Detecting concept drift of process models from event logs

被引：0

作者：

Zheng C. ^{[1
]}

Wu X. ^{[2
]}

Wen L. ^{[1
]}

Wang J. ^{[1
]}

机构：

[1] School of Software, Tsinghua University, Beijing

[2] School of Computer Science & Technology, Jilin University, Changchun

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Change detection; Concept drift; Event logs; Process discovery; Process mining;

D O I：

10.13196/j.cims.2019.04.004

中图分类号：

学科分类号：

摘要：

Business process tends to change in real world application, which is called process drift. Traditional process discovery technique assumes process to be in a steady state and does not takes account of process drift. Process drift can be classified into sudden drift and gradual drift. Sudden drift has been well solved while gradual drift not. For this problem, an approach to detect gradual drift based on sudden drift detection was proposed, which could divide the event log into multiple sublogs. For any three successive sublogs, if the middle one's behavior was a combination of the other two's, then the middle one was generated by gradual drift. Experiments on synthetic logs showed that the proposed approach had better performance than state of the art. © 2019, Editorial Department of CIMS. All right reserved.

引用

页码：830 / 836

页数：6

共 13 条

[1]

Van Der Aalst W.P., Weijters T., Maruster L., Workflow mining: discovering process models from event logs, IEEE Transactions on Knowledge and Data Engineering, 16, 9, pp. 1128-1142, (2004)

[2]

Weijters A., Van Der Aalst W.M., De Medeiros A.A., Process mining with the heuristics miner-algorithm

[3]

Van Der Aalst W.M.P., De Medeiros A.A., Weijters A., Genetic process mining, Proceedings of the International Conference on Application and Theory of Petri Nets, pp. 48-69, (2005)

[4]

Lamma E., Mello P., Riguzzi F., Et al., Applying inductive logic programming to process mining, Proceedings of the International Conference on Inductive Logic Programming, pp. 132-146, (2007)

[5]

Bose R.P.J.C., Van Der Aalst W.M.P., Zliobaite I., Et al., Dealing with concept drifts in process mining, IEEE Transactions on Neural Networks and Learning Systems, 25, 1, pp. 154-171, (2014)

[6]

Martjushev J., Jagadeesh Chandra Bose R.J.C., Van Der Aalst W.M., Change point detection and dealing with gradual and multi-order dynamics in process mining, Proceedings of the International Conference on Business Informatics Research, pp. 161-178, (2015)

[7]

Carmona J., Gavalda R., Online techniques for dealing with concept drift in process mining, Proceedings of the International Symposium on Intelligent Data Analysis, pp. 99-102, (2012)

[8]

Maaradji A., Dumas M., La Rosa M., Et al., Fast and accurate business process drift detection, Proceedings of the International Conference on Business Process Management, pp. 406-422, (2015)

[9]

Zheng C., Wen L., Wang J., Detecting Process Concept Drifts from Event Logs, Proceedings of the Confederated International Conferences On the Move to Meaningful Internet Systems, pp. 524-542, (2017)

[10]

Zha H., Wang J., Wen L., Et al., A workflow net similarity measure based on transition adjacency relations, Computers in Industry, 61, 5, pp. 463-471, (2010)

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