Materialized View Selection for Multi-cube Data Models

  • Amit Shukla
  • Prasad M. Deshpande
  • Jeffrey F. Naughton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1777)


OLAP applications use precomputation of aggregate data to improve query response time. While this problem has been well-studied in the recent database literature, to our knowledge all previous work has focussed on the special case in which all aggregates are computed from a single cube (in a star schema, this corresponds to there being a single fact table). This is unfortunate, because many real world applications require aggregates over multiple fact tables. In this paper, we attempt to fill this lack of discussion about the issues arising in multi-cube data models by analyzing these issues. Then we examine performance issues by studying the precomputation problem for multi-cube systems. We show that this problem is significantly more complex than the single cube precomputation problem, and that algorithms and cost models developed for single cube precomputation must be extended to deal well with the multi-cube case. Our results from a prototype implementation show that for multi-cube workloads substantial performance improvements can be realized by using the multi-cube algorithms.


Greedy Algorithm Cost Model Fact Table Query Response Time Space Allocation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    APB-1 Benchmark, Release II, November 1998. Available from
  2. 2.
    E. Baralis, S. Paraboschi, E. Teniente. Materialized View Selection in a Multidimensional Database, Proc. of the 23rd Int. VLDB Conf., 1997.Google Scholar
  3. 3.
    R. Elmasri, S. Navathe, Fundamentals of Database Systems, The Benjamin/Cummings Publishing Company, Inc., 1989.Google Scholar
  4. 4.
    J. Gray, A. Bosworth, A. Layman, H. Pirahesh. Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals, Proc. of the 12th Int. Conf. on Data Engg., pp 152–159, 1996.Google Scholar
  5. 5.
    H. Gupta, V. Harinarayan, A. Rajaraman, J.D. Ullman. Index Selection for OLAP. Proc. of the 13th ICDE, 208–219, 1997.Google Scholar
  6. 6.
    H. Gupta. Selection of Views to Materialize in a Data Warehouse. Proc. of the Sixth ICDT, 98–112, 1997.Google Scholar
  7. 7.
    V. Harinarayan, A. Rajaraman, J.D. Ullman. Implementing Data Cubes Effciently, Proc. ACM SIGMOD Int. Conf. on Man. of Data, 205–227, 1996.Google Scholar
  8. 8.
    Nigel Pendse and Richard Creeth, The Olap Report. Information available from
  9. 9.
    A. Shukla, P.M. Deshpande, J.F. Naughton, K. Ramasamy, Storage Estimation for Multidimensional Aggregates in the Presence of Hierarchies, Proc. of the 22nd Int. VLDB Conf., 522–531, 1996.Google Scholar
  10. 10.
    A. Shukla, P.M. Deshpande, J.F. Naughton, Materialized View Selection for Multi-dimensional Datasets, Proc. of the 24th Int. VLDB Conf., 1998.Google Scholar
  11. 11.
    A. Shukla, Materialized View Selection for Multidimensional Datasets, Ph.D. Dissertation, University of Wisconsin-Madison, 1999.Google Scholar
  12. 12.
    Erik Thomsen, Olap Solutions: Building Multidimensional Information Systems, John Wiley & Sons, 1997.Google Scholar
  13. 13.
    J.D. Ullman, Effcient Implementation of Data Cubes Via Materialized Views A survey of the field for the 1996 KDD conference.Google Scholar
  14. 14.
    J.D. Ullman, Principles of Database and Knowledge-base Systems, Volume II, Computer Science Press, 1988Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Amit Shukla
    • 1
  • Prasad M. Deshpande
    • 1
  • Jeffrey F. Naughton
    • 1
  1. 1.University of Wisconsin - MadisonMadisonUSA

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