An Infrastructure for Cost-Effective Testing of Operational Support Algorithms Based on Colored Petri Nets

  • Joyce Nakatumba
  • Michael Westergaard
  • Wil M. P. van der Aalst
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7347)


Operational support is a specific type of process mining that assists users while process instances are being executed. Examples are predicting the remaining processing time of a running insurance claim and recommending the action that minimizes the treatment costs of a particular patient. Whereas it is easy to evaluate prediction techniques using cross validation, the evaluation of recommendation techniques is challenging as the recommender influences the execution of the process. It is therefore impossible to simply use historic event data. Therefore, we present an approach where we use a colored Petri net model of user behavior to drive a real workflow system and real implementations of operational support, thereby providing a way of evaluating algorithms for operational support before implementation and a costly test using real users. In this paper, we evaluate algorithms for operational support using different user models. We have implemented our approach using Access/CPN 2.0.


Execution Time User Model Recommendation Algorithm Process Instance Operational Support 
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.
    van der Aalst, W.M.P.: Process Mining-Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
  2. 2.
    van der Aalst, W.M.P., Pesic, M., Schonenberg, H.: Declarative Workflows: Balancing Between Flexibility and Support. Computer Science - Research and Development 23(2), 99–113 (2009)CrossRefGoogle Scholar
  3. 3.
    van der Aalst, W.M.P., Pesic, M., Song, M.S.: Beyond Process Mining: From the Past to Present and Future. In: Pernici, B. (ed.) CAiSE 2010. LNCS, vol. 6051, pp. 38–52. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    van der Aalst, W.M.P., Schonenberg, H., Song, M.S.: Time Prediction based on Process Mining. Information Systems 36(2), 450–475 (2011)CrossRefGoogle Scholar
  5. 5.
    van der Aalst, W.M.P., Stahl, C., Westergaard, M.: Strategies for Modeling Complex Processes Using Colored Petri Nets. In: Jensen, K., Donatelli, S., Kleijn, J. (eds.) ToPNoC V. LNCS, vol. 6900, pp. 265–291. Springer, Heidelberg (2012)Google Scholar
  6. 6.
    CPN Tools (2012),
  7. 7.
  8. 8.
    Jensen, K., Kristensen, L.M.: Coloured Petri Nets: Modeling and Validation of Concurrent Systems. Springer (2009)Google Scholar
  9. 9.
    Mans, R.S., van der Aalst, W.M.P., Russell, N.C., Bakker, P.J.M., Moleman, A.J.: Process-Aware Information System Development for the Healthcare Domain - Consistency, Reliability, and Effectiveness. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 635–646. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Nakatumba, J., van der Aalst, W.M.P.: Analyzing Resource Behavior Using Process Mining. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 69–80. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Nakatumba, J., Westergaard, M., van der Aalst, W.M.P.: A Meta-model for Operational Support. BPM Center Report BPM-12-05, (2012)Google Scholar
  12. 12.
    Nakatumba, J., Westergaard, M., van der Aalst, W.M.P.: Generating Event Logs with Workload-Dependent Speeds from Simulation Models. In: Enterprise and Organizational Modeling and Simulation. LNBIP. Springer (2012)Google Scholar
  13. 13.
  14. 14.
    Resnick, P., Varian, H.R.: Recommender Systems. Comm. of the ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  15. 15.
    Rozinat, A., Wynn, M.T., van der Aalst, W.M.P., ter Hofstede, A.H.M., Fidge, C.: Workflow Simulation for Operational Decision Support. Data and Knowledge Engineering 68(9), 834–850 (2009)CrossRefGoogle Scholar
  16. 16.
    Schonenberg, H., Weber, B., van Dongen, B.F., van der Aalst, W.M.P.: Supporting Flexible Processes through Recommendations Based on History. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 51–66. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Weber, B., Wild, W., Breu, R.: CBRFlow: Enabling Adaptive Workflow Management Through Conversational Case-Based Reasoning. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 434–448. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Westergaard, M.: Access/CPN 2.0: A High-level Interface to Coloured Petri Net Models. In: Kristensen, L.M., Petrucci, L. (eds.) PETRI NETS 2011. LNCS, vol. 6709, pp. 328–337. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Westergaard, M., Maggi, F.M.: Modeling and Verification of a Protocol for Operational Support Using Coloured Petri Nets. In: Kristensen, L.M., Petrucci, L. (eds.) PETRI NETS 2011. LNCS, vol. 6709, pp. 169–188. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Wickens, C.D.: Engineering Psychology and Human Performance. Harper (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Joyce Nakatumba
    • 1
  • Michael Westergaard
    • 1
  • Wil M. P. van der Aalst
    • 1
  1. 1.Eindhoven University of TechnologyThe Netherlands

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