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.
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References
Gray, J., et al.: Data cube: A relational aggregation operator generalizing group-by, cross-tab and sub-total. In: ICDE 1996, pp. 152–159 (1996)
Agarwal, S., Agrawal, R., Deshpande, P., Gupta, A., Naughton, J.F., Ramakrishnan, R., Sarawagi, S.: On the computation of multidimensional aggregates. In: VLDB 1996, pp. 506–521 (1996)
Zhao, Y., Deshpande, P., Naughton, J.F.: An array-based algorithm for simultaneous multidimensional aggregates. In: SIGMOD 1997, pp. 159–170 (1997)
Ross, K.A., Srivastava, D.: Fast computation of sparse datacubes. In: VLDB 1997, pp. 116–185 (1997)
Beyer, K.S., Ramakrishnan, R.: Bottom-up computation of sparse and iceberg cubes. In: SIGMOD 1999, pp. 359–370 (1999)
Wang, W., Feng, J., Lu, H., Xu Yu, J.: Condensed Cube: An Effective Approach to Reducing Data Cube Size. In: ICDE 2002 (2002)
Sismanis, Y., Deligiannakis, A.: Dwarf: Shrinking the PetaCube. In: ACM SIGMOD 2002, pp. 464–475 (2002)
Sathe, G., Sarawagi, S.: Intelligent Rollups in Multidimensional OLAP Data. In: VLDB 2001, pp. 531–540 (2001)
Imielinski, T., et al.: Cubegrades: Generalizing Association Rules. Tec. Rep., Rutgers U (August 2000)
Lakshmanan, L.V.S., Pei, J., Han, J.: Quotient Cube: How to Summarize the Semantics of a Data Cube. In: Proceedings of the 28th VLDB Conference, Hong Kong, China (2002)
Lakshmanan, L.V.S., Pei, J., Zhao, Y.: QC-Trees: An Efficient Summary Structure for Semantic OLAP. In: SIGMOD 2003, pp. 64–75 (2003)
Li, S.-E., Wang, S.: Research on closed data cube technology. Journal of Software 15(8), 1165–1171 (2004) (in Chinese with English abstract)
Hahn, C., Warren, S., London, J.: Edited synoptic cloud reports from ships and land stations over the globe (1996), http://cdiac.esd.ornl.gov/cdiac/ndps/ndp026b.html
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© 2005 Springer-Verlag Berlin Heidelberg
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Dong, XY., Huang, HK., Li, HS. (2005). HQC: An Efficient Method for ROLAP with Hierarchical Dimensions. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W., Hu, X. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2005. Lecture Notes in Computer Science(), vol 3642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11548706_23
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DOI: https://doi.org/10.1007/11548706_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28660-8
Online ISBN: 978-3-540-31824-8
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