Abstract
Next-activity prediction methods for business processes are always introduced in a static setting, implying a single training phase followed by the application of the learned model during the test phase. Real-life processes, however, are often dynamic and prone to changes over time. Therefore, all state-of-the-art methods need regular retraining on new data to be kept up to date. It is, however, not straightforward to determine when to retrain nor what data to use; for instance, should all historic data be included or only new data? Updating models that still perform at an acceptable level wastes a potentially large amount of computational resources while postponing an update too much will deteriorate model performance. In this paper, we present incremental learning strategies for updating these existing models that do not require fully retraining them, hence reducing the number of computational resources needed while still maintaining a more consistent and correct view of the process in its current form. We introduce a basic neural network method consisting of a single dense layer. This architecture makes it easier to perform fast updates to the model and enables us to perform more experiments. We investigate the differences between our proposed incremental approaches. Experiments performed with a prototype on real-life data show that these update strategies are a promising way forward to further increase the power and usability of state-of-the-art methods.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Van der Aalst, W.M., Schonenberg, M.H., Song, M.: Time prediction based on process mining. Inf. Syst. 36(2), 450–475 (2011)
Berti, A.: Improving process mining prediction results in processes that change over time. Data Anal. 2016, 49 (2016)
Bifet, A., Gavalda, R.: SIAM: learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining (SDM), pp. 443–448. SIAM (2007)
Bose, R.J.C., Van Der Aalst, W.M., Žliobaitė, I., Pechenizkiy, M.: Dealing with concept drifts in process mining. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 154–171 (2013)
Burattin, A., Carmona, J.: A framework for online conformance checking. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 165–177. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74030-0_12
Burattin, A., Cimitile, M., Maggi, F.M., Sperduti, A.: Online discovery of declarative process models from event streams. IEEE Trans. Serv. Comput. 8(6), 833–846 (2015). https://doi.org/10.1109/TSC.2015.2459703
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
Di Francescomarino, C., Ghidini, C., Maggi, F.M., Milani, F.: Predictive process monitoring methods: which one suits me best? In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds.) BPM 2018. LNCS, vol. 11080, pp. 462–479. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98648-7_27
Di Francescomarino, C., Ghidini, C., Maggi, F.M., Rizzi, W., Persia, C.D.: Incremental predictive process monitoring: How to deal with the variability of real environments. arXiv preprint arXiv:1804.03967 (2018)
Di Mauro, N., Appice, A., Basile, T.M.A.: Activity prediction of business process instances with inception CNN models. In: Alviano, M., Greco, G., Scarcello, F. (eds.) AI*IA 2019. LNCS (LNAI), vol. 11946, pp. 348–361. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35166-3_25
Domingos, P., Hulten, G.: Mining high-speed data streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)
van Dongen, B.: BPI challenge (2012). https://doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f
van Dongen, B.: BPI challenge (2015). https://doi.org/10.4121/uuid:31a308ef-c844-48da-948c-305d167a0ec1
van Dongen, B.: Real-life event logs - hospital log, March 2011. https://doi.org/10.4121/uuid:d9769f3d-0ab0-4fb8-803b-0d1120ffcf54
Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129–140 (2017)
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 1–37 (2014)
Gepperth, A., Hammer, B.: Incremental learning algorithms and applications. In: European Symposium on Artificial Neural Networks (ESANN) (2016)
Lin, L., Wen, L., Wang, J.: MM-PRED: a deep predictive model for multi-attribute event sequence. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 118–126. SIAM (2019)
Maisenbacher, M., Weidlich, M.: Handling concept drift in predictive process monitoring. SCC 17, 1–8 (2017)
McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: the sequential learning problem. In: Psychology of Learning and Motivation, vol. 24, pp. 109–165. Elsevier (1989)
Ostovar, A., Maaradji, A., La Rosa, M., ter Hofstede, A.H.M., van Dongen, B.F.V.: Detecting drift from event streams of unpredictable business processes. In: Comyn-Wattiau, I., Tanaka, K., Song, I.-Y., Yamamoto, S., Saeki, M. (eds.) ER 2016. LNCS, vol. 9974, pp. 330–346. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46397-1_26
Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Using convolutional neural networks for predictive process analytics. In: 2019 International Conference on Process Mining (ICPM), pp. 129–136. IEEE (2019)
Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Predictive process mining meets computer vision. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNBIP, vol. 392, pp. 176–192. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58638-6_11
Pauwels, S., Calders, T.: Bayesian network based predictions of business processes. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNBIP, vol. 392, pp. 159–175. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58638-6_10
Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook. Springer, Boston (2005). https://doi.org/10.1007/0-387-25465-X_15
Serrà Julià, J., Surís, D., Miron, M., Karatzoglou, A.: Overcoming catastrophic forgetting with hard attention to the task. In: Dy, J., Krause, A., (eds.) Proceedings of the 35th International Conference on Machine Learning (ICML 2018), 10–15 July 2018, Stockholmsmässan, Sweden [Massachusetts: PMLR; 2018], pp. 4548–4557. Proceedings of Machine Learning Research (2018)
Tax, N., Teinemaa, I., van Zelst, S.J.: An interdisciplinary comparison of sequence modeling methods for next-element prediction. Softw. Syst. Model. 19(6), 1345–1365 (2020)
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
Taymouri, F., Rosa, M.L., Erfani, S., Bozorgi, Z.D., Verenich, I.: Predictive business process monitoring via generative adversarial nets: the case of next event prediction. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds.) BPM 2020. LNCS, vol. 12168, pp. 237–256. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58666-9_14
Teinemaa, I., Dumas, M., Rosa, M.L., Maggi, F.M.: Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans. Knowl. Discov Data (TKDD) 13(2), 1–57 (2019)
Theis, J., Darabi, H.: Decay replay mining to predict next process events. IEEE Access 7, 119787–119803 (2019)
Verenich, I.: Helpdesk, mendeley data, v1 (2016). https://doi.org/10.17632/39bp3vv62t.1
Weinzierl, S., et al.: An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs. arXiv preprint arXiv:2005.01194 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Pauwels, S., Calders, T. (2021). Incremental Predictive Process Monitoring: The Next Activity Case. In: Polyvyanyy, A., Wynn, M.T., Van Looy, A., Reichert, M. (eds) Business Process Management. BPM 2021. Lecture Notes in Computer Science(), vol 12875. Springer, Cham. https://doi.org/10.1007/978-3-030-85469-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-85469-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-85468-3
Online ISBN: 978-3-030-85469-0
eBook Packages: Computer ScienceComputer Science (R0)