The identification of undesirable behavior in event logs is an important aspect of process mining that is often addressed by anomaly detection methods. Traditional anomaly detection methods tend to focus on statistically rare behavior and neglect the subtle difference between rarity and undesirability. The introduction of semantic anomaly detection has opened a promising avenue by identifying semantically deviant behavior. This work addresses a gap in semantic anomaly detection, which typically indicates the occurrence of an anomaly without explaining the nature of the anomaly. We propose xSemAD, an approach that uses a sequence-to-sequence model to go beyond pure identification and provides extended explanations. In essence, our approach learns constraints from a given process model repository and then checks whether these constraints hold in the considered event log. This approach not only helps understand the specifics of the undesired behavior, but also facilitates targeted corrective actions. Our experiments demonstrate that our approach outperforms existing state-of-the-art semantic anomaly detection methods.

翻译：事件日志中不良行为的识别是过程挖掘的重要方面，通常通过异常检测方法进行处理。传统异常检测方法往往侧重于统计上的罕见行为，而忽视了罕见性与不良性之间的微妙差异。语义异常检测的引入通过识别语义上的偏差行为，开辟了一条前景广阔的途径。本研究针对语义异常检测领域的一个空白展开，该领域通常仅指示异常的发生而不解释异常的性质。我们提出xSemAD方法，该方法利用序列到序列模型超越单纯识别，提供扩展解释。本质上，我们的方法从给定的过程模型库中学习约束规则，随后检查这些约束在目标事件日志中是否成立。该方法不仅有助于理解不良行为的具体特征，还能促进针对性纠正措施的制定。实验结果表明，我们的方法在性能上优于现有最先进的语义异常检测方法。