Process mining represents a collection of data driven techniques that support the analysis, understanding and improvement of business processes. A core branch of process mining is conformance checking, i.e., assessing to what extent a business process model conforms to observed business process execution data. Alignments are the de facto standard instrument to compute such conformance statistics. However, computing alignments is a combinatorial problem and hence extremely costly. At the same time, many process models share a similar structure and/or a great deal of behavior. For collections of such models, computing alignments from scratch is inefficient, since large parts of the alignments are likely to be the same. This paper presents a technique that exploits process model similarity and repairs existing alignments by updating those parts that do not fit a given process model. The technique effectively reduces the size of the combinatorial alignment problem, and hence decreases computation time significantly. Moreover, the potential loss of optimality is limited and stays within acceptable bounds.

中文翻译：

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修复过程模型的对齐

流程挖掘代表了一组数据驱动的技术，这些技术支持业务流程的分析、理解和改进。流程挖掘的一个核心分支是一致性检查，即评估业务流程模型在多大程度上符合观察到的业务流程执行数据。比对是计算此类一致性统计数据的事实上的标准工具。然而，计算对齐是一个组合问题，因此成本极高。同时，许多流程模型共享相似的结构和/或大量行为。对于此类模型的集合，从头开始计算对齐是低效的，因为大部分对齐可能是相同的。本文介绍了一种利用过程模型相似性并通过更新那些不适合给定过程模型的部分来修复现有对齐的技术。该技术有效地减少了组合对齐问题的规模，从而显着减少了计算时间。此外，最优性的潜在损失是有限的，并保持在可接受的范围内。