Advertisement

A Multi-Tier Architecture for High-Performance Data Mining

  • Ralf Rantzau
  • Holger Schwarz
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Data mining has been recognised as an essential element of decision support, which has increasingly become a focus of the database industry. Like all computationally expensive data analysis applications, for example Online Analytical Processing (OLAP), performance is a key factor for usefulness and acceptance in business. In the course of the CRITIKAL1 project (Client-Server Rule Induction Technology for Industrial Knowledge Acquisition from Large Databases), which is funded by the European Commission, several kinds of architectures for data mining were evaluated with a strong focus on high performance. Specifically, the data mining techniques association rule discovery and decision tree induction were implemented into a prototype. We present the architecture developed by the CRITIKAL consortium and compare it to alternative architectures.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. Proceedings of the ACM SIGMOD International Conference, Washington DC, USA, 207–216, May, 1993.Google Scholar
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.: Database Mining: A Performance Perspective. IEEE Transactions on Knowledge and Data Engineering, 5 (6): 914–925, December, 1993.Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. Proceedings of the 20th International Conference on Very Large Databases, Santiago, Chile, 487–499, September, 1994.Google Scholar
  4. 4.
    Attar Software: XpertRule Profiler Reference Manual, 1996–1998.Google Scholar
  5. 5.
    Brin, S., Motwani, R., Ullman, J., Tsur, S.: Dynamic Itemset Counting and Implication Rules for Market Basket Data. Proceedings of the ACM SIGMOD International Conference, Tucson, Arizona, USA, 255–264, May, 1997.Google Scholar
  6. 6.
    Cheung, D., Ng, V., Fu, A., Fu, Y.: Efficient Mining of Association Rules in Distributed Databases. IEEE Transactions on Knowledge and Data Engineering, 8 (6): 911–922, December, 1996.Google Scholar
  7. 7.
    Han, E., Karypis, G., Kumar, V., Mobasher, B.: Hypergraph Based Clustering in High-Dimensional Data Sets: A Summary of Results. Bulletin of the Technical Committee on Data Engineering, 21 (1): 15–22, March, 1998.Google Scholar
  8. 8.
    Savasere, A., Omiencinski, E., Navathe, S.: An Efficient Algorithm for Mining Association Rules in Large Databases. Proceedings of the 21st International Conference on Very Large Databases, Zürich, Switzerland, 432–444, September, 1995.Google Scholar
  9. 9.
    Schwarz, H.: Survey of State-of-Art Association Rules Discovery. Deliverable No. D4.1, European Commission, ESPRIT Project No. 22700, Brussels, Belgium, May, 1997.Google Scholar
  10. 10.
    Shafer, J., Agrawal, R., Mehta, M.: SPRINT: A Scalable Parallel Classifier for Data Mining. Proceedings of the 22nd International Conference on Very Large Databases, Bombay, India, 544–555, September, 1996.Google Scholar
  11. 11.
    Srikant, R., Agrawal, R.: Mining Generalized Association Rules. Proceedings of the 21st International Conference on Very Large Databases, Zürich, Switzerland, 407–419, September, 1995.Google Scholar
  12. 12.
    Srikant, R., Agrawal, R.: Mining Quantitative Association Rules in Large Relational Tables, Proceedings of the ACM SIGMOD International Conference, Montreal, Canada, 1–12, June, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Ralf Rantzau
    • 1
  • Holger Schwarz
    • 1
  1. 1.Institute of Parallel and Distributed High-Performance Systems (IPVR)University of StuttgartStuttgartGermany

Personalised recommendations