Comparing observed behavior (event data generated during process executions) with modeled behavior (process models), is an essential step in process mining analyses. Alignments are the de-facto standard technique for calculating conformance checking statistics. However, the calculation of alignments is computationally complex since a shortest path problem must be solved on a state space which grows non-linearly with the size of the model and the observed behavior, leading to the well-known state space explosion problem. In this paper, we present a novel framework to approximate alignments on process trees by exploiting their hierarchical structure. Process trees are an important process model formalism used by state-of-the-art process mining techniques such as the inductive mining approaches. Our approach exploits structural properties of a given process tree and splits the alignment computation problem into smaller sub-problems. Finally, sub-results are composed to obtain an alignment. Our experiments show that our approach provides a good balance between accuracy and computation time.

中文翻译：

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过程树的对齐近似

将观察到的行为（流程执行期间生成的事件数据）与建模行为（流程模型）进行比较，是流程挖掘分析中必不可少的步骤。对齐是计算一致性检查统计数据的事实上的标准技术。然而，对齐的计算在计算上是复杂的，因为最短路径问题必须在随着模型大小和观察到的行为非线性增长的状态空间上解决，导致众所周知的状态空间爆炸问题。在本文中，我们提出了一个新的框架，通过利用它们的层次结构来近似处理树上的对齐。流程树是最先进的流程挖掘技术（例如归纳挖掘方法）所使用的重要流程模型形式。我们的方法利用给定过程树的结构特性，并将对齐计算问题拆分为更小的子问题。最后，组合子结果以获得比对。我们的实验表明，我们的方法在准确性和计算时间之间提供了良好的平衡。