Data cube has been playing an essential role in OLAP (online analytical processing). The pre-computation of data cubes is critical for improving the response time of OLAP systems. However, as the size of data cube grows, the time it takes to perform this pre-computation becomes a significant performance bottleneck. In a high dimensional OLAP, it might not be practical to build all these cuboids and their indices. In this paper, we propose a parallel hierarchical cubing algorithm, based on an extension of the previous minimal cubing approach. The algorithm has two components: decomposition of the cube space based on multiple dimension attributes, and an efficient OLAP query engine based on a prefix bitmap encoding of the indices. This method partitions the high dimensional data cube into low dimensional cube segments. Such an approach permits a significant reduction of CPU and I/O overhead for many queries by restricting the number of cube segments to be processed for both the fact table and bitmap indices. The proposed data allocation and processing model support parallel I/O and parallel processing, as well as load balancing for disks and processors. Experimental results show that the proposed parallel hierarchical cubing method is significantly more efficient than other existing cubing methods.
['Kongfa Hu', 'Ling Chen', 'Yixin Chen']
PHC: a rapid parallel hierarchical cubing algorithm on high dimensional OLAP