Abstract
The semantic relations among cells in data cube are more important for efficient query and OLAP. Normally the size of a data cube is very huge and relations among cells are very complicated so the semantic data cube is difficult to be realized. Based on quotient cube, Semantic Data Cube (SDC) structure is put forward in this paper. In SDC the lattice of cells is expressed as tree-hierarchy structure and each cell in lattice is replaced with its upper bound. The SDC depicts the lattice of cells concisely and preserves all the semantic relations among cells. Applying semantics to query answering and maintaining incrementally in SDC, the time of response and the cost of updating can be reduced greatly. Algorithms of constructing SDC, answering a query and maintaining incrementally in SDC are given. The experimental results show that the SDC is effective.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Gray, J., Bosworth, A., Layman, A., Pirahesh, H.: Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. In: ICDE, pp. 152–159 (1996)
Agarwal, S., Agrawal, R., Deshpande, P.M., Gupta, A., Naughton, J.F., Ramarkrishman, R., Sarawagi, S.: On the computation of multidimensional aggregates. In: ICDE, pp. 506–521 (1996)
Ross, K.A., Srivastava, D.: Fast computation of sparse data cubes. In: ICDE, pp. 116–125 (1997)
Zhao, Y., Deshpande, P.M., Naughton, J.F.: An array-based algorithm for simultaneous multidimensional. In: SIGMOD, pp. 159–170 (1997)
Beyer, K., Ramakrishnan, R.: Bottom-up computation of sparse and iceberg cube. In: SIGMOD, pp. 359–370 (1999)
Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing data cubes efficiently. In: SIGMOD, pp. 205–216 (1996)
Shukla, A., Deshpande, P.M., Naughton, J.F.: Materialized view selection for multidimensional datasets. In: VLDB, pp. 488–499 (1998)
Wang, W., Lu, H.J., Feng, J.L., Yu, J.X.: Condensed cube: An effective approach to reducing data cube size. In: ICDE, pp. 155–165 (2002)
Sismanis, Y., Deligiannakis, A., Roussopoulos, N., Kotidis, Y.D.: Shrinking the PetaCube. In: ACM SIGMOD, pp. 464–475. ACM Press, New York (2002)
Lakshmanan, L.V.S., Pei, J., Han, J.W.: Quotient cube: How to summarize the simantics of a data cube. In: VLDB, pp. 778–789 (2002)
Lakshmanan, L.V.S., Pei, J., Zhao, Y.: QC-Trees: An efficient summary structure for semantic OLAP. In: ACM SIGMOD, pp. 64–75. ACM Press, New York (2003)
Hahn, C., et al.: Edited synoptic cloud reports from ships and land stations over the globe, 1982-1991 (1994), cdiac.est.ornl.gov/ftp/ndp026b/SEP85L.Z
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shi, Z., Huang, H. (2007). A Summary Structure of Data Cube Preserving Semantics. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_55
Download citation
DOI: https://doi.org/10.1007/978-3-540-73451-2_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73450-5
Online ISBN: 978-3-540-73451-2
eBook Packages: Computer ScienceComputer Science (R0)