关键活动节点处的在线近似一致性检测  

Online approximate conformance checking at key activity nodes

作　　者：郭鑫 方贤文 毛古宝 GUO Xin;FANG Xian-wen;MAO Gu-bao(School of Mathematics and Big Data,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学数学与大数据学院,安徽淮南232001

出　　处：《计算机工程与设计》2023年第5期1328-1335,共8页Computer Engineering and Design

基　　金：国家自然科学基金项目(61572035、61402011);安徽省自然科学基金项目(1508085MF111、1608085QF149);安徽省高校研究生科学基金项目(YJS20210369);安徽理工大学研究生创新基金项目(2019CX2068)。

摘　　要：为校验事件流与模型之间的服从性,节约资源和提高检测效率,提出一种不需要构建模型的近似检测方法。对已有的日志进行聚类,选取代表性的迹构建模型支集,计算适合度上下界作为一致性的判断依据。系统在线运行过程中,将事件流暂存于事件堆栈,在关键活动节点处取出序列,进行与模型支集之间的一致性检测。通过实际案例对提出方法进行性能评估,并与其它方法进行对比,结果验证了算法的可行性,其具有较高的准确率。To verify the conformance between event stream and model,save resources and improve detection efficiency,an approximate detection method that had no need to build a model was proposed.The existing logs were clustered,representative traces were selected to build model support,and the upper and lower bounds of fitness were calculated as the basis of consistency judgment.During the online operation of the system,the event flow was temporarily stored in the event stack,and the event sequence was only taken out at the key nodes for conformance checking with the model support.The performance of the proposed method was evaluated through a practical log,and compared with other methods.Results show that the proposed algorithm is feasible and has high accuracy.

关 键 词：层次聚类 模型支集 近似一致性 适合度上下界 在线检测 事件堆栈 关键活动节点 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]