Given a process model and an event log, conformance checking aims to relate the two together, $\text{e.g.}$ to detect discrepancies between them. For the synchronous product net of the process and a log trace, we can assign different costs to a synchronous move, and a move in the log or model. By computing a path through this (synchronous) product net, whilst minimizing the total cost, we create a so-called optimal alignment — which is considered to be the primary target result for conformance checking. Traditional alignment-based approaches (1) have performance problems for larger logs and models, and (2) do not provide reliable diagnostics for non-conforming behaviour ($\text{e.g.}$ bottleneck analysis is based on events that did not happen). This is the reason to explore an alternative approach that maximizes the use of observed events. We also introduce the notion of milestone activities, $\text{i.e.}$ unskippable activities, and show how the different approaches relate to each other. We propose a data structure, that can be computed from the process model, which can be used for (1) computing alignments of many log traces that maximize synchronous moves, and (2) as a means for analysing non-conforming behaviour. In our experiments we show the differences of various alignment cost functions. We also show how the performance of constructing alignments with our data structure relates to that of the state-of-the-art techniques.

给定一个过程模型和一个事件日志，一致性检查旨在将两者关联在一起， $\text{例如}$以检测它们之间的差异。对于流程和日志跟踪的同步产品网，我们可以为同步移动以及日志或模型中的移动分配不同的成本。通过计算通过该（同步）产品网的路径，同时使总成本最小化，我们创建了所谓的最佳对齐方式-被视为一致性检查的主要目标结果。传统的基于对齐方式的方法（1）对于较大的日志和模型存在性能问题，并且（2）无法为不合格行为提供可靠的诊断（$\text{例如}$瓶颈分析基于未发生的事件）。这就是探索替代方法的原因，该方法可以最大程度地利用观察到的事件。我们还介绍了里程碑活动的概念，$\text{即}$不可跳过的活动，并展示不同方法之间的相互关系。我们提出了一种可以从过程模型中计算出的数据结构，该数据结构可用于（1）计算最大化同步移动的许多日志轨迹的对齐方式，以及（2）作为分析不合格行为的一种手段。在我们的实验中，我们显示了各种对齐成本函数的差异。我们还展示了使用我们的数据结构构建比对的性能与最新技术的关系。