Abstract
We present a novel approach to speeding up the evaluation of OLAP queries that return aggregates over dimensions containing hierarchies. Our approach is based on our previous version of CubiST (Cubing with Statistics Trees), which pre-computes and stores all possible aggregate views in the leaves of a statistics tree during a one-time scan of the data. However, it uses a single statistics tree to answer all possible OLAP queries. Our new version remedies this limitation by materializing a family of derived trees from the single statistics tree. Given an input query, our new query evaluation algorithm selects the smallest tree in the family which can provide the answer. Our experiments have shown drastic reductions in processing times compared with the original CubiST as well as existing ROLAP and MOLAP systems.
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.
Similar content being viewed by others
References
Arbor Systems, “Large-Scale Data Warehousing Using Hyperion Essbase OLAP Technology,” Arbor Systems, White Paper, http://www.hyperion.com/whitepapers.cfm
S. Chaudhuri and U. Dayal, “An Overview of Data Warehousing and OLAP Technology,” SIGMOD Record, 26:1, pp. 65–74, 1997
L. Fu and J. Hammer, “CubiST: A New Algorithm for Improving the Performance of Adhoc OLAP Queries,” Proceedings of the ACM Third International Workshop on Data Warehousing and OLAP (DOLAP), Washington, DC, pp. 72–79, 2000
J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh, “Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals,” Data Mining and Knowledge Discovery, 1:1, pp. 29–53, 1997
A. Gupta, V. Harinarayan, and D. Quass, “Aggregate-query Processing in Data Warehousing Environments,” Proceedings of the Eighth International Conference on Very Large Databases, Zurich, Switzerland, pp. 358–369, 1995
H. Gupta and I. Mumick, “Selection of Views to Materialize Under a Maintenance Cost Constraint,” Stanford University, Technical Report
J. Hammer and L. Fu, “Speeding Up Data Cube Queries with Statistics Trees,” University of Florida, Gainesville, FL, Research report TR01-007, January 2001
W. Labio, D. Quass, and B. Adelberg, “Physical Database Design for Data Warehouses,” in Proceedings of the International Conference on Database Engineering, Birmingham, England, pp. 277–288, 1997
M. Lee and J. Hammer, “Speeding Up Warehouse Physical Design Using A Randomized Algorithm,” Proceedings of the International Workshop on Design and Management of Data Warehouses (DMDW’ 99), Heidelberg, Germany, 1999
D. Lomet, ed. Bulletin of the Technical Committee on Data Engineering. Special Issue on Materialized Views and Data Warehousing, ed. J. Widom. 18, IEEE Computer Society, 1995
MicroStrategy Inc., “The Case For Relational OLAP,” MicroStrategy, White Paper, http://www.microstrategy.com/publications/whitepapers/Case4Rolap/
P. O’Neil and D. Quass, “Improved Query Performance with Variant Indexes,” SIGMOD Record, 26:2, pp. 38–49, 1997
14. Oracle Corp., “Oracle Express OLAP Technology,” http://www.oracle.com/olap/
Redbrick Systems, “Informix Redbrick Decision Server,” Redbrick, Los Gatos, CA, Product Overview and Data Sheet, http://www.informix.com/redbrick/
W.P. Yan and P. Larson, “Eager Aggregation and Lazy Aggregation,” Proceedings of the Eighth International Conference on Very Large Databases, Zurich, Switzerland, pp. 345–357, 1995
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hammer, J., Fu, L. (2001). Improving the Performance of OLAP Queries Using Families of Statistics Trees. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2001. Lecture Notes in Computer Science, vol 2114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44801-2_27
Download citation
DOI: https://doi.org/10.1007/3-540-44801-2_27
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42553-3
Online ISBN: 978-3-540-44801-3
eBook Packages: Springer Book Archive