Abstract
In modern business processes, the amount of data collected has increased substantially in recent years. Because this data can potentially yield valuable insights, automated knowledge extraction based on process mining has been proposed, among other techniques, to provide users with intuitive access to the information contained therein. At present, the majority of technologies aim to reconstruct explicit business process models. These are directly interpretable but limited concerning the integration of diverse and real-valued information sources. On the other hand, Machine Learning (ML) benefits from the vast amount of data available and can deal with high-dimensional sources, yet it has rarely been applied to being used in processes. In this contribution, we evaluate the capability of modern Transformer architectures as well as more classical ML technologies of modeling process regularities, as can be quantitatively evaluated by their prediction capability. In addition, we demonstrate the capability of attentional properties and feature relevance determination by highlighting features that are crucial to the processes’ predictive abilities. We demonstrate the efficacy of our approach using five benchmark datasets and show that the ML models are capable of predicting critical outcomes and that the attention mechanisms or XAI components offer new insights into the underlying processes.
This research was supported by the research training group “Dataninja” (Trustworthy AI for Seamless Problem Solving: Next Generation Intelligence Joins Robust Data Analysis) funded by the German federal state of North Rhine-Westphalia, and supported by the European Commission Horizon for ICU4COVID project, and the VW-Foundation for the project IMPACT.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16, 1128–1142 (2004)
van der Aalst, W.: Process Mining: Data Science in Action. Springer, Heidelberg (2016). https://doi.org/gjnmht
Binder, A., Montavon, G., Bach, S., Müller, K., Samek, W.: Layer-wise relevance propagation for neural networks with local renormalization layers. CoRR abs/1604.00825 (2016)
Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Bukhsh, Z.A., Saeed, A., Dijkman, R.M.: ProcessTransformer: predictive business process monitoring with transformer network. CoRR (2021)
Dailey, D.: Netezza and IBM cloud PAK for data: a knockout combo for tough data. https://ibm.co/3xvK4MG. Accessed 17 June 2022
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)
van Dongen, B.: BPI Challenge 2017 (2017). https://doi.org/jcmn
van Dongen, B., Borchert, F.: BPI Challenge 2018 (2018). https://doi.org/jcmm
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv:2010.11929 (2020)
Evermann, J., Rehse, J.R., Fettke, P.: Predicting process behaviour using deep learning. Decis. Support Syst. 100, 129–140 (2017)
Fernández, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? JMLR 15(1), 3133–3181 (2014)
Harl, M., Weinzierl, S., Stierle, M., Matzner, M.: Explainable predictive business process monitoring using gated graph neural networks. J. Decis. Syst. 29(sup1), 312–327 (2020)
Hsieh, C., Moreira, C., Ouyang, C.: Dice4el: interpreting process predictions using a milestone-aware counterfactual approach. In: ICPM, pp. 88–95. IEEE (2021)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014)
de Leoni, M.M., Mannhardt, F.: Road traffic fine management process (2015). https://doi.org/jcmk
Louppe, G.: Understanding random forests: from theory to practice (2014). https://doi.org/jcms
Maggi, F.M., Di Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes (2013)
Mannhardt, F.: Hospital Billing - Event Log (8 2017). https://doi.org/gm85w4
Mehdiyev, N., Evermann, J., Fettke, P.: A novel business process prediction model using a deep learning method. Bus. Inf. Syst. Eng. 62(2), 143–157 (2020). https://doi.org/ggqt7z
Molnar, C.: Interpretable Machine Learning, 2 edn(2022). https://www.lulu.com
Munoz-Gama, J.: Conformance Checking and Diagnosis in Process Mining, vol. 270, Springer, Heidelberg (2016). https://doi.org/jkmp
Nguyen, A., et al.: Time matters: time-aware LSTMs for predictive business process monitoring. In: Leemans, S., Leopold, H. (eds.) ICPM 2020. LNBIP, vol. 406, pp. 112–123. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72693-5_9
Pasquadibisceglie, V., Appice, A., Castellano, G., Malerba, D.: Using convolutional neural networks for predictive process analytics. In: ICPM. IEEE (2019)
Pedregosa, F.: Scikit-learn: machine learning in python. JMLR 12, 2825–2830 (2011)
Pegoraro, M., Narayana, M.B.S., Benevento, E., van der Aalst, W.M.P., Martin, L., Marx, G.: Analyzing medical data with process mining: a COVID-19 case study. In: Abramowicz, W., Auer, S., Stróżyna, M. (eds.) BIS 2021. LNBIP, vol. 444, pp. 39–44. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-04216-4_4
Rama-Maneiro, E., Vidal, J., Lama, M.: Deep learning for predictive business process monitoring: review and benchmark. IEEE Trans. Serv. Comput. (2021)
Ribeiro, M.T., Singh, S., Guestrin, C.: why should I trust you?: explaining the predictions of any classifier. In: ACM SIGKDD, pp. 1135–1144 (2016)
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
Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. 58(1), 267–288 (1996). http://doi.org/gfn45m
van Dongen, B.F., de Medeiros, A.K.A., Verbeek, H.M.W., Weijters, A.J.M.M., van der Aalst, W.M.P.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) ICATPN 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005). https://doi.org/10.1007/11494744_25
Vaswani, A., et al.: Attention is all you need (2017)
Weytjens, H., De Weerdt, J.: Creating unbiased public benchmark datasets with data leakage prevention for predictive process monitoring (2021). https://doi.org/jcmp
Yasmin, F., Bukhsh, F., De Alencar Silva, P.: Process enhancement in process mining: a literature review. In: CEUR (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Velioglu, R., Göpfert, J.P., Artelt, A., Hammer, B. (2022). Explainable Artificial Intelligence for Improved Modeling of Processes. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-21753-1_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21752-4
Online ISBN: 978-3-031-21753-1
eBook Packages: Computer ScienceComputer Science (R0)