Process mining is a research area focusing on the design of algorithms that can automatically provide insights into business processes by analysing historic process execution data, known as event logs. Among the most popular algorithms are those for automated process discovery, whose ultimate goal is to generate the best process model that summarizes the behaviour recorded in the input event log. Over the past decade, several process discovery algorithms have been proposed but, until now, this research was driven by the implicit assumption that a better algorithm would discover better process models, no matter the characteristics of the input event log. In this paper, we take a step back and question that assumption. Specifically, we investigate what are the relations between measures capturing characteristics of the input event log and the quality of the discovered process models. To this end, we review the state-of-the-art process complexity measures, propose a new process complexity measure based on graph entropy, and analyze this set of complexity measures on an extensive collection of event logs and corresponding automatically discovered process models. Our analysis shows that many process complexity measures correlate with the quality of the discovered process models, demonstrating the potential of using complexity measures as predictors for the quality of process models discovered with state-of-the-art process discovery algorithms. This finding is important for process mining research, as it highlights that not only algorithms, but also connections between input data and output quality should be studied.

中文翻译：

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事件序列的过程复杂性与过程挖掘发现的模型之间的联系

流程挖掘是一个专注于算法设计的研究领域，这些算法可以通过分析历史流程执行数据（称为事件日志）自动提供对业务流程的洞察。最流行的算法是用于自动流程发现的算法，其最终目标是生成总结输入事件日志中记录的行为的最佳流程模型。在过去的十年中，已经提出了几种流程发现算法，但直到现在，这项研究都是由隐式假设驱动的，即更好的算法会发现更好的流程模型，而不管输入事件日志的特征如何。在本文中，我们退后一步并质疑该假设。具体来说，我们调查了捕获输入事件日志特征的度量与发现的过程模型的质量之间的关系是什么。为此，我们回顾了最先进的流程复杂性度量，提出了一种基于图熵的新流程复杂性度量，并在大量事件日志和相应的自动发现的流程模型上分析了这组复杂性度量。我们的分析表明，许多流程复杂性度量与发现的流程模型的质量相关，证明了使用复杂性度量作为通过最先进的流程发现算法发现的流程模型质量的预测指标的潜力。这一发现对于过程挖掘研究很重要，因为它强调了不仅算法，