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Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction

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Business Process Management (BPM 2020)

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Abstract

Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However, due to insufficient training data or sub-optimal network configuration and architecture, these approaches do not generalize well the problem at hand. This paper proposes a novel adversarial training framework to address this shortcoming, based on an adaptation of Generative Adversarial Networks (GANs) to the realm of sequential temporal data. The training works by putting one neural network against the other in a two-player game (hence the “adversarial” nature) which leads to predictions that are indistinguishable from the ground truth. We formally show that the worst-case accuracy of the proposed approach is at least equal to the accuracy achieved in non-adversarial settings. From the experimental evaluation it emerges that the approach systematically outperforms all baselines both in terms of accuracy and earliness of the prediction, despite using a simple network architecture and a naive feature encoding. Moreover, the approach is more robust, as its accuracy is not affected by fluctuations over the case length.

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Notes

  1. 1.

    https://data.4tu.nl/repository/collection:event_logs_real.

References

  1. Camargo, M., Dumas, M., González-Rojas, O.: Learning accurate LSTM models of business processes. In: Hildebrandt, T., van Dongen, B.F., Röglinger, M., Mendling, J. (eds.) BPM 2019. LNCS, vol. 11675, pp. 286–302. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-26619-6_19

    Chapter  Google Scholar 

  2. Evermann, J., Rehse, J.-R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Supp. Syst. 100, 129–140 (2017)

    Article  Google Scholar 

  3. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  4. Goodfellow, I., et al.: Generative adversarial nets. In: Proceedings of NIPS. ACM (2014)

    Google Scholar 

  5. Goodfellow, I.J.: Nips 2016 tutorial: generative adversarial networks. ArXiv (2017)

    Google Scholar 

  6. Goodfellow, I.J., Mirza, M., Da, X., Courville, A.C., Bengio, Y.: An empirical investigation of catastrophic forgeting in gradient-based neural networks. CoRR (2013)

    Google Scholar 

  7. Hinton, G.E., et al.: Deep neural networks for acoustic modeling in speech recognition. IEEE Sig. Process. Mag. 29, 82–97 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR (2014)

    Google Scholar 

  10. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Proceedings of NIPS. ACM (2012)

    Google Scholar 

  11. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In Proceedings of the IEEE. IEEE (1998)

    Google Scholar 

  12. Lin, L., Wen, L., Wang, J.: Mm-pred: a deep predictive model for multi-attribute event sequence. In: Proceedings of SDM. SIAM (2019)

    Google Scholar 

  13. Murphy, K.P.: Machine Learning - A Probabilistic Perspective. The MIT Press, Cambridge (2012)

    MATH  Google Scholar 

  14. Ng, A.Y., Jordan, M.I.: On discriminative vs. generative classifiers: a comparison of logistic regression and Naive Bayes. In: Proceedings of NIPS. ACM (2001)

    Google Scholar 

  15. Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. In: Proceedings of ICML. ACM (2012)

    Google Scholar 

  16. Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Using convolutional neural networks for predictive process analytics. In: Proceedings of ICPM. IEEE (2019)

    Google Scholar 

  17. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986)

    Article  Google Scholar 

  18. Tax, N., Verenich, I., La Rosa, M., Dumas, M.: Predictive business process monitoring with LSTM neural networks. In: Dubois, E., Pohl, K. (eds.) CAiSE 2017. LNCS, vol. 10253, pp. 477–492. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59536-8_30

    Chapter  Google Scholar 

  19. Teinemaa, I., Dumas, M., La Rosa, M., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. TKDD 13(2), 1–57 (2019)

    Article  Google Scholar 

  20. Teinemaa, I., Dumas, M., Leontjeva, A., Maggi, F.M.: Temporal stability in predictive process monitoring. Data Min. Knowl. Discov. 32(5), 1306–1338 (2018). https://doi.org/10.1007/s10618-018-0575-9

    Article  MathSciNet  Google Scholar 

  21. Verenich, I., Dumas, M., La Rosa, M., Maggi, F.M., Teinemaa, I.: Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring. ACM TIST 10(4), 1–34 (2019)

    Article  Google Scholar 

  22. Williams, R.J., Zipser, D.: Gradient-based learning algorithms for recurrent networks and their computational complexity (1995)

    Google Scholar 

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Acknowledgments

We thank Manuel Camargo and Vincenzo Pasquadibisceglie for providing access and instructions to use their tools. This research is partly funded by the Australian Research Council (DP180102839).

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Correspondence to Farbod Taymouri .

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Taymouri, F., Rosa, M.L., Erfani, S., Bozorgi, Z.D., Verenich, I. (2020). Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management. BPM 2020. Lecture Notes in Computer Science(), vol 12168. Springer, Cham. https://doi.org/10.1007/978-3-030-58666-9_14

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  • DOI: https://doi.org/10.1007/978-3-030-58666-9_14

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