提出聚类方法，从带噪声的界面操作日志中精准提取机器人流程自动化所需的标准流程日志。
Accurate and Noise-Tolerant Extraction of Routine Logs in Robotic Process Automation (Extended Version)
- 基于聚类技术，从用户界面操作日志中识别重复性流程模式。
- 在九组带不同噪声水平的日志上，准确率优于现有方法，尤其在高噪声下表现更优。
- 适合需要从真实、不规范操作数据中构建可靠自动化流程的工业场景使用。
机器人流程挖掘旨在通过用户界面识别人力资源执行的常规任务类型，最终目标是发现可支持机器人流程自动化的常规模型。该过程依赖于常规日志的提供。然而，大多数现有工作并未直接面向模型发现，仅关注提取流程中的操作集合，且未在包含不一致执行（即噪声）的场景下进行评估，而此类噪声反映了人类操作中的自然变异和偶尔错误。本文提出一种基于聚类的技术，用于提取常规日志。在来自文献的九组用户界面日志上进行了实验，注入了不同水平的噪声。将该方法与多数并非为日志发现设计但已适配的现有技术进行比较，使用主流评估指标分析结果表明，本方法在噪声环境下能提取出比当前最优技术更准确的常规日志。
原文摘要 · Abstract (English)
Robotic Process Mining focuses on the identification of the routine types performed by human resources through a User Interface. The ultimate goal is to discover routine-type models to enable robotic process automation. The discovery of routine-type models requires the provision of a routine log. Unfortunately, the vast majority of existing works do not directly focus on enabling the model discovery, limiting themselves to extracting the set of actions that are part of the routines. They were also not evaluated in scenarios characterized by inconsistent routine execution, hereafter referred to as noise, which reflects natural variability and occasional errors in human performance. This paper presents a clustering-based technique that aims to extract routine logs. Experiments were conducted on nine UI logs from the literature with different levels of injected noise. Our technique was compared with existing techniques, most of which are not meant to discover routine logs but were adapted for the purpose. The results were evaluated through standard state-of-the-art metrics, showing that we can extract more accurate routine logs than what the state of the art could, especially in the presence of noise.
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