论文标题
使用一致性指标在过程模型中逐渐漂移检测
Gradual Drift Detection in Process Models Using Conformance Metrics
论文作者
论文摘要
在执行现实生活过程中，计划或意外的变化是常见的。检测这些更改是优化运行此类过程的组织的性能的必要条件。最先进的大多数算法都集中在突然变化的检测上，抛开其他类型的变化。在本文中，我们将专注于自动检测渐进漂移，这是一种特殊的变化类型，其中两个模型的情况在一段时间内重叠。所提出的算法依赖于一致性检查指标来自动检测这些变化，还将这些变化的全自动分类分类为突然或逐渐分类。该方法已通过一个由120个日志组成的合成数据集进行了验证，该日志具有不同的变化分布，在检测和分类准确性，延迟和变化区域在比较主要的最新算法方面取得更好的结果。
Changes, planned or unexpected, are common during the execution of real-life processes. Detecting these changes is a must for optimizing the performance of organizations running such processes. Most of the algorithms present in the state-of-the-art focus on the detection of sudden changes, leaving aside other types of changes. In this paper, we will focus on the automatic detection of gradual drifts, a special type of change, in which the cases of two models overlap during a period of time. The proposed algorithm relies on conformance checking metrics to carry out the automatic detection of the changes, performing also a fully automatic classification of these changes into sudden or gradual. The approach has been validated with a synthetic dataset consisting of 120 logs with different distributions of changes, getting better results in terms of detection and classification accuracy, delay and change region overlapping than the main state-of-the-art algorithms.
