Predictive business process monitoring method based on concept drift

doi:10.11772/j.issn.1001-9081.2023101460

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (10): 3167-3176.DOI: 10.11772/j.issn.1001-9081.2023101460

• Computer software technology • Previous Articles Next Articles

Predictive business process monitoring method based on concept drift

Hua HUANG¹, Ziyi YANG¹, Xiaolong LI¹^,²(), Chuang LI¹

^1.School of Computer Science，Hunan University of Technology and Business，Changsha Hunan 410205，China
^2.Key Laboratory in Hunan Province Higher Education Institutions of Intelligence Sensing and Distributed Collaborative Optimization of Internet of Things （Hunan University of Technology and Business），Changsha Hunan 410205，China

Received:2023-10-27 Revised:2023-12-05 Accepted:2023-12-15 Online:2024-10-15 Published:2024-10-10
Contact: Xiaolong LI
About author:HUANG Hua， born in 1981， Ph.D.， associate professor. His research interests include cloud computing， service computing， artificial intelligence， blockchain.
YANG Ziyi， born in 1999， M. S. candidate. Her research interests include business process monitoring， service computing， process mining.
LI Chuang， born in 1990， Ph. D.， associate professor. His research interests include high-performance computing， parallel computing.
Supported by:
Young Project of National Natural Science Foundation of China(62002115);Natural Science Foundation of Hunan Province(2023JJ50319);Natural Science Foundation of Hunan for Youth(2022JJ40128);Science and Technology Program of Hunan Province(2021NK2020);Scientific Research Fund of Hunan Province Education Department(21B0560);Xiangjiang Laboratory Major Project(23XJ01001);Outstanding Innovative Youth Training Program of Changsha(kq2107020);Science and Technology Innovation Team Support Program of Hunan Provincial General Higher Education Institutions

基于概念漂移的预测性业务流程监控方法

黄华¹, 杨子仪¹, 李小龙¹^,²(), 李闯¹

^1.湖南工商大学计算机学院, 长沙 410205
^2.物联网智能感知与分布式协同优化湖南省高等学校重点实验室(湖南工商大学), 长沙 410205

通讯作者: 李小龙
作者简介:黄华（1981—），男，湖南衡阳人，副教授，博士，CCF会员，主要研究方向：云计算、服务计算、人工智能、区块链
杨子仪（1999—），女，湖南岳阳人，硕士研究生，CCF会员，主要研究方向：业务流程监控、服务计算、流程挖掘
李小龙（1981—），男，湖南常德人，教授，博士，CCF会员，主要研究方向：物联网 lxl@hutb.edu.cn
李闯（1990—），男，湖南益阳人，副教授，博士，CCF会员，主要研究方向：高性能计算、并行计算。
基金资助:
国家自然科学基金青年科学基金资助项目(62002115);湖南省自然科学基金资助项目(2023JJ50319);湖南省自然科学基金青年项目(2022JJ40128);湖南省重点研发科技计划项目(2021NK2020);湖南省教育厅科学研究项目(21B0560);湘江实验室重大项目(23XJ01001);长沙市杰出创新青年培养计划项目(kq2107020);湖南省普通高等学校科技创新团队支持项目

Abstract

Abstract:

A Predictive business Process Monitoring （PPM） method based on concept drift was proposed to solve the problems of model accuracy decreasing over time and poor real-time performance in existing Business Process Monitoring （BPM） methods. Firstly， the event log data was preprocessed and encoded. Secondly， a Bidirectional Long Short-Term Memory （Bi-LSTM） network model was used to capture enough sequence information from both forward and backward directions in order to build the business process model. At the same time， the attention mechanism was utilized to fully consider the contributions of different events to the model’s prediction results， and assign different weights to event logs to reduce the influence of noise on the prediction results. Finally， the executing instances were input into the constructed model to obtain the predicted execution results， and the results were used as historical data to fine-tune the model. By testing on 8 publicly available and real datasets， the results show that the proposed method has an average prediction accuracy improvement of 5.4%-23.8% compared to existing BPM methods such as Support Vector Machine （SVM）， Logistic Regression （LR）， and Random Forest （RF）， and the proposed method also outperforms existing research methods in terms of earliness and timeliness.

Key words: concept drift, Predictive business Process Monitoring (PPM), Business Process Management (BPM), event log, Bidirectional Long Short-Term Memory (Bi-LSTM), attention mechanism

摘要：

为解决现有的业务流程监控（BPM）方法的模型精度随时间下降和实时性较差的问题，提出一种基于概念漂移的预测性业务流程监控（PPM）方法。首先，对事件日志数据进行预处理及编码；其次，利用双向长短时记忆（Bi-LSTM）网络模型从前后方向捕获足够的序列信息以构建业务流程模型，并利用注意力机制充分考虑不同事件对预测结果的贡献程度，赋予事件日志不同的权重，从而减少噪声对预测结果的影响；最后，将正在执行的实例输入构建的模型，得到预测的执行结果，并将这些结果作为历史数据对模型微调。在8个公开且真实的数据集上的测试结果表明，所提方法的平均预测准确率相较于支持向量机（SVM）、逻辑回归（LR）和随机森林（RF）等已有的BPM方法提升了5.4%~23.8%，且早期性和时间性能都优于现有的研究方法。

关键词: 概念漂移, 预测性业务流程监控, 业务流程管理, 事件日志, 双向长短时记忆, 注意力机制

CLC Number:

TP183

Hua HUANG, Ziyi YANG, Xiaolong LI, Chuang LI. Predictive business process monitoring method based on concept drift[J]. Journal of Computer Applications, 2024, 44(10): 3167-3176.

黄华, 杨子仪, 李小龙, 李闯. 基于概念漂移的预测性业务流程监控方法[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3167-3176.

Figures/Tables 14

References 29

1	VIRIYASITAVAT W， XU L D， BI Z， et al. Blockchain-based Business Process Management （BPM） framework for service composition in Industry 4.0［J］. Journal of Intelligent Manufacturing， 2020， 31（7）： 1737-1748.
2	DIJKMAN R， TURETKEN O， VAN IJZENDOORN G R， et al. Business processes exceptions in relation to operational performance［J］. Business Process Management Journal， 2019， 25（5）： 908-922.
3	KIR H， ERDOGAN N. A knowledge-intensive adaptive business process management framework［J］. Information Systems， 2021， 95： No.101639.
4	MÁRQUEZ-CHAMORRO A E， RESINAS M， RUIZ-CORTÉS A. Predictive monitoring of business processes： a survey［J］. IEEE Transactions on Services Computing， 2018， 11（6）： 962-977.
5	CHEN J， JING H， CHANG Y， et al. Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process［J］. Reliability Engineering and System Safety， 2019， 185： 372-382.
6	PASQUADIBISCEGLIE V， APPICE A， CASTELLANO G， et al. Using convolutional neural networks for predictive process analytics［C］// Proceedings of the 2019 International Conference on Process Mining. Piscataway： IEEE， 2019： 129-136.
7	TAX N， VERENICH I， LA ROSA M， et al. Predictive business process monitoring with LSTM neural networks［C］// Proceedings of the 2017 International Conference on Advanced Information Systems Engineering， LNCS 10253. Cham： Springer， 2017： 477-492.
8	MEHDIYEV N， EVERMANN J， FETTKE P. A multi-stage deep learning approach for business process event prediction［C］// Proceedings of the IEEE 19th Conference on Business Informatics — Volume 1. Piscataway： IEEE， 2017： 119-128.
9	BAYRAM F， AHMED B S， KASSLER A. From concept drift to model degradation： an overview on performance-aware drift detectors［J］. Knowledge-Based Systems， 2022， 245： No.108632.
10	DELGADO A， MAROTTA A， GONZÁLEZ L， et al. Towards a data science framework integrating process and data mining for organizational improvement［C］// Proceedings of the 15th International Conference on Software Technologies. Setúbal： SciTePress， 2020： 492-500.
11	SATO D M V， DE FREITAS S C， BARDDAL J P， et al. A survey on concept drift in process mining［J］. ACM Computing Surveys， 2021， 54（9）： No.189.
12	LU J， LIU A， DONG F， et al. Learning under concept drift： a review［J］. IEEE Transactions on Knowledge and Data Engineering， 2019， 31（12）： 2346-2363.
13	GAMA J， MEDAS P， CASTILLO G， et al. Learning with drift detection［C］// Proceedings of the 2004 Brazilian Symposium on Artificial Intelligence， LNCS 3171. Berlin： Springer， 2004： 286-295.
14	GU F， ZHANG G， LU J， et al. Concept drift detection based on equal density estimation［C］// Proceedings of the 2016 International Joint Conference on Neural Networks. Piscataway： IEEE， 2016： 24-30.
15	YU S， WANG X， PRÍNCIPE J C. Request-and-reverify： hierarchical hypothesis testing for concept drift detection with expensive labels［C］// Proceedings of the 27th International Joint Conference on Artificial Intelligence. California： ijcai.org， 2018：3033-3039.
16	RAMA-MANEIRO E， VIDAL J C， LAMA M. Deep learning for predictive business process monitoring： review and benchmark［J］. IEEE Transactions on Services Computing， 2023， 16（1）： 739-756.
17	SCHÖNIG S， JASINSKI R， ACKERMANN L， et al. Deep learning process prediction with discrete and continuous data features［C］// Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering. Setúbal： SciTePress， 2018： 314-319.
18	LEONTJEVA A， CONFORTI R， DI FRANCESCOMARINO C， et al. Complex symbolic sequence encodings for predictive monitoring of business processes［C］// Proceedings of the 2015 International Conference on Business Process Management， LNCS 9253. Cham： Springer， 2015： 297-313.
19	王娇娇，马小雨，刘畅，等. 基于XGBoost增量实现业务流程执行结果的预测性监控方法［J］. 计算机集成制造系统，2024，30（8）：2756-2775.
	WANG J J， MA X Y， LIU C， et al. Incremental outcome-oriented predictive process monitoring based on XGBoost［J］. Computer Integrated Manufacturing Systems， 2024， 30（8）： 2756-2775.
20	YAO L， GUAN Y. An improved LSTM structure for natural language processing［C］// Proceedings of the 2018 IEEE International Conference of Safety Produce Informatization. Piscataway： IEEE， 2018： 565-569.
21	SHEWALKAR A， NYAVANANDI D， LUDWIG S A. Performance evaluation of deep neural networks applied to speech recognition： RNN， LSTM and GRU［J］. Journal of Artificial Intelligence and Soft Computing Research， 2019， 9（4）： 235-245.
22	STAUDEMEYER R C， MORRIS E R. Understanding LSTM — a tutorial into long short-term memory recurrent neural networks［EB/OL］. （2019-09-12）［2013-09-23］..
23	孙笑笑，侯文杰，应钰柯，等. 基于双层机器学习的业务流程剩余时间预测［J］. 计算机学报， 2021，44（11）：2283-2294.
	SUN X X， HOU W J， YING Y K， et al. Business process remaining time prediction based on two-layer machine learning［J］. Chinese Journal of Computers， 2021， 44（11）：2283-2294.
24	夏灿铭，邢玛丽，何胜煌. 基于XLNet的业务流程下一活动预测方法［J］. 计算机集成制造系统， 2023， 29（10）：3496-3503.
	XIA C M， XING M L， HE S H. XLNet-based next activity prediction method of business process［J］. Computer Integrated Manufacturing Systems， 2023， 29（10）：3496-3503.
25	郑婷婷，陈洁璇，许洋，等. 业务流程中一种个性化的任务完成时间预测方法［J］. 计算机集成制造系统， 2019， 25（4）： 993-1000.
	ZHENG T T， CHEN J X， XU Y， et al. Approach for individual task completion time prediction in business processes［J］. Computer Integrated Manufacturing Systems， 2019， 25（4）： 993-1000.
26	TEINEMAA I， DUMAS M， LA ROSA M， et al. Outcome-oriented predictive process monitoring： review and benchmark［J］. ACM Transactions on Knowledge Discovery from Data， 2019， 13（2）： No.17.
27	DU M， LI F， ZHENG G， et al. DeepLog： anomaly detection and diagnosis from system logs through deep learning［C］// Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. New York： ACM， 2017： 1285-1298.
28	ATHIWARATKUN B， WILSON A G， ANANDKUMAR A. Probabilistic FastText for multi-sense word embeddings［C］// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics （Volume 1： Long Papers）. Stroudsburg： ACL， 2019： 1-11.
29	QAISER S， ALI R. Text mining： use of TF-IDF to examine the relevance of words to documents［J］. International Journal of Computer Applications， 2018， 181（1）： 25-29.

事件日志	案例数	事件数	活动数	案例属性数
许可	7 065	86 581	51	168
国际申报	6 449	72 151	34	18
国内申报	10 500	56 437	17	5
预付差旅费用	2 099	18 246	29	17
请求付款	6 886	36 796	19	9

事件日志	案例数	事件数	活动数	案例属性数
许可	7 065	86 581	51	168
国际申报	6 449	72 151	34	18
国内申报	10 500	56 437	17	5
预付差旅费用	2 099	18 246	29	17
请求付款	6 886	36 796	19	9

事件日志	日志解析
Declaration APPROVED by ADMINISTRATION	Declaration APPROVED
Declaration FINAL APPROVED by DIRECTOR	Declaration FINAL APPROVED
Declaration FOR APPROVAL by ADMINISTRATION	Declaration FOR APPROVAL
Declaration REJECTED by ADMINISTRATION	Declaration REJECTED

事件日志	日志解析
Declaration APPROVED by ADMINISTRATION	Declaration APPROVED
Declaration FINAL APPROVED by DIRECTOR	Declaration FINAL APPROVED
Declaration FOR APPROVAL by ADMINISTRATION	Declaration FOR APPROVAL
Declaration REJECTED by ADMINISTRATION	Declaration REJECTED

事件日志	活动数	资源数	轨迹数	事件数	轨迹变化数
BPI20T	51	2	86 581	7 065	1 478
BPI20I	34	2	72 151	6 449	753
BPI20D	17	2	56 437	5	10 500
BPI20P	29	2	18 246	2 099	202
BPI20R	19	2	36 796	6 886	89
BPI12L	36	69	262 200	13 087	4 366

Predictive business process monitoring method based on concept drift

基于概念漂移的预测性业务流程监控方法

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 29

Related Articles 15

Recommended Articles

Metrics

事件日志	SVM	LR	RF	LSTM	Bi-LSTM	Att-Bi-LSTM
平均值	0.67	0.66	0.63	0.69	0.74	0.78
BPI20T	0.58	0.56	0.54	0.62	0.63	0.79
BPI20I	0.72	0.69	0.73	0.77	0.80	0.83
BPI20D	0.77	0.73	0.67	0.79	0.83	0.85
BPI20P	0.80	0.79	0.77	0.83	0.85	0.88
BPI20R	0.63	0.60	0.58	0.66	0.71	0.72
BPI12A	0.64	0.61	0.58	0.60	0.70	0.73
BPI12J	0.60	0.59	0.53	0.62	0.65	0.68
BPI12C	0.64	0.67	0.60	0.65	0.71	0.72

事件日志	SVM	LR	RF	LSTM	Bi-LSTM	Att-Bi-LSTM
平均值	11 621	4 306	14 716	11 587	12 167	11 532
BPI20T	23 385	9 066	28 943	23 014	24 821	21 605
BPI20I	19 463	8 170	21 934	19 782	20 694	19 617
BPI20D	483	57	74	163	107	736
BPI20P	16 801	6 711	18 006	15 097	15 826	14 357
BPI20R	21 859	8 539	28 412	25 493	27 845	21 375
BPI12A	6 890	1 164	7 732	7 032	3 659	7 365
BPI12J	1 907	545	7 954	974	974	1 237
BPI12C	2 183	196	4 673	1 137	3 407	5 964

[1]	Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974.
[2]	Liting LI, Bei HUA, Ruozhou HE, Kuang XU. Multivariate time series prediction model based on decoupled attention mechanism [J]. Journal of Computer Applications, 2024, 44(9): 2732-2738.
[3]	Zhiqiang ZHAO, Peihong MA, Xinhong HEI. Crowd counting method based on dual attention mechanism [J]. Journal of Computer Applications, 2024, 44(9): 2886-2892.
[4]	Kaipeng XUE, Tao XU, Chunjie LIAO. Multimodal sentiment analysis network with self-supervision and multi-layer cross attention [J]. Journal of Computer Applications, 2024, 44(8): 2387-2392.
[5]	Pengqi GAO, Heming HUANG, Yonghong FAN. Fusion of coordinate and multi-head attention mechanisms for interactive speech emotion recognition [J]. Journal of Computer Applications, 2024, 44(8): 2400-2406.
[6]	Zhonghua LI, Yunqi BAI, Xuejin WANG, Leilei HUANG, Chujun LIN, Shiyu LIAO. Low illumination face detection based on image enhancement [J]. Journal of Computer Applications, 2024, 44(8): 2588-2594.
[7]	Shangbin MO, Wenjun WANG, Ling DONG, Shengxiang GAO, Zhengtao YU. Single-channel speech enhancement based on multi-channel information aggregation and collaborative decoding [J]. Journal of Computer Applications, 2024, 44(8): 2611-2617.
[8]	Wu XIONG, Congjun CAO, Xuefang SONG, Yunlong SHAO, Xusheng WANG. Handwriting identification method based on multi-scale mixed domain attention mechanism [J]. Journal of Computer Applications, 2024, 44(7): 2225-2232.
[9]	Huanhuan LI, Tianqiang HUANG, Xuemei DING, Haifeng LUO, Liqing HUANG. Public traffic demand prediction based on multi-scale spatial-temporal graph convolutional network [J]. Journal of Computer Applications, 2024, 44(7): 2065-2072.
[10]	Dianhui MAO, Xuebo LI, Junling LIU, Denghui ZHANG, Wenjing YAN. Chinese entity and relation extraction model based on parallel heterogeneous graph and sequential attention mechanism [J]. Journal of Computer Applications, 2024, 44(7): 2018-2025.
[11]	Li LIU, Haijin HOU, Anhong WANG, Tao ZHANG. Generative data hiding algorithm based on multi-scale attention [J]. Journal of Computer Applications, 2024, 44(7): 2102-2109.
[12]	Song XU, Wenbo ZHANG, Yifan WANG. Lightweight video salient object detection network based on spatiotemporal information [J]. Journal of Computer Applications, 2024, 44(7): 2192-2199.
[13]	Dahai LI, Zhonghua WANG, Zhendong WANG. Dual-branch low-light image enhancement network combining spatial and frequency domain information [J]. Journal of Computer Applications, 2024, 44(7): 2175-2182.
[14]	Wenliang WEI, Yangping WANG, Biao YUE, Anzheng WANG, Zhe ZHANG. Deep learning model for infrared and visible image fusion based on illumination weight allocation and attention [J]. Journal of Computer Applications, 2024, 44(7): 2183-2191.
[15]	Xiaolu WANG, Wangfei QIAN. Gait recognition method based on two-branch convolutional network [J]. Journal of Computer Applications, 2024, 44(6): 1965-1971.