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.

翻译：在实际生活过程实施过程中,计划或意外的变化是常见的。检测这些变化是优化运行这些过程的组织绩效的一个必要条件。最先进的算法大多侧重于检测突然的变化,而忽略其他类型的变化。在本文中,我们将侧重于自动检测渐进漂移,一种特殊类型的变化,其中两种模型的情况在一段时间内重叠。拟议的算法依赖合规检查标准来进行自动检测变化,同时对这些变化进行完全自动的分类,突然或逐步地进行。这一方法得到了由120个日志组成的合成数据集的验证,这些日志的变化分布各异,在检测和分类准确性、延迟和变化区域方面的结果优于主要状态算法。