论文标题
Entropia：一个基于熵的一系列一系列一致性检查工艺挖掘的措施
Entropia: A Family of Entropy-Based Conformance Checking Measures for Process Mining
论文作者
论文摘要
本文提出了一种称为Entropia的命令行工具，该工具实现了一系列一系列的检查措施，用于从信息理论中建立在熵概念上的过程挖掘。这些措施允许量化经典的非确定性和随机精度，并为从IT系统执行并在其事件日志中记录的过程中自动发现的过程模型的过程模型回忆质量标准。一个过程模型对日志具有“良好”的精度，如果它没有编码许多不是日志的痕迹，并且如果它从日志中编码大多数痕迹，则它具有“良好”召回。根据定义，这些度量具有有用的属性，并且通常可以快速计算。
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.
