The focus in the field of process mining, and process discovery in particular, has thus far been on exploring and describing event data by the means of models. Since the obtained models are often directly based on a sample of event data, the question whether they also apply to the real process typically remains unanswered. As the underlying process is unknown in real life, there is a need for unbiased estimators to assess the system-quality of a discovered model, and subsequently make assertions about the process. In this paper, an experiment is described and discussed to analyze whether existing fitness, precision and generalization metrics can be used as unbiased estimators of system fitness and system precision. The results show that important biases exist, which makes it currently nearly impossible to objectively measure the ability of a model to represent the system.

中文翻译：

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走向确认过程发现：对底层系统做出断言

迄今为止，流程挖掘领域，尤其是流程发现领域的重点一直是通过模型探索和描述事件数据。由于获得的模型通常直接基于事件数据样本，因此它们是否也适用于实际过程的问题通常仍未得到解答。由于潜在过程在现实生活中是未知的，因此需要无偏估计器来评估已发现模型的系统质量，然后对过程做出断言。在本文中，描述和讨论了一个实验，以分析现有的适应度、精度和泛化指标是否可以用作系统适应度和系统精度的无偏估计量。结果表明存在重要的偏差，