Performance Tuning an Algorithm for Compressing Relational Tables

  • Jyrki Katajainen
  • Jeppe Nejsum Madsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2368)


We study the behaviour of an algorithm which compresses relational tables by representing common subspaces as Cartesian products. The output produced allows space to be saved while preserving the functionality of many relational operations such as select, project and join. We describe an implementation of an existing algorithm, propose a slight modification which with high probability produces the same output, and present a performance study showing that for all test instances used both adaptations are considerably faster than the current implementation in a commercial software product.


Hash Function Hash Table Relation Scheme Compression Scheme Performance Tune 
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]
    A. V. Aho, J. E. Hopcroft, AND J. Ullman, The Design and Analysis of Computer Algorithms, Addison-Wesley (1974).Google Scholar
  2. [2]
    Array Technology A/S, Array technology, Website accessible at (2002).
  3. [3]
    J. L. Bentley AND J. B. Saxe, Algorithms on vector sets, SIGACT News 11,9 (1979), 36–39.CrossRefGoogle Scholar
  4. [4]
    J. L. Carter AND M. N. Wegman, Universal classes of hash functions, Journal of Computer and System Sciences 18,2 (1979), 143–154.zbMATHCrossRefMathSciNetGoogle Scholar
  5. [5]
    E. F. Codd, A relational model of data for large shared data banks, Communications of the ACM 13,6 (1970), 377–387.zbMATHCrossRefGoogle Scholar
  6. [6]
    T. Hagerup, Sorting and searching on the word RAM, Proceedings of the 15th Annual Symposium on Theoretical Aspects of Computer Science, Lecture Notes in Computer Science 1373, Springer-Verlag (1998), 366–398.Google Scholar
  7. [7]
    J. Katajainen AND M. Lykke, Experiments with universal hashing, Technical Report 96/8, Department of Computer Science, University of Copenhagen (1996).Google Scholar
  8. [8]
    A. K. Mackworth, Constraint satisfaction, Encyclopedia of Artificial Intelligence, 2nd Edition, John Wiley & Sons (1992), 285–293.Google Scholar
  9. [9]
    J. N. Madsen, Algorithms for compressing and joining relations, CPH STL Report 2002-1, Department of Computing, University of Copenhagen (2002). Available at
  10. [10]
    N. C. Meyers, Traits: A new and useful template technique, C++ Report (1995). Available at
  11. [11]
    G. L. Møller, On the technology of array-based logic, Ph.D. Thesis, Technical University of Denmark (1995). Available at
  12. [12]
    M. Thorup, Even strongly universal hashing is pretty fast, Proceedings of the 11th Annual Symposium on Discrete Algorithms, ACM-SIAM (2000), 496–497.Google Scholar
  13. [13]
    M. N. Wegman AND J. L. Carter, New hash functions and their use in authentication and set equality, Journal of Computer and System Sciences 22,3 (1981), 265–279.zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Jyrki Katajainen
    • 1
  • Jeppe Nejsum Madsen
    • 2
  1. 1.Department of ComputingUniversity of CopenhagenCopenhagen EastDenmark
  2. 2.Array Technology A/SCopenhagen EastDenmark

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