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：在现实流程执行过程中，变化（计划内或意外）普遍存在。检测这些变化对于优化运行此类流程的组织性能至关重要。现有主流算法主要关注突变检测，而忽略了其他类型的变化。本文聚焦于渐进式漂移的自动检测——这是一种特殊变化类型，其中两个流程模型的实例在特定时间段内存在重叠现象。所提出的算法基于一致性检测指标实现变化的自动识别，并能对这些变化进行全自动分类（突变或渐进）。该算法已在由120个具有不同变化分布的日志组成的合成数据集上完成验证，在检测与分类准确率、延迟时间及变化区域重叠度等指标上均优于主要主流算法。