Building large ROLAP data cubes in parallel
Ying Chen, Fabian Gregor Dehne, Todd Eavis, Andrew Rau‐Chaplin · 2004
The pre-computation of data cubes is critical to improving the response time of On-Line Analytical Processing (OLAP) systems and can be instrumental in accelerating data mining tasks in large data warehouses. However, as the size of data warehouses grows, the time it takes to perform this pre-computation becomes a significant performance bottleneck. This paper presents a fast parallel method for generating ROLAP data cubes on a shared-nothing multiprocessor based on a novel optimized data partitioning technique. Since no shared disk is required, this method can be applied on highly scalable processor clusters consisting of standard PCs with local disks, connected via a data switch. The approach taken, which uses a ROLAP representation of the data cube, is well suited to large data warehouses on high dimensional data, and supports the generation of both fully materialized and partially materialized cubes. In comparison with previous approaches, our new method does significantly improve the scalability with respect to both, the number of processors and the I/O bandwidth (number of parallel disks).