A high performance hierarchical cubing algorithm and efficient OLAP in high-dimensional data warehouse

Authors:
Kongfa Hu;Zhenzhi Gong;Qingli Da;Ling Chen
Affiliations:
Department of Computer Science and Engineering, Yangzhou University, China and School of Economics & Management, Southeast University, China;School of Economics & Management, Southeast University, China;School of Economics & Management, Southeast University, China;Department of Computer Science and Engineering, Yangzhou University, China
Venue:
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
Year:
2007

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Abstract

Data cube has been playing an essential role in fast OLAP (online analytical processing) in many data warehouses. The pre-computation of data cubes is critical for improving the OLAP response time of in large high-dimensional data warehouses. However, as the sizes of data warehouses grow, the time it takes to perform this pre-computation becomes a significant performance bottleneck. In a high dimensional data warehouse, it might not be practical to build all these cuboids and their indices. In this paper, we propose a hierarchical cubing algorithm to partition the high dimensional data cube into low dimensional cube segments. It 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. Experimental results show that the proposed method is significantly more efficient than other existing cubing methods.