Incremental Workflow Mining Based on Document Versioning Information

  • Ekkart Kindler
  • Vladimir Rubin
  • Wilhelm Schäfer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3840)


Current enterprises spend much effort to obtain precise models of their system engineering processes in order to improve the process capability of the organization. The manual design of workflow models is complicated, time-consuming and error-prone; capabilities of human beings in detecting discrepancies between the actual process and the process model are rather limited. Therefore, automatic techniques for deriving these models are becoming more and more important.

In this paper, we present an idea that exploits the user interaction with a version management system for the incremental automatic derivation, refinement and analysis of process models. Though this idea is not fully worked out yet, we sketch the architecture of the solution and the algorithms for the main steps of incremental automatic derivation of process models.


Mining Algorithm Business Process Management Resource Management System Design Review Capability Maturity Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Paulk, M.C., Curtis, B., Beth, M., Chrissis, Weber, C.V.: Capability Maturity Model for Software (SW-CMM). Technical Report CMU/SEI-93-TR-024, Carnegie Mellon University, Software Engineering Institute (1993)Google Scholar
  2. 2.
    SEI Carnegie Mellon: Capability Maturity Model® Integration (CMMISM), Version 1.1. Technical Report CMU/SEI-2002-TR-012, Carnegie Mellon, Software Engineering Institute (2002)Google Scholar
  3. 3.
    SEI Carnegie Mellon: Process Maturity Profile. Software CMM® 2004 Mid-Year Update. Technical report, Carnegie Mellon University, Software Engineering Institute (2004)Google Scholar
  4. 4.
    Weijters, A., van der Aalst, W.: Workflow Mining: Discovering Workflow Models from Event-Based Data. In: Dousson, C., Höppner, F., Quiniou, R. (eds.) Proceedings of the ECAI Workshop on Knowledge Discovery and Spatial Data, pp. 78–84 (2002)Google Scholar
  5. 5.
    Leymann, F., Roller, D.: Production Workflow: Concepts and Techniques. Prentice-Hall PTR, Upper Saddle River (1999)Google Scholar
  6. 6.
    van der Aalst, W., van Hee, K.: Workflow Management: Models, Methods, and System. In: Cooperative Information Systems. MIT Press, Cambridge (2002)Google Scholar
  7. 7.
    Kindler, E.: Using the Petri Net Markup Language for Exchanging Business Processes? Potential and Limitations. In: Nüttgens, M., Mendling, J. (eds.) XML4BPM 2004, Proceedings of the 1st GI Workshop XML4BPM – XML Interchange Formats for Business Process Management at 7th GI Conference Modellierung 2004, Marburg Germany, March 2004, pp. 43–60 (2004),
  8. 8.
    van der Aalst, W., Weijters, A.: Process mining: a research agenda. Comput. Ind. 53, 231–244 (2004)CrossRefGoogle Scholar
  9. 9.
    Cook, J.E., Wolf, A.L.: Discovering Models of Software Processes from Event-Based Data. ACM Trans. Softw. Eng. Methodol. 7, 215–249 (1998)CrossRefGoogle Scholar
  10. 10.
    Mannila, H., Rusakov, D.: Decomposing event sequences into independent components. In: Kumar, V., Grossman, R. (eds.) Proceedings of the First SIAM Conference on Data Mining, pp. 1–17. SIAM, Philadelphia (2001)Google Scholar
  11. 11.
    Agrawal, R., Gunopulos, D., Leymann, F.: Mining Process Models from Workflow Logs. In: Proceedings of the 6th International Conference on Extending Database Technology, pp. 469–483. Springer, Heidelberg (1998)Google Scholar
  12. 12.
    Herbst, J., Karagiannis, D.: An Inductive approach to the Acquisition and Adaptation of Workflow Models (1999),
  13. 13.
    Herbst, J., Karagiannis, D.: Integrating Machine Learning and Workflow Management to Support Acquisition and Adaptation of Workflow Models. In: Proceedings of the 9th International Workshop on Database and Expert Systems Applications, p. 745. IEEE Computer Society, Los Alamitos (1998)Google Scholar
  14. 14.
    Weijters, A., van der Aalst, W.: Process mining: discovering workflow models from event-based data. In: Proceedings of the 13th Belgium-Netherlands Conference on Artificial Intelligence (BNAIC 2001), pp. 283–290 (2001)Google Scholar
  15. 15.
    Weijters, T., van der Aalst, W.: Rediscovering Workflow Models from Event-Based Data. In: Hoste, V., Pauw, G. (eds.) Proceedings of the 11th Dutch-Belgian Conference on Machine Learning (Benelearn 2001), pp. 93–100 (2001)Google Scholar
  16. 16.
    Schimm, G.: Process Miner - A Tool for Mining Process Schemes from Event-Based Data. In: Proceedings of the European Conference on Logics in Artificial Intelligence, pp. 525–528. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  17. 17.
    Zur Muehlen, M., Rosemann, M.: Workflow-Based Process Monitoring and Controlling Technical and Organizational Issues. In: Proceedings of the 33rd Hawaii International Conference on System Sciences, vol. 6. IEEE Computer Society, Los Alamitos (2000)Google Scholar
  18. 18.
    Fogel, K.F.: Open Source Development with CVS. Coriolis Group Books (1999)Google Scholar
  19. 19.
    van der Aalst, W.: High level Petri nets Extending classical Petri nets with color, time and hierarchy,
  20. 20.
    OMG: UML 2.0 Superstructure Specification. Version 2.0 ptc/03-08-02, Object Management Group, Final Adopted Specification (2003)Google Scholar
  21. 21.
    Dahlqvist, A.P., Asklund, U., Crnkovic, I., Hedin, A., Larsson, M., Ranby, J., Svensson, D.: Product Data Management and Software Configuration Management - Similarities and Differences,
  22. 22.
    OMG: Meta Object Facility (MOF) specification. Technical report, Object Management Group (2002)Google Scholar
  23. 23.
    van der Aalst, W., van Dongena, B.F., Herbst, J., Marustera, L., Schimm, G., Weijters, A.J.M.M.: Workflow mining: A survey of issues and approaches. Data & Knowledge Engineering 47, 237–267 (2003)CrossRefGoogle Scholar
  24. 24.
    Murata, T.: Petri Nets: Properties, Analysis and Applications. Proceedings of the IEEE 77(4), 541–580 (1989)CrossRefGoogle Scholar
  25. 25.
    Reisig, W., Rozenberg, G. (eds.): APN 1998. LNCS, vol. 1491. Springer, Heidelberg (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ekkart Kindler
    • 1
  • Vladimir Rubin
    • 1
  • Wilhelm Schäfer
    • 1
  1. 1.Software Engineering GroupUniversity of PaderbornPaderbornGermany

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