Advertisement

Integrating Pattern Mining in Relational Databases

  • Toon Calders
  • Bart Goethals
  • Adriana Prado
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)

Abstract

Almost a decade ago, Imielinski and Mannila introduced the notion of Inductive Databases to manage KDD applications just as DBMSs successfully manage business applications. The goal is to follow one of the key DBMS paradigms: building optimizing compilers for ad hoc queries. During the past decade, several researchers proposed extensions to the popular relational query language, SQL, in order to express such mining queries. In this paper, we propose a completely different and new approach, which extends the DBMS itself, not the query language, and integrates the mining algorithms into the database query optimizer. To this end, we introduce virtual mining views, which can be queried as if they were traditional relational tables (or views). Every time the database system accesses one of these virtual mining views, a mining algorithm is triggered to materialize all tuples needed to answer the query. We show how this can be done effectively for the popular association rule and frequent set mining problems.

Keywords

Association Rule Relational Database Mining Algorithm Query Language Association Rule Mining 
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.

References

  1. 1.
    Abiteboul, S., Hull, R., Vianu, V.: Foundations of Databases. Addison-Wesley, Reading (1995)MATHGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th VLDB Conference, pp. 487–489 (1994)Google Scholar
  3. 3.
    Calders, T., Lakshmanan, L.V.S., Ng, R.T., Paredaens, J.: Expressive Power of an Algebra for Data Mining (manuscript, 2006)Google Scholar
  4. 4.
    Calders, T., Goethals, B., Prado, A.: Constraint Extraction from SQL-queries (manuscript, 2006)Google Scholar
  5. 5.
    Goethals, B., Van den Bussche, J., Vanhoof, K.: Decision Support Queries for the Interpretation of Data Mining Results (manuscript, 1998)Google Scholar
  6. 6.
    Goethals, B., Van den Bussche, J.: On Supporting Interactive Association Rule Mining. In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds.) DaWaK 2000. LNCS, vol. 1874, pp. 307–316. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Garcia-Molina, H., Ullman, J.D., Widom, J.: Database System Implementation. Prentice-Hall, Inc., Englewood Cliffs (2000)Google Scholar
  8. 8.
    Han, J., Fu, Y., Wang, W., Koperski, K., Zaiane, O.: DMQL: A Data Mining Query Language for Relational Databases. In: SIGMOD DMKD Workshop (1996)Google Scholar
  9. 9.
    Imielinski, T., Mannila, H.: A Database Perspective on Knowledge Discovery. Communications of the ACM 39, 58–64 (1996)CrossRefGoogle Scholar
  10. 10.
    Imielinski, T., Virmani, A.: MSQL: A Query Language for Database Mining. Data Mining and Knowledge Discovery 3, 373–408 (1999)CrossRefGoogle Scholar
  11. 11.
    Meo, R., Psaila, G., Ceri, S.: An Extension to SQL for Mining Association Rules. Data Mining and Knowledge Discovery 2, 195–224 (1998)CrossRefGoogle Scholar
  12. 12.
    Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)MATHGoogle Scholar
  13. 13.
    Wang, H., Zaniolo, C.: Nonmonotonic Reasoning in LDL++. In: Minker, J. (ed.) Logic-Based Artificial Intelligence, pp. 523–544. Kluwer Academic Publishers, Dordrecht (2000)Google Scholar
  14. 14.
    Wang, H., Zaniolo, C.: ATLaS: A Native Extension of SQL for Data Mining. In: 3rd SIAM Conference, pp. 130–144 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Toon Calders
    • 1
  • Bart Goethals
    • 1
  • Adriana Prado
    • 1
  1. 1.University of AntwerpBelgium

Personalised recommendations