Remaining Time Prediction for Business Processes with Concurrency Based on Log Representation  

作　　者：Rui Cao Weijian Ni Qingtian Zeng Faming Lu Cong Liu Hua Duan 

机构地区：[1]College of Computer Science and Engineering,Shandong University of Science and Technology,Qingdao 266510,China [2]School of Computer Science and Technology,Shandong University of Technology,Zibo 255000,China

出　　处：《China Communications》2021年第11期76-91,共16页中国通信（英文版）

基　　金：supported by National Natural Science Foundation of China(No.U1931207 and No.61702306);Sci.&Tech.Development Fund of Shandong Province of China(No.ZR2019LZH001,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).

摘　　要：Remaining time prediction of business processes plays an important role in resource scheduling and plan making.The structural features of single process instance and the concurrent running of multiple process instances are the main factors that affect the accuracy of the remaining time prediction.Existing prediction methods does not take full advantage of these two aspects into consideration.To address this issue,a new prediction method based on trace representation is proposed.More specifically,we first associate the prefix set generated by the event log to different states of the transition system,and encode the structural features of the prefixes in the state.Then,an annotation containing the feature representation for the prefix and the corresponding remaining time are added to each state to obtain an extended transition system.Next,states in the extended transition system are partitioned by the different lengths of the states,which considers concurrency among multiple process instances.Finally,the long short-term memory(LSTM)deep recurrent neural networks are applied to each partition for predicting the remaining time of new running instances.By extensive experimental evaluation using synthetic event logs and reallife event logs,we show that the proposed method outperforms existing baseline methods.

关 键 词：business process monitoring remaining time prediction LSTM feature representation CONCURRENCY 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]