Definitions
Predictive monitoring of business processes aims at predicting the future of an ongoing (uncomplete) process execution. Predictions related to the future of an ongoing process execution can pertain to numeric measures of interest (e.g., the completion time), to categorical outcomes (e.g., whether a given predicate will be fulfilled or violated), or to the sequence of future activities (and related payloads).
Overview
Predictive monitoring of business processes aims at providing predictions about the future of an ongoing (incomplete) process execution. The entry provides an overview of predictive process monitoring by introducing the dimensions that typically characterize existing approaches in the field, as well as using them to classify existing state-of-the-art approaches.
Introduction
Process mining deals with the analysis of business processes based on their behavior, observed and...
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Bevacqua A, Carnuccio M, Folino F, Guarascio M, Pontieri L (2013) A data-adaptive trace abstraction approach to the prediction of business process performances. In: Hammoudi S, Maciaszek LA, Cordeiro J, Dietz JLG (eds) ICEIS (1), SciTePress, pp 56–65
Cabanillas C, Di Ciccio C, Mendling J, Baumgrass A (2014) Predictive task monitoring for business processes. Springer International Publishing, Cham, pp 424–432. https://doi.org/10.1007/978-3-319-10172-9_31
Castellanos M, Salazar N, Casati F, Dayal U, Shan MC (2005) Predictive business operations management. Springer, Berlin/Heidelberg, pp 1–14. https://doi.org/10.1007/978-3-540-31970-2_1
Ceci M, Lanotte PF, Fumarola F, Cavallo DP, Malerba D (2014) Completion time and next activity prediction of processes using sequential pattern mining. Springer International Publishing, Cham, pp 49–61. https://doi.org/10.1007/978-3-319-11812-3_5
Cesario E, Folino F, Guarascio M, Pontieri L (2016) A cloud-based prediction framework for analyzing business process performances. Springer International Publishing, Cham, pp 63–80. https://doi.org/10.1007/978-3-319-45507-5_5
Conforti R, de Leoni M, La Rosa M, van der Aalst WMP (2013) Supporting risk-informed decisions during business process execution. In: Proceeding of CAiSE 2013. Springer, pp 116–132
Conforti R, de Leoni M, La Rosa M, van der Aalst WMP, ter Hofstede AHM (2015) A recommendation system for predicting risks across multiple business process instances. Decis Support Syst 69:1–19. https://doi.org/10.1016/j.dss.2014.10.006
Conforti R, Fink S, Manderscheid J, Röglinger M (2016) PRISM – a predictive risk monitoring approach for business processes. Springer International Publishing, Cham, pp 383–400. https://doi.org/10.1007/978-3-319-45348-4_22
de Leoni M, van der Aalst WMP, Dees M (2016) A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf Syst 56:235–257. https://doi.org/10.1016/j.is.2015.07.003
Di Francescomarino C, Dumas M, Federici M, Ghidini C, Maggi FM, Rizzi W (2016a) Predictive business process monitoring framework with hyperparameter optimization. In: Advanced information systems engineering – Proceedings of 28th international conference, CAiSE 2016, Ljubljana, 13–17 June 2016, pp 361–376. https://doi.org/10.1007/978-3-319-39696-5_22
Di Francescomarino C, Dumas M, Maggi FM, Teinemaa I (2016b) Clustering-based predictive process monitoring. IEEE Trans Serv Comput PP(99)
Di Francescomarino C, Ghidini C, Maggi FM, Petrucci G, Yeshchenko A (2017) An eye into the future: leveraging A-priori knowledge in predictive business process monitoring. Springer International Publishing, Cham, pp 252–268. https://doi.org/10.1007/978-3-319-65000-5_15
Evermann J, Rehse JR, Fettke P (2016) A deep learning approach for predicting process behaviour at runtime. In: PRAISE-2016
Evermann J, Rehse JR, Fettke P (2017) Predicting process behaviour using deep learning. Decis Support Syst. https://doi.org/10.1016/j.dss.2017.04.003
Folino F, Guarascio M, Pontieri L (2012) Discovering context-aware models for predicting business process performances. In: Proceeding of on the move to meaningful internet systems (OTM). Springer, pp 287–304
Folino F, Guarascio M, Pontieri L (2013) Discovering high-level performance models for ticket resolution processes. Springer, Berlin/Heidelberg, pp 275–282. https://doi.org/10.1007/978-3-642-41030-7_18
Jorbina K, Rozumnyi A, Verenich I, Di Francescomarino C, Dumas M, Ghidini C, Maggi FM, La Rosa M, Raboczi S (2017) Nirdizati: a web-based tool for predictive process monitoring. In: Proceedings of the BPM demo track and BPM dissertation award co-located with 15th international conference on business process modeling (BPM 2017), Barcelona, 13 Sept 2017
Kang B, Jung J, Cho NW, Kang S (2011) Real-time business process monitoring using formal concept analysis. Ind Manag Data Syst 111(5):652–674. https://doi.org/10.1108/02635571111137241
Kang B, Kim D, Kang SH (2012) Real-time business process monitoring method for prediction of abnormal termination using knni-based lof prediction. Expert Syst Appl 39(5):6061–6068. https://doi.org/10.1016/j.eswa.2011.12.007
Leitner P, Ferner J, Hummer W, Dustdar S (2013) Data-driven and automated prediction of service level agreement violations in service compositions. Distrib Parallel Databases 31(3):447–470. https://doi.org/10.1007/s10619-013-7125-7
Leontjeva A, Conforti R, Di Francescomarino C, Dumas M, Maggi FM (2015) Complex symbolic sequence encodings for predictive monitoring of business processes. In: BPM 2015. Springer International Publishing, pp 297–313
Ly LT, Rinderle-Ma S, Knuplesch D, Dadam P (2011) Monitoring business process compliance using compliance rule graphs. In: CoopIS, pp 82–99
Maggi FM, Montali M, Westergaard M, van der Aalst WMP (2011a) Monitoring business constraints with linear temporal logic: an approach based on colored automata. In: Proceeding of BPM 2011
Maggi FM, Westergaard M, Montali M, van der Aalst WMP (2011b) Runtime verification of LTL-based declarative process models. In: Proceeding of RV, vol 7186, pp 131–146
Maggi FM, Montali M, van der Aalst WMP (2012) An operational decision support framework for monitoring business constraints. In: FASE12
Maggi FM, Di Francescomarino C, Dumas M, Ghidini C (2014) Predictive monitoring of business processes. In: Advanced information systems engineering – Proceedings of 26th international conference, CAiSE 2014, Thessaloniki, 16–20 June 2014, pp 457–472
Márquez-Chamorro AE, Resinas M, Ruiz-Cortés A, Toro M (2017) Run-time prediction of business process indicators using evolutionary decision rules. Expert Syst Appl 87(Suppl C):1 – 14. https://doi.org/10.1016/j.eswa.2017.05.069
Metzger A, Leitner P, Ivanović D, Schmieders E, Franklin R, Carro M, Dustdar S, Pohl K (2015) Comparing and combining predictive business process monitoring techniques. IEEE Trans Syst Man Cybern Syst 45(2):276–290. https://doi.org/10.1109/TSMC.2014.2347265
Pandey S, Nepal S, Chen S (2011) A test-bed for the evaluation of business process prediction techniques. In: 7th international conference on collaborative computing: networking, applications and worksharing (CollaborateCom), pp 382–391. https://doi.org/10.4108/icst.collaboratecom.2011.247129
Pika A, van der Aalst WMP, Fidge CJ, ter Hofstede AHM, Wynn MT (2013a) Predicting deadline transgressions using event logs. Springer, Berlin/Heidelberg, pp 211–216. https://doi.org/10.1007/978-3-642-36285-9_22
Pika A, van der Aalst WMP, Fidge CJ, ter Hofstede AHM, Wynn MT (2013b) Profiling event logs to configure risk indicators for process delays. Springer, Berlin/Heidelberg, pp 465–481. https://doi.org/10.1007/978-3-642-38709-8_30
Polato M, Sperduti A, Burattin A, de Leoni M (2014) Data-aware remaining time prediction of business process instances. In: 2014 international joint conference on neural networks (IJCNN), pp 816–823. https://doi.org/10.1109/IJCNN.2014.6889360
Polato M, Sperduti A, Burattin A, de Leoni M (2018) Time and activity sequence prediction of business process instances. Computing. https://doi.org/10.1007/s00607-018-0593-x
Rogge-Solti A, Weske M (2013) Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: ICSOC 2013. Springer, pp 389–403
Rogge-Solti A, Weske M (2015) Prediction of business process durations using non-markovian stochastic petri nets. Inf Syst 54(Suppl C):1–14. https://doi.org/10.1016/j.is.2015.04.004
Senderovich A, Weidlich M, Gal A, Mandelbaum A (2015) Queue mining for delay prediction in multi-class service processes. Inf Syst 53:278–295. https://doi.org/10.1016/j.is.2015.03.010
Senderovich A, Di Francescomarino C, Ghidini C, Jorbina K, Maggi FM (2017) Intra and inter-case features in predictive process monitoring: a tale of two dimensions. Springer International Publishing, Cham, pp 306–323. https://doi.org/10.1007/978-3-319-65000-5_18
Tax N, Verenich I, La Rosa M, Dumas M (2017) Predictive business process monitoring with LSTM neural networks. In: Advanced information systems engineering – Proceedings of 29th international conference, CAiSE 2017, Essen, 12–16 June 2017, pp 477–492
Teinemaa I, Dumas M, Maggi FM, Di Francescomarino C (2016) Predictive business process monitoring with structured and unstructured data. In: BPM 2016, pp 401–417
Tu TBH, Song M (2016) Analysis and prediction cost of manufacturing process based on process mining. In: 2016 international conference on industrial engineering, management science and application (ICIMSA), pp 1–5. https://doi.org/10.1109/ICIMSA.2016.7503993
van der Aalst WMP, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 36(2):450–475
van Dongen BF, Crooy RA, van der Aalst WMP (2008) Cycle time prediction: when will this case finally be finished? Springer, Berlin/Heidelberg, pp 319–336. https://doi.org/10.1007/978-3-540-88871-0_22
Verenich I, Dumas M, La Rosa M, Maggi FM, Di Francescomarino C (2016) Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring. Springer International Publishing, Cham, pp 218–229. https://doi.org/10.1007/978-3-319-42887-1_18
Weidlich M, Ziekow H, Mendling J, Günter O, Weske M, Desai N (2011) Event-based monitoring of process execution violations. In: Proceeding of CAiSE
Zeng L, Lingenfelder C, Lei H, Chang H (2008) Event-driven quality of service prediction. Springer, Berlin/Heidelberg, pp 147–161. https://doi.org/10.1007/978-3-540-89652-4_14
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Francescomarino, C.D. (2019). Predictive Business Process Monitoring. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_105
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DOI: https://doi.org/10.1007/978-3-319-77525-8_105
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