HQC: an efficient method for ROLAP with hierarchical dimensions

  • Authors:

  • Xing-Ye Dong;Hou-Kuan Huang;Hong-Song Li

  • Affiliations:

  • School of Computer and IT, Beijing Jiaotong University, Beijing, China;School of Computer and IT, Beijing Jiaotong University, Beijing, China;School of Computer and IT, Beijing Jiaotong University, Beijing, China

  • Venue:
  • RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
  • Year:
  • 2005

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Abstract

A useful concept called cover equivalence was proposed recently. By using this concept, the size of data cube can be reduced, and quotient cube was proposed. The scheme of ROLAP put forward in this paper is called HQC, in which a cover window is set and hierarchical dimensions are introduced. By using the concept of cover window, the size of data cube can be reduced further. E.g, for the Weather dataset, there are about 5.7M aggregated tuples in quotient table, but only about 0.18M in HQC when the cover window is 100. At the same time, the query performance can be improved. By using hierarchical dimensions, the size of HQC can be reduced without information being lost. This paper also illustrates a construction algorithm and a query algorithm for HQC. Some experimental results are presented, using both synthetic and real-world datasets. These results show that our techniques are effective.