense regions. We prove a lower bound on the accuracy of the dense regions computed. Also, we analyze the sensitivity of the accuracy on user inputs. Finally, extensive experiments are performed to study the efficiency and accuracy of the proposed algorithm.