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Business process remaining time prediction using explainable reachability graph from gated RNNs

Business process remaining time prediction using explainable reachability graph from gated RNNs Gated recurrent neural networks (RNNs) are successfully applied to predict the remaining time of business processes. Existing methods typically train multiple prediction models for prefixes bucketing. Furthermore, the gated RNNs are more like black boxes and lack interpretability. An explainable gated RNN using a reachability graph is proposed to improve the results of prediction. First, prefixes of the event log are generated to train a single prediction model, and hidden states of the gated RNN are saved. Second, a Petri net and its corresponding reachability graph are constructed by taking an event log as input. Next, the hidden states of the gated RNN are mapped to a reachability state of the reachability graph by the decoding mapping to explain the remaining time prediction model, i.e., gated RNN. Finally, our method is validated by six real-life event logs. Based on the experimental results, it is shown that a mapping from the hidden states of the gated RNN to a reachability state of the reachability graph is established, and the gated RNN that recognizes transition sequences is also explained for improving the performance of remaining time prediction of business processes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Business process remaining time prediction using explainable reachability graph from gated RNNs

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References (40)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
0924-669X
eISSN
1573-7497
DOI
10.1007/s10489-022-04192-x
Publisher site
See Article on Publisher Site

Abstract

Gated recurrent neural networks (RNNs) are successfully applied to predict the remaining time of business processes. Existing methods typically train multiple prediction models for prefixes bucketing. Furthermore, the gated RNNs are more like black boxes and lack interpretability. An explainable gated RNN using a reachability graph is proposed to improve the results of prediction. First, prefixes of the event log are generated to train a single prediction model, and hidden states of the gated RNN are saved. Second, a Petri net and its corresponding reachability graph are constructed by taking an event log as input. Next, the hidden states of the gated RNN are mapped to a reachability state of the reachability graph by the decoding mapping to explain the remaining time prediction model, i.e., gated RNN. Finally, our method is validated by six real-life event logs. Based on the experimental results, it is shown that a mapping from the hidden states of the gated RNN to a reachability state of the reachability graph is established, and the gated RNN that recognizes transition sequences is also explained for improving the performance of remaining time prediction of business processes.

Journal

Applied Intelligence – Springer Journals

Published: Jun 1, 2023

Keywords: Business process; Petri net; RNN; Remaining time prediction; Interpretability

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