Rule-Based Business Process Mining: Applications for Management

  • Filip Caron
  • Jan Vanthienen
  • Bart Baesens
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 171)


The abundance of available event data, originating from process-aware information systems, creates opportunities for enterprise risk management applications at the intersection of the business & management, artificial intelligence and knowledge representation research fields. This paper proposes a rule-based process mining approach for dealing with uncertainty and risk. The applicability of the approach is demonstrated using the updating and debugging process of a social security service provider.


Business Process Process Instance Business Rule Management Control System Conformance Check 
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.
    COSO. Enterprise risk management - integrated framework. Technical report, Committee of Sponsoring Organizations of the Treadway Commission (2004)Google Scholar
  2. 2.
    Curtis, B., Kellner, M.I., Over, J.: Process modeling. Communications of the ACM 35(9), 75–90 (1992)CrossRefGoogle Scholar
  3. 3.
    Giannakopoulou, D., Havelund, K.: Automata-based verification of temporal properties on running programs. In: Proceedings of the 16th Annual Conference on Automated Software Engineering, pp. 412–416. IEEE Computer Society (2001)Google Scholar
  4. 4.
    Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust process discovery with artificial negative events. The Journal of Machine Learning Research 10, 1305–1340 (2009)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Herbst, J.: A machine learning approach to workflow management. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 183–194. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Pickett, K.H.S.: The Internal Auditing Handbook. Wiley (2010)Google Scholar
  7. 7.
    Rozinat, A., van der Aalst, W.M.P.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)CrossRefGoogle Scholar
  8. 8.
    van der Aalst, W.M.P., de Beer, H.T., van Dongen, B.F.: Process mining and verification of properties: An approach based on temporal logic. In: Meersman, R. (ed.) OTM 2005. LNCS, vol. 3760, pp. 130–147. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  10. 10.
    Wen, L., Wang, J., Sun, J.: Detecting implicit dependencies between tasks from event logs. In: Zhou, X., Li, J., Shen, H.T., Kitsuregawa, M., Zhang, Y. (eds.) APWeb 2006. LNCS, vol. 3841, pp. 591–603. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Yip, F., Wong, A.K.Y., Parameswaran, N., Ray, P.: Rules and ontology in compliance management. In: 11th IEEE International Enterprise Distributed Object Computing Conference, pp. 435–435. IEEE (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Filip Caron
    • 1
  • Jan Vanthienen
    • 1
  • Bart Baesens
    • 1
  1. 1.Department of Decision Sciences and Information ManagementKU LeuvenLeuvenBelgium

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