Predicting process behaviour using deep learning

被引:246
作者
Evermann, Joerg [1 ]
Rehse, Jana-Rebecca [2 ,3 ]
Fettke, Peter [2 ,3 ]
机构
[1] Mem Univ Newfoundland, St John, NF, Canada
[2] German Res Ctr Artificial Intelligence, Saarbrucken, Germany
[3] Saarland Univ, Saarbrucken, Germany
关键词
Process management; Runtime support; Process prediction; Deep learning; Neural networks; NEURAL-NETWORKS; BUSINESS; MODELS; TIME;
D O I
10.1016/j.dss.2017.04.003
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:129 / 140
页数:12
相关论文
共 39 条
[1]  
[Anonymous], 2016, 18492016 IEEE, P1
[2]  
[Anonymous], 2011, P 28 INT C MACH LEAR
[3]  
Bahrampour S., 2015, CORR
[4]   COMPREHENSIBLE PREDICTIVE MODELS FOR BUSINESS PROCESSES [J].
Breuker, Dominic ;
Matzner, Martin ;
Delfmann, Patrick ;
Becker, Joerg .
MIS QUARTERLY, 2016, 40 (04) :1009-+
[5]  
Ceci M, 2014, LECT NOTES ARTIF INT, V8777, P49, DOI 10.1007/978-3-319-11812-3_5
[6]  
Choi K., 2016, CORR
[7]  
Claes J, 2013, LECT NOTES BUS INF P, V132, P187
[8]  
Evermann J., 2016, PRAISE WORKSH 14 INT
[9]   Recurrent nets that time and count [J].
Gers, FA ;
Schmidhuber, J .
IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL III, 2000, :189-194
[10]  
Graves A., 2013, CORR