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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 7
2005-2011

Effective infrequent behaviors analysis method based on activity recovery sets

Ren Ziweia
Wang Lilia,b,c
Zuo Yinkaia
a. College of Mathematics & Big Data, b. State Key Laboratory of Mining Response & Disaster Prevention & Control in Deep Coal Mines, c. Anhui Province Engineering Laboratory for Big Data Analysis & Early Warning Technology of Coal Mine Safety, Anhui University of Science & Technology, Huainan Anhui 232001, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0567

Abstract

Infrequent behavior recognition is one of the methods to reveal important information about business processes and optimize process models. Existing process discovery methods have overlooked the impact of data influence chains on infrequent behavior, resulting in some infrequent behavior being considered as noise and filtered out directly. To address this issue, this paper proposed a novel infrequent behavior analysis method based on activity recovery sets. Firstly, it filtered the event logs based on the importance of behavior and constructed an initial process model. Secondly, it extracted input and output data items of activities from transaction logs, and constructed an activity influence chain graph based on these data items. It obtained activity recovery sets based on these graphs. Finally, it calculated the behavior tolerance of each trace using the activity recovery sets to distinguish effective infrequent behavior from noise. The experimental results indicate that, compared to other methods, this study effectively distinguishes valid infrequent behaviors from noise and improves the quality of the process model in terms of fitness, precision, and simplicity. This method considers the biases caused by the activity recovery set and successfully identifies valid infrequent behaviors in event logs, thereby optimizing the process model.

Foundation Support

国家自然科学基金资助项目(61572035,61402011)
安徽理工大学高层次引进人才科研启动基金资助项目(2022yjrc87)
安徽省煤矿安全大数据分析与预警技术工程实验室开放基金资助项目(CSBD2022-ZD03)
深部煤矿采动响应与灾害防控国家重点实验室开放基金资助项目(SKLMRDPC22KF12)
安徽理工大学研究生创新基金资助项目(2022CX2136)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0567
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 7
Section: Algorithm Research & Explore
Pages: 2005-2011
Serial Number: 1001-3695(2024)07-012-2005-07

Publish History

[2024-01-30] Accepted Paper
[2024-07-05] Printed Article

Cite This Article

任紫薇, 王丽丽, 左殷恺. 基于活动恢复集的有效低频行为分析方法 [J]. 计算机应用研究, 2024, 41 (7): 2005-2011. (Ren Ziwei, Wang Lili, Zuo Yinkai. Effective infrequent behaviors analysis method based on activity recovery sets [J]. Application Research of Computers, 2024, 41 (7): 2005-2011. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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