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

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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.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the [4tu] repository, [https://data.4tu.nl/]

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No.U1931207 and No.61702306), Sci. & Tech. Development Fund of Shandong Province of China (No.ZR2017BF015 and No.ZR2017MF027), the Humanities and Social Science Research Project of the Ministry of Education (No.18YJAZH017), Shandong Chongqing Science and technology cooperation project (No.cstc2020jscx-lyjsAX0008), Sci. & Tech. Development Fund of Qingdao (No.21-1-5-zlyj-1-zc), the Taishan Scholar Program of Shandong Province, SDUST Research Fund (No.2015TDJH102 and No.2019KJN024), and National Statistical Science Research Project in 2019 (No.2019LY49).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266510, China

    Rui Cao, Qingtian Zeng, Weijian Ni, Hua Duan, Faming Lu & Ziqi Zhao

  2. School of Computer Science and Technology, Shandong University of Technology, Zibo, 255000, China

    Cong Liu

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  1. Rui Cao
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Correspondence to Qingtian Zeng, Weijian Ni or Hua Duan.

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Business process remaining time prediction using explainable reachability graph from gated RNNs”.

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Cong Liu, Faming Lu and Ziqi Zhao contributed equally to this work.

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Cao, R., Zeng, Q., Ni, W. et al. Business process remaining time prediction using explainable reachability graph from gated RNNs. Appl Intell 53, 13178–13191 (2023). https://doi.org/10.1007/s10489-022-04192-x

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