This paper presents a command-line tool, called Entropia, that implements a family of conformance checking measures for process mining founded on the notion of entropy from information theory. The measures allow quantifying classical non-deterministic and stochastic precision and recall quality criteria for process models automatically discovered from traces executed by IT-systems and recorded in their event logs. A process model has "good" precision with respect to the log it was discovered from if it does not encode many traces that are not part of the log, and has "good" recall if it encodes most of the traces from the log. By definition, the measures possess useful properties and can often be computed quickly.

中文翻译：

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Entropia：用于过程挖掘的一系列基于熵的一致性检查措施

本文介绍了一种名为 Entropia 的命令行工具，它基于信息论中的熵概念实现了一系列用于流程挖掘的一致性检查措施。这些措施允许量化经典的非确定性和随机精度和召回质量标准，用于从 IT 系统执行的跟踪中自动发现并记录在其事件日志中的过程模型。如果一个过程模型没有编码许多不属于日志的痕迹，那么它对于从中发现的日志来说具有“良好”的精度，如果它编码了日志中的大部分痕迹，则它具有“良好”的召回率。根据定义，这些度量具有有用的特性并且通常可以快速计算。