目标中心工序采矿的精度和精度 (Precision and Fitness in Object-Centric Process Mining)

Traditional process mining considers only one single case notion and discovers and analyzes models based on this. However, a single case notion is often not a realistic assumption in practice. Multiple case notions might interact and influence each other in a process. Object-centric process mining introduces the techniques and concepts to handle multiple case notions. So far, such event logs have been standardized and novel process model discovery techniques were proposed. However, notions for evaluating the quality of a model are missing. These are necessary to enable future research on improving object-centric discovery and providing an objective evaluation of model quality. In this paper, we introduce a notion for the precision and fitness of an object-centric Petri net with respect to an object-centric event log. We give a formal definition and accompany this with an example. Furthermore, we provide an algorithm to calculate these quality measures. We discuss our precision and fitness notion based on an event log with different models. Our precision and fitness notions are an appropriate way to generalize quality measures to the object-centric setting since we are able to consider multiple case notions, their dependencies and their interactions.

翻译：传统开采过程只考虑一个案例概念,发现并分析基于这个概念的模型。然而,一个案例概念往往不是现实的假设。多案例概念可能会在一个过程中相互作用和影响对方。以物体为中心的过程采矿会引入处理多个案例概念的技术和概念。到目前为止,这种事件日志已经标准化,提出了新的过程模型发现技术。然而,缺少评估模型质量的概念。这些对于今后研究改进以物体为中心的发现和提供对模型质量的客观评价是必要的。在本文中,我们引入一个以物体为中心的Petri网对于以物体为中心的事件日志的精确性和适切性的概念。我们给出一个正式的定义,并附一个实例。此外,我们提供了计算这些质量计量的算法。我们用不同模型的事件日志讨论我们的精确性和健康概念。我们的精确性和健康概念是将质量措施推广到以物体为中心的环境的适当方法,因为我们能够考虑多种案例概念、其依赖性和相互作用。