Decision support systems use online 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 multi-dimensional 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.
['Sanjay Goil', 'Alok N. Choudhary']
Parallel data cube construction for high performance on-line analytical processing