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Improving the Performance of OLAP Queries Using Families of Statistics Trees

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Data Warehousing and Knowledge Discovery (DaWaK 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2114))

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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.

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References

  1. Arbor Systems, “Large-Scale Data Warehousing Using Hyperion Essbase OLAP Technology,” Arbor Systems, White Paper, http://www.hyperion.com/whitepapers.cfm

  2. S. Chaudhuri and U. Dayal, “An Overview of Data Warehousing and OLAP Technology,” SIGMOD Record, 26:1, pp. 65–74, 1997

    Article  Google Scholar 

  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, 2000

    Google Scholar 

  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, 1997

    Article  Google Scholar 

  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, 1995

    Google Scholar 

  6. H. Gupta and I. Mumick, “Selection of Views to Materialize Under a Maintenance Cost Constraint,” Stanford University, Technical Report

    Google Scholar 

  7. J. Hammer and L. Fu, “Speeding Up Data Cube Queries with Statistics Trees,” University of Florida, Gainesville, FL, Research report TR01-007, January 2001

    Google Scholar 

  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, 1997

    Google Scholar 

  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, 1999

    Google Scholar 

  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, 1995

    Google Scholar 

  11. MicroStrategy Inc., “The Case For Relational OLAP,” MicroStrategy, White Paper, http://www.microstrategy.com/publications/whitepapers/Case4Rolap/

  12. P. O’Neil and D. Quass, “Improved Query Performance with Variant Indexes,” SIGMOD Record, 26:2, pp. 38–49, 1997

    Article  Google Scholar 

  13. 14. Oracle Corp., “Oracle Express OLAP Technology,” http://www.oracle.com/olap/

  14. Redbrick Systems, “Informix Redbrick Decision Server,” Redbrick, Los Gatos, CA, Product Overview and Data Sheet, http://www.informix.com/redbrick/

  15. 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

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dept. of CISE, University of Florida, Florida, 32611-6120, U.S.A.

    Joachim Hammer & Lixin Fu

Authors
  1. Joachim Hammer
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  2. Lixin Fu
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Editor information

Editors and Affiliations

  1. Kyoto University, Kyoto, 606-8501, Japan

    Yahiko Kambayashi

  2. EC3, Siebensterngasse 21/3, 1070, Wien

    Werner Winiwarter

  3. Center for Spatial Information Science (CSIS), University of Tokyo, 4-6-1, Komaba Meguro-ku, Tokyo, 153-8904, Japan

    Masatoshi Arikawa

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© 2001 Springer-Verlag Berlin Heidelberg

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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

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  • DOI: https://doi.org/10.1007/3-540-44801-2_27

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42553-3

  • Online ISBN: 978-3-540-44801-3

  • eBook Packages: Springer Book Archive

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