Explainable Business Process Remaining Time Prediction Using Reachability Graph
作者机构：College of Computer Science and Engineering Shandong University of Science and Technology School of Computer Science and Technology Shandong University of Technology
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2023年第32卷第3期
页 面：625-639页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：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) National Statistical Science Research Project in 2019 (2019LY49)
主 题：Deep learning Training Visualization Recurrent neural networks Petri nets Transfer learning Process control
摘 要：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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