摘要
日志中发生的低频次行为与挖掘的流程模型中某些不必要的结构相对应,而这些结构的出现会引起挖掘模型在适应度和精确度等指标上的下降。为解决这些结构对流程挖掘模型质量造成的影响,提出一种基于依赖关联度的噪声日志过滤方法。该方法首先根据日志中事件及其依赖关系的统计频率,定义了依赖关系的局部关联度和整体关联度,并将两者归一化为混合关联度来筛选出噪声日志。然后通过轨迹可达性分析去除日志中的噪声,以便最大程度地保留日志轨迹中记录的其他行为。与传统噪声日志过滤算法过滤掉包含噪声日志的整条日志轨迹不同,所提算法在移除噪声日志的同时最大程度地保留了原始日志中的其他非噪声日志。
The behavior with low frequency in the log corresponds to some unnecessary structures in the process model,which leads to the decline of the fitness and accuracy of mining models.To address this problem,a noise log filtering method based on dependent relevancy named Dependency Relevancy driven noise Filtering(DRF)was proposed.According to the statistical frequency of events and edges,the local correlation degree and global correlation degree of the dependency relationship were defined and normalized into a mixed relationship degree,based on which the noise log was filtered out.The noise in the log by the path accessibility analysis was removed,so the other behaviors recorded in the log trajectory were preserved to the maximum extent.Unlike the traditional log filtering algorithms that filtered out the whole log trajectory containing noisy log,DRF could preserve non-noisy log while removing the noisy one.
引文
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