Improving the Performance of OLAP Queries Using Families of Statistics Trees

  • Joachim Hammer
  • Lixin Fu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)


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.


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  1. 1.
    Arbor Systems, “Large-Scale Data Warehousing Using Hyperion Essbase OLAP Technology,” Arbor Systems, White Paper,
  2. 2.
    S. Chaudhuri and U. Dayal, “An Overview of Data Warehousing and OLAP Technology,” SIGMOD Record, 26:1, pp. 65–74, 1997CrossRefGoogle Scholar
  3. 3.
    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, 2000Google Scholar
  4. 4.
    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, 1997CrossRefGoogle Scholar
  5. 5.
    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, 1995Google Scholar
  6. 6.
    H. Gupta and I. Mumick, “Selection of Views to Materialize Under a Maintenance Cost Constraint,” Stanford University, Technical ReportGoogle Scholar
  7. 7.
    J. Hammer and L. Fu, “Speeding Up Data Cube Queries with Statistics Trees,” University of Florida, Gainesville, FL, Research report TR01-007, January 2001Google Scholar
  8. 8.
    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, 1997Google Scholar
  9. 9.
    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, 1999Google Scholar
  10. 10.
    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, 1995Google Scholar
  11. 11.
    MicroStrategy Inc., “The Case For Relational OLAP,” MicroStrategy, White Paper,
  12. 13.
    P. O’Neil and D. Quass, “Improved Query Performance with Variant Indexes,” SIGMOD Record, 26:2, pp. 38–49, 1997CrossRefGoogle Scholar
  13. 14. Oracle Corp., “Oracle Express OLAP Technology,”
  14. 15.
    Redbrick Systems, “Informix Redbrick Decision Server,” Redbrick, Los Gatos, CA, Product Overview and Data Sheet,
  15. 16.
    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, 1995Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Joachim Hammer
    • 1
  • Lixin Fu
    • 1
  1. 1.Dept. of CISEUniversity of FloridaFloridaU.S.A.

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