Predictive Monitoring of Business Processes

  • Fabrizio Maria Maggi
  • Chiara Di Francescomarino
  • Marlon Dumas
  • Chiara Ghidini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8484)


Modern information systems that support complex business processes generally maintain significant amounts of process execution data, particularly records of events corresponding to the execution of activities (event logs). In this paper, we present an approach to analyze such event logs in order to predictively monitor business constraints during business process execution. At any point during an execution of a process, the user can define business constraints in the form of linear temporal logic rules. When an activity is being executed, the framework identifies input data values that are more (or less) likely to lead to the achievement of each business constraint. Unlike reactive compliance monitoring approaches that detect violations only after they have occurred, our predictive monitoring approach provides early advice so that users can steer ongoing process executions towards the achievement of business constraints. In other words, violations are predicted (and potentially prevented) rather than merely detected. The approach has been implemented in the ProM process mining toolset and validated on a real-life log pertaining to the treatment of cancer patients in a large hospital.


Predictive Process Monitoring Recommendations Business Constraints Linear Temporal Logic 


  1. 1.
    3TU Data Center: BPI Challenge 2011 Event Log (2011), doi:10.4121/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54Google Scholar
  2. 2.
    van der Aalst, W.M.P., Pesic, M., Song, M.: 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
  3. 3.
    van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)CrossRefGoogle Scholar
  4. 4.
    Beheshti, S.-M.-R., Benatallah, B., Motahari-Nezhad, H.R., Sakr, S.: A query language for analyzing business processes execution. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 281–297. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Birukou, A., D’Andrea, V., Leymann, F., Serafinski, J., Silveira, P., Strauch, S., Tluczek, M.: An integrated solution for runtime compliance governance in SOA. In: Maglio, P.P., Weske, M., Yang, J., Fantinato, M. (eds.) ICSOC 2010. LNCS, vol. 6470, pp. 122–136. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Castellanos, M., Salazar, N., Casati, F., Dayal, U., Shan, M.-C.: Predictive business operations management. In: Bhalla, S. (ed.) DNIS 2005. LNCS, vol. 3433, pp. 1–14. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Conforti, R., de Leoni, M., La Rosa, M., van der Aalst, W.M.P.: Supporting risk-informed decisions during business process execution. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 116–132. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Feldman, Z., Fournier, F., Franklin, R., Metzger, A.: Proactive event processing in action: a case study on the proactive management of transport processes. In: DEBS (2013)Google Scholar
  9. 9.
    Folino, F., Guarascio, M., Pontieri, L.: Discovering context-aware models for predicting business process performances. In: Meersman, R., et al. (eds.) OTM 2012, Part I. LNCS, vol. 7565, pp. 287–304. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Holmes, T., Mulo, E., Zdun, U., Dustdar, S.: Model-aware monitoring of SOAs for compliance. In: Service Engineering, pp. 117–136. Springer (2011)Google Scholar
  11. 11.
    Kang, B., Kim, D., Kang, S.H.: Real-time business process monitoring method for prediction of abnormal termination using knni-based lof prediction. Expert Syst. Appl. (2012)Google Scholar
  12. 12.
    Lo, D., Cheng, H.: Lucia: Mining closed discriminative dyadic sequential patterns. In: Proc. of EDBT, pp. 21–32. Springer (2011)Google Scholar
  13. 13.
    Ly, L.T., Rinderle-Ma, S., Knuplesch, D., Dadam, P.: Monitoring business process compliance using compliance rule graphs. In: Meersman, R., et al. (eds.) OTM 2011, Part I. LNCS, vol. 7044, pp. 82–99. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  14. 14.
    Maggi, F.M., Westergaard, M., Montali, M., van der Aalst, W.M.P.: Runtime verification of LTL-based declarative process models. In: Khurshid, S., Sen, K. (eds.) RV 2011. LNCS, vol. 7186, pp. 131–146. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Maggi, F.M., Montali, M., Westergaard, M., van der Aalst, W.M.P.: Monitoring business constraints with linear temporal logic: An approach based on colored automata. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 132–147. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  16. 16.
    Maggi, F.M., Montali, M., van der Aalst, W.M.P.: An operational decision support framework for monitoring business constraints. In: de Lara, J., Zisman, A. (eds.) FASE 2012. LNCS, vol. 7212, pp. 146–162. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  17. 17.
    de Medeiros, A.K.A., Guzzo, A., Greco, G., van der Aalst, W.M.P., Weijters, A.J.M.M.T., van Dongen, B.F., Saccà, D.: Process mining based on clustering: A quest for precision. In: ter Hofstede, A.H.M., Benatallah, B., Paik, H.-Y. (eds.) BPM Workshops 2007. LNCS, vol. 4928, pp. 17–29. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Metzger, A., Franklin, R., Engel, Y.: Predictive monitoring of heterogeneous service-oriented business networks: The transport and logistics case. In: SRII (2012)Google Scholar
  19. 19.
    Montali, M., Pesic, M., van der Aalst, W.M.P., Chesani, F., Mello, P., Storari, S.: Declarative specification and verification of service choreographiess. TWEB 4(1) (2010)Google Scholar
  20. 20.
    Pesic, M., van der Aalst, W.M.P.: A Declarative Approach for Flexible Business Processes Management. In: Eder, J., Dustdar, S. (eds.) BPM Workshops 2006. LNCS, vol. 4103, pp. 169–180. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Pesic, M., Schonenberg, H., van der Aalst, W.M.P.: Declare: Full support for loosely-structured processes. In: Proc. of EDOC, pp. 287–300 (2007)Google Scholar
  22. 22.
    Pika, A., van der Aalst, W.M.P., Fidge, C.J., ter Hofstede, A.H.M., Wynn, M.T.: Predicting deadline transgressions using event logs. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 211–216. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  23. 23.
    Pnueli, A.: The temporal logic of programs. In: SFCS, pp. 46–57 (1977)Google Scholar
  24. 24.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. M. Kaufmann Publishers Inc. (1993)Google Scholar
  25. 25.
    Rogge-Solti, A., Weske, M.: Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 389–403. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  26. 26.
    Santos, E.A.P., Francisco, R., Vieira, A.D.: F.R. Loures, E., Busetti, M.A.: Modeling business rules for supervisory control of process-aware information systems (2012)Google Scholar
  27. 27.
    Suriadi, S., Ouyang, C., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Root cause analysis with enriched process logs. In: La Rosa, M., Soffer, P. (eds.) BPM Workshops 2012. LNBIP, vol. 132, pp. 174–186. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  28. 28.
    Weidlich, M., Ziekow, H., Mendling, J., Günther, O., Weske, M., Desai, N.: Event-based monitoring of process execution violations. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 182–198. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  29. 29.
    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

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fabrizio Maria Maggi
    • 1
  • Chiara Di Francescomarino
    • 2
  • Marlon Dumas
    • 1
  • Chiara Ghidini
    • 2
  1. 1.University of TartuTartuEstonia
  2. 2.FBK-IRSTTrentoItaly

Personalised recommendations