A Greedy Approach to Concurrent Processing of Frequent Itemset Queries

  • Pawel Boinski
  • Marek Wojciechowski
  • Maciej Zakrzewicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4081)


We consider the problem of concurrent execution of multiple frequent itemset queries. If such data mining queries operate on overlapping parts of the database, then their overall I/O cost can be reduced by integrating their dataset scans. The integration requires that data structures of many data mining queries are present in memory at the same time. If the memory size is not sufficient to hold all the data mining queries, then the queries must be scheduled into multiple phases of loading and processing. Since finding the optimal assignment of queries to phases is infeasible for large batches of queries due to the size of the search space, heuristic algorithms have to be applied. In this paper we formulate the problem of assigning the queries to phases as a particular case of hypergraph partitioning. To solve the problem, we propose and experimentally evaluate two greedy optimization algorithms.


Association Rule Concurrent Processing Greedy Approach Concurrent Execution Restricted Candidate List 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules Between Sets of Items in Large Databases. In: Proc. of the 1993 ACM SIGMOD Conf. on Management of Data (1993)Google Scholar
  2. 2.
    Agrawal, R., Mehta, M., Shafer, J., Srikant, R., Arning, A., Bollinger, T.: The Quest Data Mining System. In: Proc. of the 2nd KDD Conference (1996)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Proc. of the 20th Int’l Conf. on Very Large Data Bases (1994)Google Scholar
  4. 4.
    Alpert, C.J., Kahng, A.B.: Recent Directions in Netlist Partitioning: A Survey. Integration: The VLSI Journal 19 (1995)Google Scholar
  5. 5.
    Baralis, E., Psaila, G.: Incremental Refinement of Mining Queries. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 173–182. Springer, Heidelberg (1999)Google Scholar
  6. 6.
    Boinski, P., Jozwiak, K., Wojciechowski, M., Zakrzewicz, M.: Improving Quality of Agglomerative Scheduling in Concurrent Processing of Frequent Itemset Queries. In: Proc. of the International IIS: IIPWM 2006 Conference (2006)Google Scholar
  7. 7.
    Cheung, D.W., Han, J., Ng, V., Wong, C.Y.: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. In: Proc. of the 12th ICDE (1996)Google Scholar
  8. 8.
    Garey, M.R., Johnson, D.S.: Computers and Intractability. A Guide to the Theory of NP-Completeness. WH Freeman and Company, New York (1979)zbMATHGoogle Scholar
  9. 9.
    Hart, J.P., Shogan, A.W.: Semi-greedy Heuristics: An Empirical Study. Operations Research Letters 6 (1987)Google Scholar
  10. 10.
    Imielinski, T., Mannila, H.: A Database Perspective on Knowledge Discovery. Communications of the ACM 39(11) (1996)Google Scholar
  11. 11.
    Jeudy, B., Boulicaut, J.-F.: Using Condensed Representations for Interactive Association Rule Mining. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Karypis, G.: Multilevel Hypergraph Partitioning. In: Cong, J., Shinnerl, J. (eds.) Multilevel Optimization Methods for VLSI, Kluwer Academic Publishers, Dordrecht (2002)Google Scholar
  13. 13.
    Karypis, G., Han, E., Kumar, V.: Chameleon: A Hierarchical Clustering Algorithm Using Dynamic Modeling. IEEE Computer 32(8) (1999)Google Scholar
  14. 14.
    Meo, R.: Optimization of a Language for Data Mining. In: Proc. of the ACM Symposium on Applied Computing - Data Mining Track (2003)Google Scholar
  15. 15.
    Morzy, M., Wojciechowski, M., Zakrzewicz, M.: Optimizing a Sequence of Frequent Pattern Queries. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2005. LNCS, vol. 3589, Springer, Heidelberg (2005)Google Scholar
  16. 16.
    Sellis, T.: Multiple Query Optimization. ACM Transactions on Database Systems 13(1) (1988)Google Scholar
  17. 17.
    Wojciechowski, M., Zakrzewicz, M.: Evaluation of Common Counting Method for Concurrent Data Mining Queries. In: Kalinichenko, L.A., Manthey, R., Thalheim, B., Wloka, U. (eds.) ADBIS 2003. LNCS, vol. 2798, pp. 76–87. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Wojciechowski, M., Zakrzewicz, M.: Evaluation of the Mine Merge Method for Data Mining Query Processing. In: Benczúr, A.A., Demetrovics, J., Gottlob, G. (eds.) ADBIS 2004. LNCS, vol. 3255, Springer, Heidelberg (2004)Google Scholar
  19. 19.
    Wojciechowski, M., Zakrzewicz, M.: On Multiple Query Optimization in Data Mining. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 696–701. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pawel Boinski
    • 1
  • Marek Wojciechowski
    • 1
  • Maciej Zakrzewicz
    • 1
  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznanPoland

Personalised recommendations