Coordinating Agent Activities in Knowledge Discovery Processes
David Jensen, Yulin Dong, Barbara Staudt Lerner, Eric K. McCall,
Leon J. Osterweil, Stanley M. Sutton, Jr., and Alexander Wise
Department of Computer Science
University of Massachusetts Amherst
Amherst, MA 01003 USA
{jensen|yldong|lerner|mccall|ljo|sutton|wise}@cs.umass.edu
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.