Skip to main content

Development of a Grid Service for Scalable Decision Tree Construction from Grid Databases

  • Conference paper
Parallel Processing and Applied Mathematics (PPAM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3911))

  • 712 Accesses

Abstract

Classification deals with discovery of a predictive learning function that classifies a data object into one of several predefined classes. We present a novel decision-tree-based classification service which can be used either autonomously or as a building block to construct distributed and scalable classifiers that operate on data repositories integrated into the Grid that typically involve large, complex, heterogeneous, and geographically distributed datasets. Although classification is considered as a well-studied problem – a lot of classification methods were proposed for sequential, parallel and distributed environments, so far, to our best knowledge, no effort was devoted to building classifiers based on federation of Grid resources. The Grid service described in this paper was fully implemented and integrated into the GridMiner framework (www.gridminer.org). Scalability and performance of the prototype implementation have been examined and the results are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Brezany, P., Kloner, C.: Programming the decision tree service within the grid data mining framework gridminer-core. Technical Report GridMiner TR 2004-05, University of Vienna Austria (September 2004)

    Google Scholar 

  2. Brezany, P., Min Tjoa, A., Rusnak, M., Janciak, I.: Knowledge grid support for treatment of traumatic brain injury victims. In: International Conference on Computational Science and Its Applications, Montreal, Canada (May 2003)

    Google Scholar 

  3. Hofer, J., Brezany, P.: Distributed decision tree induction within the grid data mining framework gridminer-core. Technical Report GridMiner TR 2004-04, University of Vienna, Vienna, Austria (March 2004)

    Google Scholar 

  4. Mehta, M., Agrawal, R., Rissanen, J.: SLIQ: A fast scalable classifier for data mining. In: Extending Database Technology, pp. 18–32 (1996)

    Google Scholar 

  5. Quinlan, J.R.: Induction on decision trees. Machine Learning 1, 81–106 (1986)

    Google Scholar 

  6. Quinlan, J.R.: Simplifying decision trees. International Journal of Man-Machine Studies 27, 221–234 (1987)

    Article  Google Scholar 

  7. Shafer, J.C., Agrawal, R., Mehta, M.: SPRINT: A scalable parallel classifier for data mining. In: Vijayaraman, T.M., Buchmann, A.P., Mohan, C., Sarda, N.L. (eds.) Proc. 22nd Int. Conf. Very Large Databases, VLDB, 3–6, pp. 544–555. Morgan Kaufmann, San Francisco (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Brezany, P., Kloner, C., Tjoa, A.M. (2006). Development of a Grid Service for Scalable Decision Tree Construction from Grid Databases. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2005. Lecture Notes in Computer Science, vol 3911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752578_74

Download citation

  • DOI: https://doi.org/10.1007/11752578_74

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34141-3

  • Online ISBN: 978-3-540-34142-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics