Abstract
Predicting the final state of a running process, the remaining time to completion or the next activity of a running process are important aspects of runtime process management. Runtime management requires the ability to identify processes that are at risk of not meeting certain criteria in order to offer case managers decision information for timely intervention. This in turn requires accurate prediction models for process outcomes and for the next process event, based on runtime information available at the prediction and decision point. In this paper, we describe an initial application of deep learning with recurrent neural networks to the problem of predicting the next process event. This is both a novel method in process prediction, which has previously relied on explicit process models in the form of Hidden Markov Models (HMM) or annotated transition systems, and also a novel application for deep learning methods.
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References
Houy, C., Fettke, P., Loos, P., Aalst, W.M.P., Krogstie, J.: BPM-in-the-large – towards a higher level of abstraction in business process management. In: Janssen, M., Lamersdorf, W., Pries-Heje, J., Rosemann, M. (eds.) EGES/GISP -2010. IAICT, vol. 334, pp. 233–244. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15346-4_19
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015)
Sutskever, I., Martens, J., Hinton, G.E.: Generating text with recurrent neural networks. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011, pp. 1017–1024 (2011)
Graves, A.: Generating sequences with recurrent neural networks. CoRR abs/1308.0850 (2013)
Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. CoRR abs/1409.2329 (2014)
Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. SCI, vol. 385. Springer, New York (2012)
Dongen, B.F., Crooy, R.A., Aalst, W.M.P.: Cycle time prediction: When will this case finally be finished? In: Meersman, R., Tari, Z. (eds.) OTM 2008. LNCS, vol. 5331, pp. 319–336. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88871-0_22
Pandey, S., Nepal, S., Chen, S.: A test-bed for the evaluation of business process prediction techniques. In: 7th International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2011, Orlando, FL, USA, 15–18 October, 2011, pp. 382–391 (2011)
van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)
Folino, F., Guarascio, M., Pontieri, L.: Context-aware predictions on business processes: an ensemble-based solution. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) NFMCP 2012. LNCS (LNAI), vol. 7765, pp. 215–229. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37382-4_15
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). doi:10.1007/978-3-642-33606-5_18
Schwegmann, B., Matzner, M., Janiesch, C.: preCEP: facilitating predictive event-driven process analytics. In: Brocke, J., Hekkala, R., Ram, S., Rossi, M. (eds.) DESRIST 2013. LNCS, vol. 7939, pp. 448–455. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38827-9_36
Rogge-Solti, A., Weske, M.: Prediction of remaining service execution time using stochastic petri nets with arbitrary firing delays. In: Basu, S., Pautasso, C., Zhang, L., Fu, X. (eds.) ICSOC 2013. LNCS, vol. 8274, pp. 389–403. Springer, Heidelberg (2013). doi:10.1007/978-3-642-45005-1_27
Rogge-Solti, A., Weske, M.: Prediction of business process durations using non-markovian stochastic petri nets. Inf. Syst. 54, 1–14 (2015)
Bevacqua, A., Carnuccio, M., Folino, F., Guarascio, M., Pontieri, L.: A data-driven prediction framework for analyzing and monitoring business process performances. In: Hammoudi, S., Cordeiro, J., Maciaszek, L.A., Filipe, J. (eds.) ICEIS 2013. LNBIP, vol. 190, pp. 100–117. Springer, Cham (2014). doi:10.1007/978-3-319-09492-2_7
Bolt, A., Sepúlveda, M.: Process remaining time prediction using query catalogs. In: Lohmann, N., Song, M., Wohed, P. (eds.) BPM 2013. LNBIP, vol. 171, pp. 54–65. Springer, Cham (2014). doi:10.1007/978-3-319-06257-0_5
Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Data-aware remaining time prediction of business process instances. In: 2014 International Joint Conference on Neural Networks, IJCNN 2014, Beijing, China, July 6–11, 2014, pp. 816–823 (2014)
Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Time and activity sequence prediction of business process instances. CoRR abs/1602.07566 (2016)
Castellanos, M., Salazar, N., Casati, F., Dayal, U., Shan, M.-C.: Predictive business operations management. In: Bhalla, S. (ed.) DNIS 2005. LNCS, vol. 3433, pp. 1–14. Springer, Heidelberg (2005). doi:10.1007/978-3-540-31970-2_1
Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., Shan, M.: Business process intelligence. Comput. Ind. 53(3), 321–343 (2004)
Grigori, D., Casati, F., Dayal, U., Shan, M.: Improving business process quality through exception understanding, prediction, and prevention. In: VLDB 2001, Proceedings of 27th International Conference on Very Large Data Bases, September 11–14, 2001, Roma, Italy, pp. 159–168 (2001)
Conforti, R., Leoni, M., Rosa, M., Aalst, W.M.P.: Supporting risk-informed decisions during business process execution. In: Salinesi, C., Norrie, M.C., Pastor, Ó. (eds.) CAiSE 2013. LNCS, vol. 7908, pp. 116–132. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38709-8_8
Kang, B., Kim, D., Kang, S.: Periodic performance prediction for real-time business process monitoring. Ind. Manage. Data Syst. 112(1), 4–23 (2011)
Kang, B., Kim, D., Kang, S.: Real-time business process monitoring method for prediction of abnormal termination using knni-based LOF prediction. Expert Syst. Appl. 39(5), 6061–6068 (2012)
Maggi, F.M., Francescomarino, C.D., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. CoRR abs/1312.4874 (2013)
Maggi, F.M., Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: Jarke, M., Mylopoulos, J., Quix, C., Rolland, C., Manolopoulos, Y., Mouratidis, H., Horkoff, J. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 457–472. Springer, Cham (2014). doi:10.1007/978-3-319-07881-6_31
Leontjeva, A., Conforti, R., Francescomarino, C., Dumas, M., Maggi, F.M.: Complex symbolic sequence encodings for predictive monitoring of business processes. In: Motahari-Nezhad, H.R., Recker, J., Weidlich, M. (eds.) BPM 2015. LNCS, vol. 9253, pp. 297–313. Springer, Cham (2015). doi:10.1007/978-3-319-23063-4_21
Francescomarino, C., Dumas, M., Federici, M., Ghidini, C., Maggi, F.M., Rizzi, W.: Predictive business process monitoring framework with hyperparameter optimization. In: Nurcan, S., Soffer, P., Bajec, M., Eder, J. (eds.) CAiSE 2016. LNCS, vol. 9694, pp. 361–376. Springer, Cham (2016). doi:10.1007/978-3-319-39696-5_22
Metzger, A., Leitner, P., Ivanovic, D., Schmieders, E., Franklin, R., Carro, M., Dustdar, S., Pohl, K.: Comparing and combining predictive business process monitoring techniques. IEEE Trans. Syst. Man Cybern. Syst. 45(2), 276–290 (2015)
Folino, F., Guarascio, M., Pontieri, L.: A prediction framework for proactively monitoring aggregate process-performance indicators. In: 19th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2015, Adelaide, Australia, September 21–25, 2015, pp. 128–133 (2015)
Le, M., Gabrys, B., Nauck, D.: A hybrid model for business process event prediction. In: Proceedings of AI-2012, The Thirty-second SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, England, UK, December 11–13, 2012, pp. 179–192 (2012)
Lakshmanan, G.T., Shamsi, D., Doganata, Y.N., Unuvar, M., Khalaf, R.: A markov prediction model for data-driven semi-structured business processes. Knowl. Inf. Syst. 42(1), 97–126 (2015)
Unuvar, M., Lakshmanan, G.T., Doganata, Y.N.: Leveraging path information to generate predictions for parallel business processes. Knowl. Inf. Syst. 47(2), 433–461 (2016)
Ceci, M., Lanotte, P.F., Fumarola, F., Cavallo, D.P., Malerba, D.: Completion time and next activity prediction of processes using sequential pattern mining. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds.) DS 2014. LNCS (LNAI), vol. 8777, pp. 49–61. Springer, Cham (2014). doi:10.1007/978-3-319-11812-3_5
Breuker, D., Matzner, M., Delfmann, P., Becker, J.: Comprehensible predictive models for business processes. MIS Q. 40, 1009–1034 (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Sak, H., Senior, A.W., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: INTERSPEECH 2014, 15th Annual Conference of the International Speech Communication Association, Singapore, September 14–18, 2014, pp. 338–342 (2014)
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Evermann, J., Rehse, JR., Fettke, P. (2017). A Deep Learning Approach for Predicting Process Behaviour at Runtime. In: Dumas, M., Fantinato, M. (eds) Business Process Management Workshops. BPM 2016. Lecture Notes in Business Information Processing, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-319-58457-7_24
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