Text-Aware Predictive Monitoring of Business Processes

Authors

DOI:

https://doi.org/10.52825/bis.v1i.62

Keywords:

Predictive Monitoring, Process Mining, Natural Language Processing, LSTM Neural Networks

Abstract

The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. Experiments show that the text-aware model is able to outperform state-of-the-art process prediction methods on simulated and real-world event logs containing textual data.

Downloads

Download data is not yet available.

References

van der Aalst, Wil M.P., M. Helen Schonenberg, and Minseok Song. ”Time prediction based on process mining.” Information Systems 36.2 (2011): 450-475.

Abadi, Mart´ın, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin et al. ”Tensorflow: A system for large-scale machine learning.” 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). 2016.

Berti, Alessandro, Sebastiaan J. van Zelst, and Wil M.P. van der Aalst. ”Process Mining forPython (PM4Py): Bridging the Gap Between Process-and Data Science.” In International Conference on Process Mining (ICPM) Demo Track (CEUR 2374). pp. 13–16 (2019)

Blei, David M., Andrew Y. Ng, and Michael I. Jordan. ”Latent Dirichlet Allocation.” The Journal of Machine Learning Research 3 (2003): 993-1022.

Brown, Peter F., Vincent J. Della Pietra, Peter V. Desouza, Jennifer C. Lai, and Robert L. Mercer. ”Class-based n-gram models of natural language.” Computational linguistics 18.4 (1992): 467-480.

Ceci, Michelangelo, Pasqua Fabiana Lanotte, Fabio Fumarola, Dario Pietro Cavallo, and Donato Malerba. ”Completion time and next activity prediction of processes using sequential pattern mining.” In International Conference on Discovery Science, pp. 49-61. Springer, Cham, 2014.

Evermann, Joerg, Jana-Rebecca Rehse, and Peter Fettke. ”A deep learning approach for predicting process behaviour at runtime.” In International Conference on Business Process Management, pp. 327-338. Springer, Cham, 2016.

Le, Quoc, and Tomas Mikolov. ”Distributed representations of sentences and documents.” In International Conference on Machine Learning, pp. 1188-1196. PMLR, 2014.

Navarin, Nicol ` o, Beatrice Vincenzi, Mirko Polato, and Alessandro Sperduti. ”LSTM networks for data-aware remaining time prediction of business process instances.” In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-7. IEEE, 2017.

Park, Gyunam, and Minseok Song. ”Predicting performances in business processes using deep neural networks.” Decision Support Systems 129 (2020): 113191.

Pedregosa, Fabian, Ga¨ el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel et al. ”Scikit-learn: Machine learning in Python.” the Journal of Machine Learning Research 12 (2011): 2825-2830.

Polato, Mirko, Alessandro Sperduti, Andrea Burattin, and Massimiliano de Leoni. ”Time and activity sequence prediction of business process instances.” Computing 100.9 (2018): 1005-1031.

Rogge-Solti, Andreas, and Mathias Weske. ”Prediction of remaining service execution time using stochastic Petri nets with arbitrary firing delays.” In International Conference on Service-Oriented Computing, pp. 389-403. Springer, Berlin, Heidelberg, 2013.

Tax, Niek, Irene Teinemaa, and Sebastiaan J. van Zelst. ”An interdisciplinary comparison of sequence modeling methods for next-element prediction.” Software and Systems Modeling 19.6 (2020): 1345-1365.

Tax, Niek, Ilya Verenich, Marcello La Rosa, and Marlon Dumas. ”Predictive business process monitoring with LSTM neural networks.” In International Conference on Advanced Information Systems Engineering, pp. 477-492. Springer, Cham, 2017.

Teinemaa, Irene, Marlon Dumas, Fabrizio Maria Maggi, and Chiara Di Francescomarino. ”Predictive business process monitoring with structured and unstructured data.” In International Conference on Business Process Management, pp. 401-417. Springer, Cham,, 2016.

Uysal, Merih Seran, Sebastiaan J. van Zelst and Tobias Brockhoff and Anahita Farhang Ghahfarokhi and Mahsa Pourbafrani and Ruben Schumacher and Sebastian Junglas and Günther Schuh and Wil M.P. van der Aalst. ”Process Mining for Production Processes in the Automotive Industry” In Industry Forum at BPM 2020 co-located with International Conference on Business Process Management, Springer, 2020.

Published

2021-07-02