Abstract
Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in running process instances. Even though organisations measure process performance by key performance indicators (KPIs), the DNN’s learning procedure is not directly affected by them. Therefore, the resulting next most likely activity predictions can be less beneficial in practice. Prescriptive business process monitoring (PrBPM) approaches assess predictions regarding their impact on the process performance (typically measured by KPIs) to prevent undesired process activities by raising alarms or recommending actions. However, none of these approaches recommends actual process activities as actions that are optimised according to a given KPI. We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI. Thereby, our technique uses business process simulation to ensure the control-flow conformance of the recommended actions. Based on our evaluation with two real-life event logs, we show that our technique’s next best actions can outperform next activity predictions regarding the optimisation of a KPI and the distance from the actual process instances.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We include the temporal control-flow attributes of Tax et al. [20] in the implementation, which are not described in this paper for better understanding.
- 2.
- 3.
- 4.
References
Bengio, Y., Simard, P., Frasconi, P., et al.: Learning long-term dependencies with gradient descent is difficult. Trans. Neural Netw. 5(2), 157–166 (1994)
Centobelli, P., Converso, G., Gallo, M., Murino, T., Santillo, L.C.: From process mining to process design: a simulation model to reduce conformance risk. Eng. Lett. 23(3), 145–155 (2015)
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). https://doi.org/10.1007/978-3-642-38709-8_8
Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F.: Predictive process monitoring methods: which one suits me best? In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 462–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_27
Fahrenkrog-Petersen, S.A., et al.: Fire now, fire later: alarm-based systems for prescriptive process monitoring. arXiv preprint arXiv:1905.09568 (2019)
Gröger, C., Schwarz, H., Mitschang, B.: Prescriptive analytics for recommendation-based business process optimization. In: Abramowicz, W., Kokkinaki, A. (eds.) BIS 2014. LNBIP, vol. 176, pp. 25–37. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06695-0_3
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)
Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_31
Márquez-Chamorro, A., Resinas, M., Ruiz-Cortás, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. (TSC) 11(6), 962–977 (2017). https://ieeexplore.ieee.org/document/8103817
Metzger, A., Föcker, F.: Predictive business process monitoring considering reliability estimates. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 445–460. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_28
Metzger, A., Franke, J., Jansen, T.: Data-driven deep learning for proactive terminal process management. In: Proceedings of the 17th International Conference on Business Process Management (BPM), pp. 196–211 (2019)
Metzger, A., Neubauer, A., Bohn, P., Pohl, K.: Proactive process adaptation using deep learning ensembles. In: Giorgini, P., Weber, B. (eds.) CAiSE 2019. LNCS, vol. 11483, pp. 547–562. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21290-2_34
Omohundro, S.M.: Five Balltree Construction Algorithms. International Computer Science Institute, Berkeley (1989)
Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Data-aware remaining time prediction of business process instances. In: Proceeding of the International Joint Conference on Neural Networks (IJCNN), pp. 816–823. IEEE (2014)
Redlich, D., Gilani, W.: Event-driven process-centric performance prediction via simulation. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 473–478. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28108-2_46
Rosenthal, K., Ternes, B., Strecker, S.: Business process simulation: a systematic literature review. In: Proceedings of the 26th European Conference on Information Systems (ECIS) (2018)
Rozinat, A., Wynn, M.T., van der Aalst, W.M., ter Hofstede, A.H., Fidge, C.J.: Workflow simulation for operational decision support. Data Knowl. Eng. 68(9), 834–850 (2009)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 477–492. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_30
Teinemaa, I., Tax, N., de Leoni, M., Dumas, M., Maggi, F.M.: Alarm-based prescriptive process monitoring. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNBIP, vol. 329, pp. 91–107. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98651-7_6
Tumay, K.: Business process simulation. In: Proceedings of the Winter Simulation Conference, pp. 93–98. ACM (1996)
van der Aalst, W.M.P.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4
Weinzierl, S., et al.: An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs. arXiv:2005.01194 (2020b)
Weinzierl, S., Stierle, M., Zilker, S., Matzner, M.: A next click recommender system for web-based service analytics with context-aware LSTMs. In: Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS) (2020)
Weinzierl, S., Zilker, S., Stierle, M., Park, G., Matzner, M.: From predictive to prescriptive process monitoring: recommending the next best actions instead of calculating the next most likely events. In: Proceedings of the 15th International Conference on Wirtschaftsinformatik. AISeL (2020c)
Wynn, M.T., Dumas, M., Fidge, C.J., ter Hofstede, A.H.M., van der Aalst, W.M.P.: Business process simulation for operational decision support. In: ter Hofstede, A., Benatallah, B., Paik, H.-Y. (eds.) BPM 2007. LNCS, vol. 4928, pp. 66–77. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78238-4_8
Acknowledgments
This project is funded by the German Federal Ministry of Education and Research (BMBF) within the framework programme Software Campus under the number 01IS17045.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Weinzierl, S., Dunzer, S., Zilker, S., Matzner, M. (2020). Prescriptive Business Process Monitoring for Recommending Next Best Actions. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management Forum. BPM 2020. Lecture Notes in Business Information Processing, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-58638-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-58638-6_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58637-9
Online ISBN: 978-3-030-58638-6
eBook Packages: Computer ScienceComputer Science (R0)