A new framework for business process knowledge discovery

Hyerim Bae, Wonchang Hur, Sajal K. Das, Seoung Bum Kim

Research output: Contribution to conferencePaperpeer-review


The Business Process Management System (BPMS) has received more attention as companies increasingly realize the importance of business processes. However, traditional BPMS focuses mainly on correct modeling and exact automation of the process flow and pays little attention to the achievement of the final goals of improving process efficiency and process innovation. During and after execution of processes, BPMS usually generates much process log data in which numerous meaningful rules and patterns are hidden. In the present study we employ a data mining technique to extract useful knowledge from the complex process log data. A data model and a system framework for process mining are provided to help understand the existing BPMS. Experiments with the simulated data demonstrate the effectiveness of the model and of the framework.

Original languageEnglish
Number of pages6
Publication statusPublished - 2008
Externally publishedYes
EventIIE Annual Conference and Expo 2008 - Vancouver, BC, Canada
Duration: 2008 May 172008 May 21


OtherIIE Annual Conference and Expo 2008
CityVancouver, BC

Bibliographical note

Funding Information:
Silvia Ferrari ([email protected]) is an assistant professor of mechanical engineering and materials science at Duke University, where she directs the Laboratory for Intelligent Systems and Controls. Her research interests include robust adaptive control of aircraft, learning and approximate dynamic programming, and distributed sensor management. She received the B.S. from Embry-Riddle Aeronautical University and the M.A. and Ph.D. from Princeton University. She is a member of IEEE, ASME, SPIE, and AIAA. She is the recipient of the ONR young investigator award (2004), the NSF CAREER award (2005), and the PECASE award (2006). She can be contacted at Duke University, Department of Mechanical Engineering and Materials Science, Box 90300, Durham, NC 27708-0005 USA.


  • Business process
  • Business process mining
  • Decision tree
  • Process pattern

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Industrial and Manufacturing Engineering


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