nach oben

Erschienen in:

2017 | OriginalPaper | Buchkapitel

Predictive Business Process Monitoring with LSTM Neural Networks

verfasst von : Niek Tax, Ilya Verenich, Marcello La Rosa, Marlon Dumas

Erschienen in: Advanced Information Systems Engineering

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relative accuracy is highly sensitive to the dataset at hand, thus requiring users to engage in trial-and-error and tuning when applying them in a specific setting. This paper investigates Long Short-Term Memory (LSTM) neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks. First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp. Next, we show how to use models for predicting the next task in order to predict the full continuation of a running case. Finally, we apply the same approach to predict the remaining time, and show that this approach outperforms existing tailor-made methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Leveraging Game-Tree Search for Robust Process Enactment

Nächstes Kapitel Searching Linked Data with a Twist of Serendipity

doi:10.17632/39bp3vv62t.1.

doi:10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f.

doi:10.4121/uuid:26aba40d-8b2d-435b-b5af-6d4bfbd7a270.

van der Aalst, W.M.P., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)CrossRef

Becker, J., Breuker, D., Delfmann, P., Matzner, M.: Designing and implementing a framework for event-based predictive modelling of business processes. In: Proceedings of the 6th International Workshop on Enterprise Modelling and Information Systems Architectures, pp. 71–84. Springer, Heidelberg (2014)

Breuker, D., Matzner, M., Delfmann, P., Becker, J.: Comprehensible predictive models for business processes. MIS Q. 40(4), 1009–1034 (2016)

Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)MathSciNetCrossRef

Chollet, F.: Keras (2015). https://github.com/fchollet/keras

Collobert, R., Weston, J.: A unified architecture for natural language processing: deep neural networks with multitask learning. In: ICML, pp. 160–167. ACM (2008)

Damerau, F.J.: A technique for computer detection and correction of spelling errors. Commun. ACM 7(3), 171–176 (1964)CrossRef

van Dongen, B.F., Crooy, R.A., van der 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 CrossRef

Evermann, J., Rehse, J.R., Fettke, P.: A deep learning approach for predicting process behaviour at runtime. In: Proceedings of the 1st International Workshop on Runtime Analysis of Process-Aware Information Systems. Springer, Rio de Janeiro (2016)

10.

Folino, F., Guarascio, M., Pontieri, L.: Discovering context-aware models for predicting business process performances. In: CoopIS, pp. 287–304 (2012)

11.

Francescomarino, C.D., 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

12.

Francescomarino, C.D., Dumas, M., Maggi, F.M., Teinemaa, I.: Clustering-based predictive process monitoring. CoRR (2015). http://arxiv.org/abs/1506.01428. to appear in Transactions on Services Computing

13.

Graves, A., Mohamed, A.R., Hinton, G.: Speech recognition with deep recurrent neural networks. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649. IEEE (2013)

14.

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef

15.

Kingma, D., Ba, J.: ADAM: a method for stochastic optimization. In: Proceedings of the 3rd International Conference for Learning Representations (2015)

16.

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)CrossRef

17.

LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRef

18.

de Leoni, M., van der Aalst, W.M.P., Dees, M.: A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf. Syst. 56, 235–257 (2016)CrossRef

19.

Leontjeva, A., Conforti, R., Francescomarino, C.D., 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 CrossRef

20.

Maggi, F.M., Francescomarino, C.D., 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

21.

Metzger, A., Franklin, R., Engel, Y.: Predictive monitoring of heterogeneous service-oriented business networks: the transport and logistics case. In: 2012 Annual SRII Global Conference, pp. 313–322. IEEE (2012)

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)CrossRef

23.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

24.

Pika, A., van der Aalst, W.M.P., Fidge, C.J., ter Hofstede, A.H.M., Wynn, M.T.: Predicting deadline transgressions using event logs. In: Rosa, M., Soffer, P. (eds.) BPM 2012. LNBIP, vol. 132, pp. 211–216. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36285-9_22 CrossRef

25.

Polato, M., Sperduti, A., Burattin, A., de Leoni, M.: Time and activity sequence prediction of business process instances. arXiv preprint (2016). arXiv:1602.07566

26.

Pravilovic, S., Appice, A., Malerba, D.: Process mining to forecast the future of running cases. In: Appice, A., Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z.W. (eds.) NFMCP 2013. LNCS, vol. 8399, pp. 67–81. Springer, Cham (2014). doi:10.1007/978-3-319-08407-7_5

27.

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 CrossRef

28.

Senderovich, A., Weidlich, M., Gal, A., Mandelbaum, A.: Queue mining - predicting delays in service processes. In: CAiSE, pp. 42–57 (2014)

29.

van der Spoel, S., van Keulen, M., Amrit, C.: Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital. In: Cudre-Mauroux, P., Ceravolo, P., Gašević, D. (eds.) SIMPDA 2012. LNBIP, vol. 162, pp. 60–83. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40919-6_4 CrossRef

Titel: Predictive Business Process Monitoring with LSTM Neural Networks
verfasst von: Niek Tax
Ilya Verenich
Marcello La Rosa
Marlon Dumas
Verlag: Springer International Publishing
Buch: Advanced Information Systems Engineering
Print ISBN: 978-3-319-59535-1

Electronic ISBN: 978-3-319-59536-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-59536-8_30

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner