A fundamental problem in conformance checking is aligning event data with process models. Unfortunately, existing techniques for this task are either complex, or can only be applicable to restricted classes of models. This in practice means that for large inputs, current techniques often fail to produce a result. In this paper we propose a method to compute alignments for unconstrained process models, which relies on the use of relaxation labelling techniques on top of a partial order representation of the process model. The technique proposed in this paper precomputes information used in the search for alignments, and is able to produce real alignments that may be close to optimal ones by combining the aforementioned techniques with a locally applied A${}^{\ast }$ strategy. Remarkably, the implementation on the proposed technique achieves a speed-up of several orders of magnitude with respect to the approaches in the literature (either optimal, sup-optimal or approximate), often with a reasonable trade-off on the cost of the obtained alignment.

一致性检查的一个基本问题是使事件数据与流程模型保持一致。不幸的是，用于此任务的现有技术要么很复杂，要么仅适用于受限的模型类别。实际上，这意味着对于大量输入，当前的技术通常无法产生结果。在本文中，我们提出了一种用于计算不受约束的过程模型的对齐方式的方法，该方法依赖于在过程模型的部分顺序表示之上使用松弛标记技术。本文提出的技术会预先计算用于搜索比对的信息，并且能够通过将上述技术与局部应用的A相结合，产生可能接近最佳比对的真实比对。${}^{\ast }$战略。值得注意的是，相对于文献中的方法（最优，次优或近似），所提出技术的实现方式实现了几个数量级的加速，通常在获得的成本上进行合理的权衡。对准。