Workflow mining with InWoLvE

  • Authors:
  • Joachim Herbst;Dimitris Karagiannis

  • Affiliations:
  • DaimlerChrysler AG, Postfach 2360, 89013 Ulm, Germany;University of Vienna, Henna, Austria

  • Venue:
  • Computers in Industry - Special issue: Process/workflow mining
  • Year:
  • 2004

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Abstract

State of the art information systems are based on explicit process models called workflow models. Experience from industrial practice shows that the definition of workflow models is a very time consuming and error prone task. Recently, there has been an increasing interest in applying techniques from data mining and machine learning to support this task. This approach has also been termed as process or workflow mining. In this paper, we give an overview of the algorithms that were implemented within the InWoLvE workflow mining system, we summarize the most important results of their experimental evalualion and we present the experiences that were made in the first industrial application of InWoLvE.