The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to timely counteract to undesired situations, in order to prevent losses. Therefore, the ability to accurately predict future features of running business process instances would be a very helpful aid when managing processes, especially under service level agreement constraints. However, making such accurate forecasts is not easy: many factors may influence the predicted features. Many approaches have been proposed to cope with this problem but, generally, they assume that the underlying process is stationary. However, in real cases this assumption is not always true. In this work we present new methods for predicting the remaining time of running cases. In particular we propose a method, assuming process stationarity, which achieves state-of-the-art performances and two other methods which are able to make predictions even with non-stationary processes. We also describe an approach able to predict the full sequence of activities that a running case is going to take. All these methods are extensively evaluated on different real case studies.
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We assume this representation according to the Unix epoch time.
We assume that a fixed order is always available for attribute’s values (for example, the lexicographical order).
Source code available at https://www.dropbox.com/s/6ps93qmc81clz74/Polato_etal_Prediction_plugin_prom.zip
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De Leoni and Mannhardt (2015) Road traffic fine management process. doi:10.4121/ uuid:270fd440-1057-4fb9-89a9-b699b47990f5
Polato (2017) Ticketing. doi:10.4121/uuid:0c60edf1-6f83-4e75-9367-4c63b3e9d5b
Basak D, Pal S, Patranabis DC (2007) Support vector regression. Neural Inf Process Lett Rev 10(10):203–224
Bolch G, Greiner S, de Meer H, Trivedi KS (2006) Queueing networks and Markov chains: modeling and performance evaluation with computer science applications. Wiley, New York
Burattin A (2015) Process mining techniques in business environments. Springer International Publishing, Heidelberg
Ceci M, Lanotte PF, Fumarola F, Cavallo DP, Malerba D (2014) Completion time and next activity prediction of processes using sequential pattern mining. In: DS 2014, pp 49–61
Damerau F (1964) A technique for computer detection and correction of spelling errors. Commun ACM 7(3):171–176
Domhan T, Springenberg JT, Hutter F (2015) Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves, IJCAI’15. AAAI Press, Menlo Park, pp 3460–3468
Drucker H, Burges C, Kaufman L, Smola AJ, Vapnik V (1996) Support vector regression machines. Neural Inf Process Syst 1:155–161
Evermann J, Rehse JR, Fettke P (2017) A deep learning approach for predicting process behaviour at runtime. Springer International Publishing, New York, pp 327–338
Folino F, Guarascio M, Pontieri L (2012) Discovering context-aware models for predicting business process performances. In: OTM 2012, 7565, pp 287–304
Folino F, Guarascio M, Pontieri L (2013) Discovering high-level performance models for ticket resolution Processes. In: OTM 2013, 8185, pp 275–282
Francescomarino CD, Dumas M, Maggi FM, Teinemaa I (2017) Clustering-based predictive process monitoring. In: IEEE transactions on services computing, vol PP
Ghattas J, Soffer P, Peleg M (2014) Improving business process decision making based on past experience. Decis Support Syst 59:93–107
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software. ACM SIGKDD Explor Newslett 11(1):10–18
Hall RW (1990) Queueing methods for services and manufacturing
IEEE Task Force on Process Mining (2012) Process mining manifesto. In: Daniel F, Barkaoui K, Dustdar S (eds) Business process management workshops, vol 99. Lecture notes in business information processing. Springer, Berlin, pp 169–194
Lakshmanan GT, Shamsi D, Doganata YN, Unuvar M, Khalaf RA (2013) Markov prediction model for data-driven semi-structured business processes. Knowledge and information systems
Leitner P, Wetzstein B, Rosenberg F, Michlmayr A, Dustdar S, Leymann F (2009) Runtime prediction of service level agreement violations for composite services. In: International workshops, ICSOC/ServiceWave. Springer, New York, pp 176–186
Leoni MD, Aalst WMPVD, Dees MA (2014) General framework for correlating business process characteristics. In: Business process management, pp 250–266
Leontjeva A, Conforti R, Di Francescomarino C, Dumas M, Maggi FM (2015) Complex symbolic sequence encodings for predictive monitoring of business processes. In: BPM
Maggi F.M, Di Francescomarino C, Dumas M, Ghidini C (2014) Predictive monitoring of business processes. Springer International Publishing, Cham, pp 457–472
Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval, 1st edn. Cambrige University Press, Cambrige
Mitchell TM (1997) Machine learning, 1st edn. McGraw-Hill, Maidenhead
Tax IN, Verenich MLR, Dumas M (2017) Predictive business process monitoring with lstm neural networks. In: CAiSE
Pandey S, Nepal S, Chen S (2011) A test-bed for the evaluation of business process prediction techniques. In: 7th ICCC: networking, applications and worksharing (CollaborateCom), pp 382–391
Polato M, Sperduti A, Burattin A, de Leoni M (2014) Data-aware remaining time prediction of business process instances. In: IJCNN (WCCI)
Rogge-Solti A, Weske M (2015) Prediction of business process durations using non-markovian stochastic petri nets. Inf Syst 54:1–14
Senderovich A, Weidlich M, Gal A, Mandelbaum A (2015) Queue mining for delay prediction in multi-class service processes. Inf Syst 53:278–295
Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222
Teinemaa I, Dumas M, Maggi FM, Di Francescomarino C (2016) Predictive business process monitoring with structured and unstructured data. In: La Rosa M, Loos P, Pastor O (eds) BPM 2016. Springer International Publishing, pp 401–417
van der Spoel S, van Keulen M, Amrit C (2013) Process prediction in noisy data sets: a case study in a dutch hospital. Springer, Berlin, pp 60–83
van der Aalst WMP (2011) Process mining–discovery, conformance and enhancement of business processes, 1st edn. Springer, Heidelberg
van der Aalst WMP, Rubin V, Verbeek E, van Dongen BF, Kindler E, Günther CW (2008) Process mining: a two-step approach to balance between underfitting and overfitting. Softw Syst Model 9(1):87–111
van der Aalst WMP, Schonenberg H, Song M (2011) Time prediction based on process mining. Inf Syst 36(2):450–475
Verbeek E, Buijs JCAM, van Dongen BF, van der Aalst WMP (2010) ProM 6: the process mining toolkit. In: BPM 2010 Demos. Springer, New York, pp. 34–39
Verenich I, Dumas M, Rosa ML, Maggi FM, Francescomarino CD (2016) Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring. In: 11th International workshop on BPI 2015. Springer, pp. 218–229
The work reported in this paper is supported by the Eurostars-Eureka project PROMPT (E!6696).
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Polato, M., Sperduti, A., Burattin, A. et al. Time and activity sequence prediction of business process instances. Computing 100, 1005–1031 (2018). https://doi.org/10.1007/s00607-018-0593-x
- Process mining
- Remaining time
- Machine learning
Mathematics Subject Classification