Abstract
Predictive monitoring in business processes has gained attention in recent years. It uses a predictive model, learned from event logs, to predict the variables of interest for a running process instance (case). An example of such a variable considered here is the remaining time to complete the running case. Prediction usually relies on the properties of individual cases. Recently, the effects of the case’s environment, particularly cases that are executed in parallel to it, have been incorporated into prediction as inter-case properties. Furthermore, it has been recognized that, when different variants of the process exist, variant information should be considered by the predictive model. However, different prediction approaches use inter-case properties and variant information differently, and there is still no clear and agreed-upon manner in which these are considered for prediction. This paper proposes a conceptual framework that suggests categories of inter-case properties related to cases within a time window. Moreover, the framework considers the possible variant-awareness of these properties and suggests how variant information should be addressed in a predictive model. Reported experimentation supports our proposals.
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Notes
- 1.
The code can be found at https://github.com/avihaigr/ConcurrentFeaturesPrediction.
- 2.
The Bpic datasets were also evaluated with a window size of a day, but the better results were obtained with a window size of seven days.
References
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). https://doi.org/10.1007/978-3-642-13094-6_5
van der Aalst, W.M., Rosemann, M., Dumas, M.: Deadline-based escalation in process-aware information systems. Decis. Support Syst. 43(2), 492–511 (2007)
Bolt, A., van der Aalst, W.M.P., de Leoni, M.: Finding process variants in event logs. In: Panetto, H., et al. (eds.) OTM 2017. LNCS, vol. 10573, pp. 45–52. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69462-7_4
Buehler, R., Griffin, D., Ross, M.: Exploring the “planning fallacy”: why people underestimate their task completion times. J. Pers. Soc. Psychol. 67(3), 366–381 (1994)
Cabanillas, C., Di Ciccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 424–432. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10172-9_31
Conforti, R., de Leoni, M., La Rosa, M., van der Aalst, W.M., ter Hofstede, A.H.: A recommendation system for predicting risks across multiple business process instances. Decis. Support Syst. 69, 1–19 (2015)
De Leoni, M., van der Aalst, W.M., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016)
Di Francescomarino, C., Dumas, M., Maggi, F.M., Teinemaa, I.: Clustering-based predictive process monitoring. IEEE Trans. Serv. Comput. 12(6), 896–909 (2017)
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
Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129–140 (2017)
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
Ghattas, J., Peleg, M., Soffer, P., Denekamp, Y.: Learning the context of a clinical process. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 545–556. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12186-9_53
Ghattas, J., Soffer, P., Peleg, M.: Improving business process decision making based on past experience. Decis. Support Syst. 59, 93–107 (2014)
Kasekamp, T.A.: Web application to support researchers in predictive process monitoring tasks. Master thesis, University of Tartu (2018)
Klijn, E.L., Fahland, D.: Identifying and reducing errors in remaining time prediction due to inter-case dynamics. In: 2nd International Conference on Process Mining (ICPM) (2020)
Mannhardt, F., Arnesen, P., Landmark, A.D.: Estimating the impact of incidents on process delay. In: 2019 International Conference on Process Mining (ICPM), pp. 49–56. IEEE (2019)
Márquez-Chamorro, A.E., Resinas, M., Ruiz-Cortes, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. 11(6), 962–977 (2017)
Polato, M., Sperduti, A., Burattin, A., de 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
Senderovich, A., Di Francescomarino, C., Ghidini, C., Jorbina, K., Maggi, F.M.: Intra and inter-case features in predictive process monitoring: a tale of two dimensions. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 306–323. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65000-5_18
Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining for delay prediction in multi-class service processes. Inf. Syst. 53, 278–295 (2015)
Senderovich, A., Di Francescomarino, C., Maggi, F.M.: From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoring. Inf. Syst. 84, 255–264 (2019)
Soffer, P., Ghattas, J., Peleg, M.: A goal-based approach for learning in business processes. In: Nurcan, S., Salinesi, C., Souveyet, C., Ralyté, J. (eds.) Intentional Perspectives on Information Systems Engineering, pp. 239–256. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12544-7_13
Steenken, D., Voß, S., Stahlbock, R.: Container terminal operation and operations research-a classification and literature review. OR Spectr. 26(1), 3–49 (2004). https://doi.org/10.1007/s00291-003-0157-z
Tater, T., Dechu, S., Mani, S., Maurya, C.: Prediction of invoice payment status in account payable business process. In: Pahl, C., Vukovic, M., Yin, J., Yu, Q. (eds.) ICSOC 2018. LNCS, vol. 11236, pp. 165–180. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03596-9_11
Taymouri, F., La Rosa, M., Carmona, J.: Business process variant analysis based on mutual fingerprints of event logs. In: Dustdar, S., Yu, E., Salinesi, C., Rieu, D., Pant, V. (eds.) CAiSE 2020. LNCS, vol. 12127, pp. 299–318. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49435-3_19
Teinemaa, I., Dumas, M., La Rosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Discov. Data (TKDD) 13(2), 1–57 (2019)
Teinemaa, I., Dumas, M., Leontjeva, A., Maggi, F.M.: Temporal stability in predictive process monitoring. Data Min. Knowl. Discov. 32(5), 1306–1338 (2018). https://doi.org/10.1007/s10618-018-0575-9
Verenich, I., Dumas, M., La Rosa, M., Maggi, F.M., Teinemaa, I.: Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM Trans. Intell. Syst. Technol. (TIST) 10(4), 1–34 (2019)
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
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Grinvald, A., Soffer, P., Mokryn, O. (2021). Inter-case Properties and Process Variant Considerations in Time Prediction: A Conceptual Framework. In: Augusto, A., Gill, A., Nurcan, S., Reinhartz-Berger, I., Schmidt, R., Zdravkovic, J. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS EMMSAD 2021 2021. Lecture Notes in Business Information Processing, vol 421. Springer, Cham. https://doi.org/10.1007/978-3-030-79186-5_7
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