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.

该论文提出了一种基于符合度检查度量的算法，用于自动检测渐进漂移的变化，并对这些变化进行自动分类，通过合成数据集验证了该算法的准确性和延迟，并得到了更好的结果。

使用符合度量检测流程模型中的渐进漂移

Gradual Drift Detection in Process Models Using Conformance Metrics

While the importance of automatic image analysis is continuously increasing,
recent meta-research revealed major flaws with respect to algorithm validation.
Performance metrics are particularly key for meaningful, objective, and
transparent performance assessment and validation of the used automatic
algorithms, but relatively little attention has been given to the practical
pitfalls when using specific metrics for a given image analysis task. These are
typically related to (1) the disregard of inherent metric properties, such as
the behaviour in the presence of class imbalance or small target structures,
(2) the disregard of inherent data set properties, such as the non-independence
of the test cases, and (3) the disregard of the actual biomedical domain
interest that the metrics should reflect. This living dynamically document has
the purpose to illustrate important limitations of performance metrics commonly
applied in the field of image analysis. In this context, it focuses on
biomedical image analysis problems that can be phrased as image-level
classification, semantic segmentation, instance segmentation, or object
detection task. The current version is based on a Delphi process on metrics
conducted by an international consortium of image analysis experts from more
than 60 institutions worldwide.

本研究通过国际图像分析专家团队的 Delphi 过程，指出算法验证中经常存在的性能度量的局限性，无法全面评估自动算法的有效性及透明性，特别是在医学图像分析方面，这一问题尤为突出。