通过投影放松对齐，区分系统日志与模型中的可信与不可信内容。
In System Alignments we Trust! Explainable Alignments via Projections
- 用投影引入松弛机制，处理部分正确的模型和日志。
- 可识别日志与模型中可信与不可信的执行行为。
- 适合流程挖掘中需解释对齐结果的用户使用。
对齐是流程挖掘中用于调和系统日志与规范流程模型的经典技术。真实系统中某些行为的证据可能仅存在于日志或模型之一，而不在另一方。当多个实体（如对象、资源）参与活动时，它们的交互会影响行为，因此必须在对齐中予以考虑。此外，日志和模型对现实的表示都可能存在不精确或部分缺失的情况。本文提出通过投影引入‘松弛’概念，以应对部分正确的模型与日志。松弛对齐有助于区分日志与模型中可信与不可信的内容，从而更深入理解底层流程并揭示质量缺陷。
原文摘要 · Abstract (English)
Alignments are a well-known process mining technique for reconciling system logs and normative process models. Evidence of certain behaviors in a real system may only be present in one representation - either a log or a model - but not in the other. Since for processes in which multiple entities, like objects and resources, are involved in the activities, their interactions affect the behavior and are therefore essential to take into account in the alignments. Additionally, both logged and modeled representations of reality may be imprecise and only partially represent some of these entities, but not all. In this paper, we introduce the concept of "relaxations" through projections for alignments to deal with partially correct models and logs. Relaxed alignments help to distinguish between trustworthy and untrustworthy content of the two representations (the log and the model) to achieve a better understanding of the underlying process and expose quality issues.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。