Abstract
Process mining provides techniques to extract process-centric knowledge from event data available in information systems. These techniques have been successfully adopted to solve process-related problems in diverse industries. In recent years, the attention of the process mining discipline has shifted to supporting continuous process management and actual process improvement. To this end, techniques for operational support, including predictive process monitoring, have been actively studied to monitor and influence running cases. However, the conversion from insightful diagnostics to actual actions is still left to the user (i.e., the “action part” is missing and outside the scope of today’s process mining tools). In this paper, we propose a general framework for action-oriented process mining that supports the continuous management of operational processes and the automated execution of actions to improve the process. As proof of concept, the framework is implemented in ProM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aalst, W.: Data science in action. Process Mining, pp. 3–23. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-49851-4_1
Reinkemeyer, L. (ed.): Process Mining in Action. Principles, Use Cases and Outlook. Springer, Heidelberg (2020). https://doi.org/10.1007/978-3-030-40172-6
de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016)
Marquez-Chamorro, A.E., Resinas, M., Ruiz-Cortes, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. 11(6), 962–977 (2018)
Aalst, W.M.P.: Object-centric process mining: dealing with divergence and convergence in event data. In: Ölveczky, P.C., Salaün, G. (eds.) SEFM 2019. LNCS, vol. 11724, pp. 3–25. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30446-1_1
Carmona, J., van Dongen, B.F., Solti, A., Weidlich, M.: Conformance Checking - Relating Processes and Models. Springer, Switzerland (2018). https://doi.org/10.1007/978-3-319-99414-7
Ramezani, E., Fahland, D., van der Aalst, W.M.P.: Where did i misbehave? Diagnostic information in compliance checking. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 262–278. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32885-5_21
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., Tari, Z. (eds.) OTM 2005. LNCS, vol. 3760, pp. 130–147. Springer, Heidelberg (2005). https://doi.org/10.1007/11575771_11
Bezerra, F., Wainer, J.: Algorithms for anomaly detection of traces in logs of process aware information systems. Inf. Syst. 38(1), 33–44 (2013)
Ghionna, L., Greco, G., Guzzo, A., Pontieri, L.: Outlier detection techniques for process mining applications. In: An, A., Matwin, S., Raś, Z.W., Slezak, D. (eds.) ISMIS 2008. LNCS (LNAI), vol. 4994, pp. 150–159. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68123-6_17
Badakhshan, P., Bernhart, G., Geyer-Klingeberg, J., Nakladal, J., Schenk, S., Vogelgesang, T.: The action engine - turning process insights into action. In: ICPM Demo Track. Aachen, Germany, ceur-ws 2019, pp. 28–31 (2019)
Conforti, R., de Leoni, M., La Rosa, M., van der Aalst, W.M.P., ter Hofstede, A.H.: A recommendation system for predicting risks across multiple business process instances. Decis. Support Syst. 69, 1–19 (2015)
Fahrenkrog-Petersen, S.A., et al.: Fire now, fire later: alarm-based systems for prescriptive process monitoring. arXiv:1905.09568 [cs, stat] (2019)
Agostinelli, S., Marrella, A., Mecella, M.: Towards intelligent robotic process automation for BPMers. arXiv:2001.00804 [cs] (2020)
Acknowledgements
We thank the Alexander von Humboldt (AvH) Stiftung for supporting our research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Park, G., van der Aalst, W.M.P. (2020). A General Framework for Action-Oriented Process Mining. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds) Business Process Management Workshops. BPM 2020. Lecture Notes in Business Information Processing, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-66498-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-66498-5_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66497-8
Online ISBN: 978-3-030-66498-5
eBook Packages: Computer ScienceComputer Science (R0)