XNAP: Making LSTM-based next activity predictions explainable by using LRP
Weinzierl S, Zilker S, Brunk J, Revoredo K, Matzner M, Becker J (2020)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2020
Pages Range: 129-141
Conference Proceedings Title: Proceedings of the BPM 2020 International Workshops.
Event location: Sevilla
DOI: 10.1007/978-3-030-66498-5_10
Abstract
Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques' limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques' predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs.
Authors with CRIS profile
Sven Weinzierl
Lehrstuhl für Digital Industrial Service Systems
Sandra Zilker
Lehrstuhl für Digital Industrial Service Systems
Martin Matzner
Lehrstuhl für Digital Industrial Service Systems
Related research project(s)
How to cite
APA:
Weinzierl, S., Zilker, S., Brunk, J., Revoredo, K., Matzner, M., & Becker, J. (2020). XNAP: Making LSTM-based next activity predictions explainable by using LRP. In Proceedings of the BPM 2020 International Workshops. (pp. 129-141). Sevilla, ES.
MLA:
Weinzierl, Sven, et al. "XNAP: Making LSTM-based next activity predictions explainable by using LRP." Proceedings of the International Conference on Business Process Management, Sevilla 2020. 129-141.
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