摘要
现有的大多数偏差检测方法能够识别来自流程活动及部分数据属性的偏差,但是无法处理流程执行过程中数据变化对流程的影响问题,尤其是在涉及多对象交互的情况下。针对这一问题,提出了一种多对象交互情况下基于数据影响的业务流程偏差检测方法。首先,基于控制流与数据信息识别可能的偏差活动;然后,根据数据变化对活动的影响定义影响集;接着,将以对象为中心的概念引入偏差检测过程,形式化以对象为中心的Petri网模型,在此基础上,通过分析对象是否对其修改的数据具有执行权限,分类并定义了四种数据影响类型及其计算标准,据此得到基于数据影响的偏差检测结果;最后,与其他偏差检测方法对比验证,结果表明,应用该方法得到的偏差检测结果值得到提升,并且能够处理多对象交互的流程偏差。该方法能够有效捕获多对象交互流程中数据变化影响的流程活动,提高偏差检测的合理性与准确性。
Most existing deviation detection methods are capable of identifying deviations from process activities and data attributes,but they fail to address the issue of how changes in data during process execution impact the process,especially in cases involving interactions among multiple objects.To address this issue,this paper proposed a business process deviation detection method based on data impact in the context of multi-object interactions.Firstly,this method identified potential deviant activities based on control flow and data information.Then,it defined impact sets based on the impact of data changes on activities.Next,it introduced the concept of object-centric into the deviation detection process and formalized the object-centric Petri net model.On this basis,by analyzing whether the object had execution privilege on the data it modified,this paper classified and defined four types of data impacts and their calculation criteria,from which the results of deviation detection based on data impacts were obtained.Finally,compared with other deviation detection methods,the results show that the devia-tion detection results obtained by applying the method are improved and are able to handle process deviations for multi-object interactions.This method can effectively capture the process activities affected by data changes in multi-object interaction processes and improve the rationality and accuracy of deviation detection.
作者
钱陈婧
方贤文
张希为
Qian Chenjing;Fang Xianwen;Zhang Xiwei(College of Mathematics&Big Data,Anhui University of Science&Technology,Huainan Anhui 232001,China;Anhui Province Engineering Laboratory for Big Data Analysis&Early Warning Technology of Coal Mine Safety,Anhui University of Science&Technology,Huainan Anhui 232001,China)
出处
《计算机应用研究》
CSCD
北大核心
2024年第3期880-886,共7页
Application Research of Computers
基金
国家自然科学基金资助项目(61572035,61402011)
安徽省重点研究与开发计划资助项目(2022a05020005)
安徽省高校领军骨干人才项目(2020-1-12)。
