Abstract
Predictive process monitoring (PPM) offers multiple benefits for enterprises, e.g., the early planning of resources. The success of PPM-based actions depends on the prediction quality and the explainability of the prediction results. Both, prediction quality and explainability, can be influenced by unseen behavior, i.e., events that have not been observed in the training data so far. Unseen behavior can be caused by, for example, concept drift. Existing approaches are concerned with strategies on how to update the prediction model if unseen behavior occurs. What has not been investigated so far, is the question how unseen behavior itself can be predicted, comparable to approaches from machine learning such as zero-shot learning. Zero-shot learning predicts new classes in case of unavailable training data by exploiting context information. This work follows this idea and proposes an approach to predict unseen process behavior, i.e., unseen event labels, based on process event streams by exploiting compliance constraints as context information. This is reasonable as compliance constraints change frequently and are often the cause for concept drift. The approach employs state transition systems as prediction models in order to explain the effects of predicting unseen behavior. The approach also provides update strategies as the event stream evolves. All algorithms are prototypically implemented and tested on an artificial as well as real-world data set.
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
Notes
- 1.
- 2.
https://cpee.org, last access: 2023-03-21.
- 3.
References
van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011). https://doi.org/10.1016/j.is.2010.09.001
Brunk, J., Stierle, M., Papke, L., Revoredo, K., Matzner, M., Becker, J.: Cause vs. effect in context-sensitive prediction of business process instances. Inf. Syst. 95, 101635 (2021). https://doi.org/10.1016/j.is.2020.101635
Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: Processtransformer: predictive business process monitoring with transformer network. CoRR abs/2104.00721 (2021). https://arxiv.org/abs/2104.00721
Chamorro, A.E.M., Nepomuceno-Chamorro, I.A., Resinas, M., Ruiz-Cortés, A.: Updating prediction models for predictive process monitoring. In: Advanced Information Systems Engineering, pp. 304–318 (2022). https://doi.org/10.1007/978-3-031-07472-1_18
Chamorro, A.E.M., Revoredo, K., Resinas, M., del-Río-Ortega, A., Santoro, F.M., Ruiz-Cortés, A.: Context-aware process performance indicator prediction. IEEE Access 8, 222050–222063 (2020). https://doi.org/10.1109/ACCESS.2020.3044670
Ehrendorfer, M., Mangler, J., Rinderle-Ma, S.: Assessing the impact of context data on process outcomes during runtime. In: Hacid, H., Kao, O., Mecella, M., Moha, N., Paik, H. (eds.) ICSOC 2021. LNCS, vol. 13121, pp. 3–18. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91431-8_1
Folino, F., Guarascio, M., Pontieri, L.: Discovering context-aware models for predicting business process performances. In: Meersman, R., et al. (eds.) OTM 2012. LNCS, vol. 7565, pp. 287–304. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33606-5_18
Di Francescomarino, C., Ghidini, C., Maggi, F.M., Petrucci, G., Yeshchenko, A.: An eye into the future: leveraging a-priori knowledge in predictive business process monitoring. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 252–268. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_15
Francescomarino, C.D., Ghidini, C., Maggi, F.M., Rizzi, W., Persia, C.D.: Incremental predictive process monitoring: How to deal with the variability of real environments. CoRR abs/1804.03967 (2018). http://arxiv.org/abs/1804.03967
Hashmi, M., Governatori, G., Lam, H.-P., Wynn, M.T.: Are we done with business process compliance: state of the art and challenges ahead. Knowl. Inf. Syst. 57(1), 79–133 (2018). https://doi.org/10.1007/s10115-017-1142-1
Hüllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110(3), 457–506 (2021). https://doi.org/10.1007/s10994-021-05946-3
Ly, L.T., Maggi, F.M., Montali, M., Rinderle-Ma, S., van der Aalst, W.M.P.: Compliance monitoring in business processes: functionalities, application, and tool-support. Inf. Syst. 54, 209–234 (2015). https://doi.org/10.1016/j.is.2015.02.007
Maisenbacher, M., Weidlich, M.: Handling concept drift in predictive process monitoring. In: IEEE International Conference on Services Computing, pp. 1–8 (2017). https://doi.org/10.1109/SCC.2017.10
Mangat, A.S., Rinderle-Ma, S.: Next-activity prediction for non-stationary processes with unseen data variability. In: Almeida, J.P.A., Karastoyanova, D., Guizzardi, G., Montali, M., Maggi, F.M., Fonseca, C.M. (eds.) EDOC 2022. LNCS, vol. 13585, pp. 145–161. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-17604-3_9
Mangler, J., Rinderle-Ma, S.: Cloud process execution engine: architecture and interfaces (2022). https://doi.org/10.48550/ARXIV.2208.12214
Márquez-Chamorro, A.E., Resinas, M., Ruiz-Cortés, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. 11(6), 962–977 (2018). https://doi.org/10.1109/TSC.2017.2772256
Park, G., Benzin, J., van der Aalst, W.M.P.: Detecting context-aware deviations in process executions. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds.) BPM 2022. LNBIP, pp. 190–206. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16171-1_12
Pauwels, S., Calders, T.: Incremental predictive process monitoring: the next activity case. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds.) BPM 2021. LNCS, vol. 12875, pp. 123–140. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85469-0_10
Polato, M., Sperduti, A., Burattin, A., Leoni, M.: Time and activity sequence prediction of business process instances. Computing 100(9), 1005–1031 (2018). https://doi.org/10.1007/s00607-018-0593-x
Rizzi, W., Di Francescomarino, C., Ghidini, C., Maggi, F.M.: How do I update my model? On the resilience of Predictive Process Monitoring models to change. Knowl. Inf. Syst. (9), 1–32 (2022). https://doi.org/10.1007/s10115-022-01666-9
Stertz, F., Rinderle-Ma, S., Mangler, J.: Analyzing process concept drifts based on sensor event streams during runtime. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 202–219. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58666-9_12
Teinemaa, I., Dumas, M., Maggi, F.M., Di Francescomarino, C.: Predictive business process monitoring with structured and unstructured data. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 401–417. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45348-4_23
Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Discov. Data 13(2), 17:1-17:57 (2019). https://doi.org/10.1145/3301300
Verenich, I., Dumas, M., Rosa, M.L., Maggi, F.M., Teinemaa, I.: Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM Trans. Intell. Syst. Technol. 10(4), 34:1-34:34 (2019). https://doi.org/10.1145/3331449
Weinzierl, S., Revoredo, K.C., Matzner, M.: Predictive business process monitoringwith context information from documents. In: 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019, Stockholm and Uppsala, Sweden, June 8–14, 2019 (2019). https://aisel.aisnet.org/ecis2019_rip/59
Weytjens, H., Weerdt, J.D.: Learning uncertainty with artificial neural networks for predictive process monitoring. Appl. Soft Comput. 109134 (2022). https://doi.org/10.1016/j.asoc.2022.109134
Xian, Y., Lampert, C.H., Schiele, B., Akata, Z.: Zero-shot learning - a comprehensive evaluation of the good, the bad and the ugly. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2251–2265 (2019). https://doi.org/10.1109/TPAMI.2018.2857768
Yeshchenko, A., Durier, F., Revoredo, K., Mendling, J., Santoro, F.: Context-aware predictive process monitoring: the impact of news sentiment. In: Panetto, H., Debruyne, C., Proper, H.A., Ardagna, C.A., Roman, D., Meersman, R. (eds.) OTM 2018. LNCS, vol. 11229, pp. 586–603. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02610-3_33
Acknowledgements
This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project number 277991500.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, Q., Winter, K., Rinderle-Ma, S. (2023). Predicting Unseen Process Behavior Based on Context Information from Compliance Constraints. In: Di Francescomarino, C., Burattin, A., Janiesch, C., Sadiq, S. (eds) Business Process Management Forum. BPM 2023. Lecture Notes in Business Information Processing, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-41623-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-41623-1_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-41622-4
Online ISBN: 978-3-031-41623-1
eBook Packages: Computer ScienceComputer Science (R0)