Improved Data Partitioning for Building Large ROLAP Data Cubes in Parallel

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 an improved 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, our method can be used for highly scalable processor clusters consisting of standard PCs with local disks only, connected via a data switch. The approach taken, which uses a ROLAP representation of the data cube, is well suited for large data warehouses and high dimensional data, and supports the generation of both fully materialized and partially materialized data cubes. We have implemented our new parallel shared-nothing data cube generation method and evaluated the impact of our novel optimized data partitioning technique. The experiements show a significant performace improvement. As a result, our new optimized parallel data cube generation method achieves close to optimal speedup for as many as 32 processors, generating a full data cube for a fact table with 16 million rows and 8 attributes in under 7 minutes. For a fact table with 256 million rows and 8 attributes, our improved method reaches optimal speedup for 32 processors, generating a full data cube consisting of ≈ 7 billion rows (200 Gigabytes) in under 88 minutes. 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). Keywor ds: Data Cube, ROLAP, Parallel Computing.