Parallel Data Cube Construction for High Performance On-Line Analytical Processing

Decision support systems use On-Line Analytical Processing (OLAP) to analyze data by posing complex queries that require different views of data. Traditionally, a relational approach (ROLAP) has been taken to build such systems. More recently, multi-dimensional database techniques (MOLAP) have been applied to decision-support applications. Data is stored in multidimensional arrays which is a natural way to express the multi-dimensionality of the enterprise and is more suited for analysis. Precomputed aggregate calculations in a Data cube can provide efficient query processing for OLAP applications. In this paper we present algorithms and results for in-memory data cube construction on distributed memory machines. 1 Introduction On-line Analytical Processing (OLAP) systems enable analysts and managers to gain insight into the performance of an enterprise through a wide variety of views of data organized to reflect the multi-dimensional nature of the enterprise [1] . OLAP gives insight..