Conformance checking unleashes the full power of process mining: techniques from this discipline enable the analysis of the quality of a process model through the discovery of event data, the identification of potential deviations, and the projection of real traces onto process models. In this way, the insights gained from the available event data can be transferred to a richer conceptual level, amenable for human interpretation. Unfortunately, most of the aforementioned functionalities are grounded in an extremely difficult fundamental problem: given an observed trace and a process model, find the model trace that most closely resembles to the trace observed. This paper presents an architecture that supports the creation and distribution of alignment subproblems based on an innovative horizontal acyclic model decomposition, disengaged from the conformance checking algorithm applied for their solution. This is supported by a Big Data infrastructure that facilitates the customised distribution of a gross amount of data. Experiments are provided that testify to the enormous potential of the architecture proposed, thereby opening the door to further research in several directions.

一致性检查释放了过程挖掘的全部功能：该学科的技术可以通过发现事件数据，识别潜在偏差以及将真实轨迹投影到过程模型上来分析过程模型的质量。通过这种方式，从可用事件数据中获得的见解可以转移到更丰富的概念级别，适合人类解释。不幸的是，大多数上述功能都基于一个极其困难的基本问题：给定观察到的轨迹和过程模型，找到与观察到的轨迹最相似的模型轨迹。本文提出了一种架构，该架构基于创新的水平非循环模型分解，支持对齐子问题的创建和分配，脱离了适用于其解决方案的一致性检查算法。大数据基础架构对此提供了支持，该基础架构有助于自定义分配总数据量。提供的实验证明了所提出的体系结构的巨大潜力，从而为在多个方向上的进一步研究打开了大门。