Volume 32 Issue 3
May  2023
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CAO Rui, ZENG Qingtian, NI Weijian, et al., “Explainable Business Process Remaining Time Prediction Using Reachability Graph,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 625-639, 2023, doi: 10.23919/cje.2021.00.170
Citation: CAO Rui, ZENG Qingtian, NI Weijian, et al., “Explainable Business Process Remaining Time Prediction Using Reachability Graph,” Chinese Journal of Electronics, vol. 32, no. 3, pp. 625-639, 2023, doi: 10.23919/cje.2021.00.170

Explainable Business Process Remaining Time Prediction Using Reachability Graph

doi: 10.23919/cje.2021.00.170
Funds:  This work was supported by the National Natural Science Foundation of China (U1931207, 61702306), Sci. & Tech. Development Fund of Shandong Province of China (ZR2017BF015, ZR2017MF027), the Humanities and Social Science Research Project of the Ministry of Education (18YJAZH017), Shandong Chongqing Science and Technology Cooperation Project (cstc2020jscx-lyjsAX0008), Sci. & Tech. Development Fund of Qingdao (21-1-5-zlyj-1-zc), the Shandong Postgraduate Education Quality Improvement Plan (SDYJG19075), Shandong Education Teaching Research Key Project (2021JXZ010), National Statistical Science Research Project (2021LY053), the Taishan Scholar Program of Shandong Province, SDUST Research Fund (2015TDJH102, 2019KJN024), and National Statistical Science Research Project in 2019 (2019LY49)
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  • Author Bio:

    Rui CAO received the B.E. degree in College of Mathematics and Big Data, Anhui University of Science and Technology. She is a Ph.D. candidate of the College of Computer Science and Engineering, Shandong University of Science and Technology. Her research interests include Petri nets, process mining, and deep learning. (Email: ruicaoqing@163.com)

    Qingtian ZENG (corresponding author) received the Ph.D. degree in computer software and theory from the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, in 2005. He was a Visiting Professor at the City University of Hong Kong, Kowloon, Hong Kong, in 2008. He is currently a Professor with Shandong University of Science and Technology, Qingdao, China. His current research interests include Petri nets, process mining, and knowledge management. (Email: qtzeng@sdust.edu.cn)

    Weijian NI received the Ph.D. degree in computer science and technology from Nankai University, Tianjin, China, in 2008. He was a Visiting Scholar at the State University of New York, New York, USA, in 2015. He is currently a Professor with Shandong University of Science and Technology, Qingdao, China. His current research interests include process mining, deep learning, and text mining.(Email: niwj@foxmail.com)

    Faming LU received the Ph.D. degree in computer software and theory from Shandong University of Science and Technology, Qingdao, China, in 2013. He is currently a Associate Professor with Shandong University of Science and Technology, Qingdao, China. His current research interests include Petri nets, process mining, and machine learning. (Email: fm_lu@163.com)

    Cong LIU received the Ph.D. degree in the Department of Mathematics and Computer Science, Section of Information Systems (IS), Eindhoven University of Technology, Eindhoven, The Netherlands, in 2019. He is currently a Professor with the Shandong University of Technology, Zibo, China. His current research interests include Petri nets, process mining, and software engineering. (Email: liucongchina@sdust.edu.cn)

    Hua DUAN received the Ph.D. degree in applied mathematics from Shanghai Jiaotong University, Shanghai, China, in 2008. She is currently a Professor with Shandong University of Science and Technology, Qingdao, China. Her current research interests include Petri nets, process mining, and machine learning. (Email: huaduan59@163.com)

  • Received Date: 2021-05-15
  • Accepted Date: 2021-12-01
  • Available Online: 2022-03-18
  • Publish Date: 2023-05-05
  • With the recent advances in the field of deep learning, an increasing number of deep neural networks have been applied to business process prediction tasks, remaining time prediction, to obtain more accurate predictive results. However, existing time prediction methods based on deep learning have poor interpretability, an explainable business process remaining time prediction method is proposed using reachability graph, which consists of prediction model construction and visualization. For prediction models, a Petri net is mined and the reachability graph is constructed to obtain the transition occurrence vector. Then, prefixes and corresponding suffixes are generated to cluster into different transition partitions according to transition occurrence vector. Next, the bidirectional recurrent neural network with attention is applied to each transition partition to encode the prefixes, and the deep transfer learning between different transition partitions is performed. For the visualization of prediction models, the evaluation values are added to the sub-processes of a Petri net to realize the visualization of the prediction models. Finally, the proposed method is validated by publicly available event logs.
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