Coordinating agent activities in knowledge discovery processes

  • Authors:

  • David Jensen;Yulin Dong;Barbara Staudt Legner;Eric K. McCall;Leon J. Osterweil;Stanley M. Sutton, Jr.;Alexander Wise

  • Affiliations:

  • Department of Computer Science, University of Massachusetts Amherst, Amherst, MA;Department of Computer Science, University of Massachusetts Amherst, Amherst, MA;Department of Computer Science, University of Massachusetts Amherst, Amherst, MA;Department of Computer Science, University of Massachusetts Amherst, Amherst, MA;Department of Computer Science, University of Massachusetts Amherst, Amherst, MA;Department of Computer Science, University of Massachusetts Amherst, Amherst, MA;Department of Computer Science, University of Massachusetts Amherst, Amherst, MA

  • Venue:
  • WACC '99 Proceedings of the international joint conference on Work activities coordination and collaboration
  • Year:
  • 1999

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Abstract

Knowledge discovery in databases (KDD) is an increasingly widespread activity. KDD processes may entail the use of a large number of data manipulation and analysis techniques, and new techniques are being developed on an ongoing basis. A challenge for the effective use of KDD is coordinating the use of these techniques, which may be highly specialized, conditional and contingent. Additionally, the understanding and validity of KDD results can depend critically on the processes by which they were derived. We propose to use process programming to address the coordination of agents in the use of KDD techniques. We illustrate this approach using the process language Little-JIL to program a representative bivariate regression process. With Little-JIL programs we can clearly capture the coordination of KDD activities, including control flow, pre- and post-requisites, exception handling, and resource usage.