基于双向准循环神经网络和注意力机制的业务流程剩余时间预测方法

徐兴荣; 刘聪; 李婷; 郭娜; 任崇广; 曾庆田

doi:10.12263/DZXB.20211477

您当前的位置：

首页 >

文章列表页 >

基于双向准循环神经网络和注意力机制的业务流程剩余时间预测方法

学术论文 | 更新时间：2025-12-08

- 基于双向准循环神经网络和注意力机制的业务流程剩余时间预测方法
- Business Process Remaining Time Prediction: An Approach Based on Bidirectional Quasi Recurrent Neural Network with Attention
- 电子学报 2022年50卷第8期页码：1975-1984
- 作者机构：
  
  1.山东理工大学计算机科学与技术学院，山东淄博 255000
  2.山东科技大学计算机科学与工程学院，山东青岛 266590
- 作者简介：
  
  [ "徐兴荣男，1995年5月出生于山东省济南市.现为山东理工大学计算机科学与技术学院硕士研究生.主要研究方向为流程挖掘." ]
  [ "刘聪男，1990年8月出生于山东省淄博市 . 现为山东理工大学计算机科学与技术学院教授、博士生导师. 主要研究方向为流程挖掘. E-mail: liucongchina@sdust.edu.cn" ]
  [ "李婷女，1996年11月出生于山东省济宁市.现为山东理工大学计算机科学与技术学院硕士研究生.主要研究方向为流程挖掘.E-mail: Ltingoo@163.com" ]
  郭娜女，1996年7月出生于山东省淄博市.现为山东理工大学计算机科学与技术学院博士研究生.主要研究方向为流程挖掘.E-mail: guona_7@163.com
  任崇广男，1982年7月出生于山东省淄博市.现为山东理工大学计算机科学与技术学院教授、硕士生导师.主要研究方向为智能装备.E-mail: renchg@sdut.edu.cn
  曾庆田男，1976年1月出生于山东省潍坊市.现为山东科技大学计算机科学与工程学院教授、博士生导师.主要研究方向为流程挖掘.E-mail: qtzeng@163.com
- 基金信息：
  
  国家自然科学基金(61902222);山东省泰山学者工程专项基金(ts20190936;tsqn201909109);山东省自然科学基金优秀青年基金(ZR2021YQ45);山东省高等学校青创科技计划创新团队项目(2021KJ031)
- DOI：10.12263/DZXB.20211477
  中图分类号： TP391;TP311.5
- 收稿：2021-11-02，
  
  修回：2022-04-28，
  
  纸质出版：2022-08-25
- 稿件说明：
移动端阅览
徐兴荣,刘聪,李婷等.基于双向准循环神经网络和注意力机制的业务流程剩余时间预测方法[J].电子学报,2022,50(08):1975-1984.

XU Xing-rong,LIU Cong,LI Ting,et al.Business Process Remaining Time Prediction: An Approach Based on Bidirectional Quasi Recurrent Neural Network with Attention[J].ACTA ELECTRONICA SINICA,2022,50(08):1975-1984.
徐兴荣,刘聪,李婷等.基于双向准循环神经网络和注意力机制的业务流程剩余时间预测方法[J].电子学报,2022,50(08):1975-1984. DOI： 10.12263/DZXB.20211477.

XU Xing-rong,LIU Cong,LI Ting,et al.Business Process Remaining Time Prediction: An Approach Based on Bidirectional Quasi Recurrent Neural Network with Attention[J].ACTA ELECTRONICA SINICA,2022,50(08):1975-1984. DOI： 10.12263/DZXB.20211477.

摘要

业务流程预测可以有效帮助企业进行流程控制和传递高质量服务，因此作为此类场景中的核心任务之一，业务流程剩余时间预测得到国内外学者的广泛关注.当前，在利用深度学习技术对业务流程剩余时间进行预测时，大都采用传统长短期记忆循环神经网络，然而，由于长短期记忆循环神经网络在处理序列数据的过程中缺乏并行性且建模能力有限，使得预测准确度还有进一步提升空间.因此，本文提出一种基于双向准循环神经网络和注意力机制的业务流程剩余时间预测方法.首先，该方法以双向准循环神经网络构建剩余时间预测模型，并在预测模型中融入注意力机制增强双向准循环神经网络输出的特征信息.其次，设计了一种基于不同长度轨迹前缀训练迭代策略，解决流程实例中不同长度轨迹前缀数量存在差异性的问题.最后，提出一种基于Word2vec的事件表示学习方法，实现对同一轨迹且经常出现事件的相似性向量表示，从而达到提高剩余时间预测准确度的目的.经在5个公开事件日志数据集上实验，本文方法与已有方法相比在预测准确度上平均提高近15%，模型训练时间平均缩短约26%.

Abstract

Business process prediction can effectively facilitate enterprises to control processes and deliver high-quality services. As one of the core tasks of process prediction

remaining time prediction has been widely concerned by scholars. Currently

traditional long short-term memory(LSTM) neural networks have been used to predict the remaining time of business process instances. However

due to the lack of parallelism and limited modeling ability of LSTM in processing sequence data

the accuracy of prediction has further room to improve. In this paper

the remaining time prediction method based on bidirectional quasi-recurrent neural network with attention is proposed. Firstly

this method uses the bidirectional quasi-recurrent neural network to build the prediction model

and adds the attention mechanism to the model enhances the characteristic information of the bidirectional quasi-recurrent neural network output.Secondly

a training iteration strategy based on different length trace prefixes is designed

which solves the problem of the difference in the number of trace prefixes of different lengths. Finally

event representation learning method is proposed

to achieve vectors representation of similarity to the same traces and frequent events

improves the accuracy of the remaining time prediction. Experiments on five public event log datasets show this method has improved the accuracy of prediction by an average of nearly 15%

and the average training time is reduced by about 26%

compared with the existing methods.

关键词

Keywords

references

TEINEMAA I , DUMAS M , ROSA M L , et al . Outcome-oriented predictive process monitoring: Review and benchmark [J]. ACM Transactions on Knowledge Discovery from Data , 2019 , 13 ( 2 ): 17 .

TAX N , VERENICH I , LA ROSA M , et al . Predictive business process monitoring with LSTM neural networks [C]// International Conference on Advanced Information Systems Engineering . Aachen : Springer , 2017 : 477 - 492 .

VAN DER AALST W P M , SCHONENBERG M H , SONG M . Time prediction based on process mining [J]. Information Systems , 2011 , 36 ( 2 ): 450 - 475 .

赵海燕 , 李帅标 , 陈庆奎 , 等 . 面向业务过程的时间预测方法 [J]. 小型微型计算机系统 , 2019 , 40 ( 2 ): 42 - 48 .

ZHAO Hai-yan , LI Shuai-biao , CHEN Qing-kui , et al . Method of time prediction for business process [J]. Journal of Chinese Computer Systems , 2019 , 40 ( 2 ): 42 - 48 . (in Chinese)

ROGGE-SOLTI A , WESKE M . Prediction of business process durations using non-Markovian stochastic petri nets [J]. Information Systems , 2015 , 54 : 1 - 14 .

NAVARIN N , VINCENZI B , POLATO M , et al . LSTM networks for data-aware remaining time prediction of business process instances [C]// IEEE Symposium Series on Computational Intelligence . Hawaiian : IEEE , 2017 : 1 - 7 .

LEONTJEVA A , CONFORTI R , DI FRANCESCOMARINO C , et al . Complex symbolic sequence encodings for predictive monitoring of business processes [C]// International Conference on Business Process Management . Dubai : Springer , 2016 : 297 - 313 .

FOLINO F , GUARASCIO M , PONTIERI L . Mining predictive process models out of low-level multidimensional logs [C]// International Conference on Advanced Information Systems Engineering . Luxembourg : Springer , 2014 : 533 - 547 .

崔亮 . 基于机器学习的业务流程系统的预测 [D]. 北京 : 北京邮电大学 , 2019 .

POLATO M , SPERDUTI A , BURATTIN A , et al . Time and activity sequence prediction of business process instances [J]. Computing , 2018 , 100 ( 9 ): 1005 - 1031 .

VERENICH I , NGUYEN H , LA ROSA M , et al . White-box prediction of process performance indicators via flow analysis [C]// Proceedings of the 2017 International Conference on Software and System Process . Paris : ACM , 2017 : 85 - 94 .

EVERMANN J , REHSE J , FETTKE P . A deep learning approach for predicting process behaviour at runtime [C]// Business Process Management Workshops . Rio de Janeiro : Springer , 2016 : 327 - 388 .

NGUYEN A , CHATTERJEE S , WEINZIERL S , et al . Time matters: Time-aware LSTMs for predictive business process monitoring [C]// International Workshop on Leveraging Machine Learning in Process Mining . Padua : ICPM , 2020 : 1 - 12 .

倪维健 , 孙宇健 , 刘彤 , 等 . 基于注意力双向循环神经网络的业务流程剩余时间预测方法 [J]. 计算机集成制造系统 , 2020 , 26 ( 6 ): 1564 - 1572 .

NI Wei-jian , SUN Yu-jian , LIU Tong , et al . Business process remaining time prediction using bidirectional recurrent neural networks with attention [J]. Computer Integrated Manufacturing Systems , 2020 , 26 ( 6 ): 1564 - 1572 . (in Chinese)

KHAN A , LE H , DO K , et al . DeepProcess: Supporting business process execution using a MANN-based recommender system [EB/OL]. ( 2018 )[2021]. https://arxiv.org/abs/1802.00938 https://arxiv.org/abs/1802.00938 .

PASQUADIBISCEGLIE V , APPICE A , CASTELLANO G , et al . Using convolutional neural networks for predictive process analytics [C]// International Conference on Process Mining , Aachen : ICPM , 2019 : 29 - 136 .

TAYMOURI F , ROSA M L . Encoder-decoder generative adversarial nets for suffix generation and remaining time predication of business process models [EB/OL].( 2020 )[2021]. https://arxiv.org/abs/2007.16030 https://arxiv.org/abs/2007.16030 .

BUKHSH Z A , SAEED A , DIJKMAN R M . ProcessTransformer: Predictive business process monitoring with transformer network [EB/OL]. ( 2021 )[2021]. https://arxiv.org/abs/2104.00721 https://arxiv.org/abs/2104.00721 .

MIKOLOV T , CHEN K , CORRADO G , et al . Efficient estimation of word representations in vector space [EB/OL]. ( 2013 )[2021]. https://arxiv.org/abs/1301.3781 https://arxiv.org/abs/1301.3781 .

BRADBURY J , MERITY S . Quasi-recurrent neural networks [EB/OL]. ( 2019 )[2021]. https://arxiv.org/abs/1611.01576 https://arxiv.org/abs/1611.01576 .

王栋 , 李业刚 , 张晓 , 等 . 基于准循环神经网络的中文命名实体识别 [J]. 计算机工程与设计 , 2020 , 41 ( 7 ): 2038 - 2043 .

WANG Dong , LI Ye-gang , ZHANG Xiao , et al . Chinese name entity recognition based on quasi-recurrent neural networks [J]. Computer Engineering and Design , 2020 , 41 ( 7 ): 2038 - 2043 . (in Chinese)

时云龙 , 袁文浩 , 胡少东 , 等 . 一种用于实时语音增强的卷积准循环网络 [J]. 西安电子科技大学学报 , 2022 , 49 ( 3 ): 1 - 8 .

Shi Yun-long , Yuan Wen-hao , Hu Shao-dong , et al . Convolutional quasi-recurrent network for real-time speechenhancement [J]. Journal of Xidian University , 2022 , 49 ( 3 ): 1 - 8 . (in Chinese)

VAN DONGEN . Bpi2012 [OL]. https://doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f https://doi.org/10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f .

IlYA VERENICH . Helpdesk [OL]. https://mendeley.figshare.com/articles/dataset/Helpdesk/8951084 https://mendeley.figshare.com/articles/dataset/Helpdesk/8951084 .

FELIX MANNHARDT . Hospital Billing [OL]. https://data.4tu.nl/articl es/dataset/Hospital_Billing_-_Event_Log/12705113 https://data.4tu.nl/articles/dataset/Hospital_Billing_-_Event_Log/12705113 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向时序异常检测的可变视距多向扫描方法

基于稀疏平滑自蒸馏的差分隐私深度学习方法

基于非一般类算子融合方法及硬件架构设计

基于注意力融合多尺度特征的解压缩点云质量增强方法

基于深度压缩感知的联合信源信道编码方法研究