计算机应用研究2024，Vol.41Issue(7)：2005-2011,7.DOI:10.19734/j.issn.1001-3695.2023.11.0567
基于活动恢复集的有效低频行为分析方法
Effective infrequent behaviors analysis method based on activity recovery sets
摘要
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 ob-tained 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.关键词
行为重要性/有效低频行为/数据影响链/恢复集/行为容忍度Key words
behavioral importance/effective infrequent behavior/data impact chain/recovery sets/behavioral tolerance分类
信息技术与安全科学引用本文复制引用
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):2005-2011,7.基金项目
国家自然科学基金资助项目(61572035,61402011) （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) （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) （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) （SKLMRDPC22KF12）
安徽理工大学研究生创新基金资助项目(2022CX2136) （2022CX2136）
