Prediction of Remaining Service Execution Time Using Stochastic Petri Nets with Arbitrary Firing Delays
Companies realize their services by business processes to stay competitive in a dynamic market environment. In particular, they track the current state of the process to detect undesired deviations, to provide customers with predicted remaining durations, and to improve the ability to schedule resources accordingly. In this setting, we propose an approach to predict remaining process execution time, taking into account passed time since the last observed event.
While existing approaches update predictions only upon event arrival and subtract elapsed time from the latest predictions, our method also considers expected events that have not yet occurred, resulting in better prediction quality. Moreover, the prediction approach is based on the Petri net formalism and is able to model concurrency appropriately. We present the algorithm and its implementation in ProM and compare its predictive performance to state-of-the-art approaches in simulated experiments and in an industry case study.
Keywordsbusiness process performance remaining time prediction stochastic Petri nets generally distributed durations conditional probability
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- 2.van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer (2011)Google Scholar
- 5.Agrawal, R., Gunopulos, D., Leymann, F.: Mining process models from workflow logs. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 469–483. Springer, Heidelberg (1998)Google Scholar
- 11.Härdle, W.: Applied nonparametric regression. Cambridge University Press (1990)Google Scholar
- 14.Leitner, P., Wetzstein, B., Rosenberg, F., Michlmayr, A., Dustdar, S., Leymann, F.: Runtime prediction of service level agreement violations for composite services. In: Dan, A., Gittler, F., Toumani, F. (eds.) ICSOC/ServiceWave 2009. LNCS, vol. 6275, pp. 176–186. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 19.Rogge-Solti, A., van der Aalst, W.M.P., Weske, M.: Discovering stochastic Petri nets with arbitrary delay distributions from event logs. In: BPM Workshops. Springer, Heigelberg (to appear)Google Scholar
- 22.Weske, M.: Business Process Management: Concepts, Languages, Architectures, 2nd edn. Springer (2012)Google Scholar
- 25.Zimmermann, A.: Modeling and evaluation of stochastic Petri nets with TimeNET 4.1. In: 2012 6th International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS), pp. 54–63. IEEE (2012)Google Scholar