论文标题
定时对齐
Timed Alignments
论文作者
论文摘要
本文的主题是研究定时模型的一致性检查，即，在过程中既考虑事件序列又一次记录每个事件的时间戳。时间吸引的过程挖掘是一个越来越多的研究子领域，并且作为试图发现过程中相关属性发展的工具，因此需要对时间限制并为时间感知过程模型提供有见地的质量措施的一致性检查技术的需求也是如此。特别是，最有用的一致性人工制品之一是对齐，也就是说，找到了纠正新观察以符合过程模型所需的最小变化。在本文中，我们设定了定时对准问题的问题，并解决了两种情况，每个案例对应于不同的度量。首先，我们有一种算法，其时间复杂性在观察到的轨迹的大小和过程模型的大小上是线性的，而在第二个则具有线性过程模型的二次时间算法。
The subject of this paper is to study conformance checking for timed models, that is, process models that consider both the sequence of events in a process as well as the timestamps at which each event is recorded. Time-aware process mining is a growing subfield of research, and as tools that seek to discover timing related properties in processes develop, so does the need for conformance checking techniques that can tackle time constraints and provide insightful quality measures for time-aware process models. In particular, one of the most useful conformance artefacts is the alignment, that is, finding the minimal changes necessary to correct a new observation to conform to a process model. In this paper, we set our problem of timed alignment and solve two cases each corresponding to a different metric over time processes. For the first, we have an algorithm whose time complexity is linear both in the size of the observed trace and the process model, while for the second we have a quadratic time algorithm for linear process models.
