Abstract
For a data cube there are always constraints between dimensions or between attributes in a dimension, such as functional dependencies. We introduce the problem that when there are functional dependencies, how to use them to speed up the computation of sparse data cubes. A new algorithm CFD is presented to satisfy this demand. CFD determines the order of dimensions by considering their cardinalities and functional dependencies between them together. It makes dimensions with functional dependencies adjacent and their codes satisfy monotonic mapping, thus reduces the number of partitions for such dimensions. It also combines partitioning from bottom to up and aggregate computation from top to bottom to speed up the computation further. In addition CFD can efficiently compute a data cube with hierarchies from the smallest granularity to the coarsest one, and at most one attribute in a dimension takes part in the computation each time. The experiments have shown that the performance of CFD has a significant improvement.
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References
Baeyer, K., Ramakrishnan, R.: Bottom-Up Computation of Sparse and Iceberg CUBEs. In: SIGMOD 1999, pp. 359–370 (1999)
Ross, K.A., Strivastava, D.: Fast computation of sparse data cubes. In: Proc. Of the 23rd VLDB Conf., Athens, Green, pp. 116–125 (1997)
Agarwal, S., Agrawal, R., Desgpande, P.M., Gupta, A., Naughton, J.F., Ramakrishnan, R., Sarawagi, S.: On the computation of multidimensional aggregates. In: Proc. Of the 22nd VLDB Conf., pp. 506–521 (1996)
Niemi, T., Nummenmaa, J., Thanisch, P.: Constructing OLAP Cubes Based on Queries. In: DOLAP 2001, pp. 1–8 (2001)
Gray, J., Bosworth, A., Layman, A., Pirahesh, H.: Datacube: A relational aggregation operator generalizing group by, cross-tab, and sub-totals. In: ICDE 1996, pp. 152–159 (1996)
Zhao, Y., Desgpande, P.M., Naughton, J.F.: An array-based algorithm for simultaneous mutldimensional aggregates. In: SIGMOD 1997, pp. 159–170 (1997)
Wang, W., Feng, J., Lu, H., Yu, J.X.: Condensed Cube: An Effective Approach to Reducing Data Cube Size. In: Proc. of the 18th Int. Conf. on Data Engineering, pp. 155–165 (2002)
Sismanis, Y., Deligiannakis, A., Roussopoulos, N., Kotidis, Y.: Dwarf: Shrinking the PetaCube. In: SIGMOD 2002 (2002)
Han, J., Pei, J., Dong, G., Wang, K.: Efficient Computation of Iceberg Cubes with Complex Measures. In: SIGMOD 2001 (2001)
Lakshmanan, L., Pei, J., Han, J.: Quotient Cube: How to Summarize the Semantics of a Data CubeFast. In: Proc. the 28rd VLDB Conference, HongKong, China (2002)
Lenher, W., Albrecht, J., Wedekind, H.: Normal forms for multidimensional databases. In: Rafanelli, M., Svensson, P., Klensin, J.C. (eds.) SSDBM 1988. LNCS, vol. 339, pp. 63–72. Springer, Heidelberg (1989)
Hahn, C., Warren, S., London, J.: Edited synoptic cloud reports from ships and land stations over the globe (1982-1991) (1994), http://cdiac.esd.ornl.gov/-cdiac/ndps/ndp026b.html , http://cdiac.esd.ornl.gov/-ftp/ndp026b/SEP85L.Z
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Chen, C., Feng, J., Xiang, L. (2003). Computation of Sparse Data Cubes with Constraints. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2003. Lecture Notes in Computer Science, vol 2737. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45228-7_3
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DOI: https://doi.org/10.1007/978-3-540-45228-7_3
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