Abstract
MOLAP is a important application on multidimensional data warehouse. We often execute range queries on aggregate cube computed by pre-aggregate technique in MOLAP. For the cube with d dimensions, it can generate 2d cuboids. But in a high-dimensional cube, it might not be practical to build all these cuboids. In this paper, we propose a multi-dimensional hierarchical fragmentation of the fact table based on multiple dimension attributes and their dimension hierarchical encoding. This method partition the high dimensional data cube into shell mini-cubes. The proposed data allocation and processing model also supports parallel I/O and parallel processing as well as load balancing for disks and processors. We have compared the methods of shell mini-cubes with the other existed ones such as partial cube and full cube by experiment. The results show that the algorithms of mini-cubes proposed in this paper are more efficient than the other existed ones.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data Cube: A Relational Aggregation Operator Generalizing Group-by, Cross-tab and Subtotals. Data Mining and Knowledge Discovery 1, 29–54 (1997)
Beyer, K., Ramakrishnan, R.: Bottom-up Computation of Sparse and Iceberg Cubes. In: ACM SIDMOD, pp. 359–370 (1999)
Han, J., Pei, J., Dong, G., Wang, K.: Efficient Computation of Iceberg Cubes with Complex Measures. In: ACM SIGMOD, pp. 1–12 (2001)
Xin, D., Han, J., Li, X., Wah, B.W.: Star-cubing:Computing Iceberg Cubes by Top-down and Bottom-up Integration. In: VLDB, pp. 476–487 (2003)
Wang, W., Lu, H., Feng, J., Yu, J.X.: Condensed Cube: An Effective Approach to Reducing Data Cube Size. In: ICDE, pp. 155–165 (2002)
Sismanis, Y.: Hierarchical Dwarfs for the Rollup Cube. In: VLDB, pp. 540–551 (2004)
Shanmugasundaram, J., Fayyad, U.M., Bradley, P.S.: Compressed Data Cubes for OLAP Aggregate Query Approximation on Continuous Dimensions. In: ACM SIGKDD, pp. 223–232 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, Kf., Ling, C., Jie, S., Qi, G., Tang, Xl. (2005). Computing High Dimensional MOLAP with Parallel Shell Mini-cubes. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_149
Download citation
DOI: https://doi.org/10.1007/11539506_149
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28312-6
Online ISBN: 978-3-540-31830-9
eBook Packages: Computer ScienceComputer Science (R0)