Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase. Many organizations have to carry out this phase, before they can utilize business process management and its benefits. Yet, research towards this is severely restrained by an apparent lack of large and high-quality datasets. This lack of data can be attributed to, among other things, an absence of proper tool assistance for dataset creation, resulting in high workloads and inferior data quality. We explore two assistance features to support dataset creation, a recommendation system for identifying process information in the text and visualization of the current state of already identified process information as a graphical business process model. A controlled user study with 31 participants shows that assisting dataset creators with recommendations lowers all aspects of workload, up to $-51.0\%$, and significantly improves annotation quality, up to $+38.9\%$. We make all data and code available to encourage further research on additional novel assistance strategies.

本研究解决了在业务流程发现阶段高耗时和高成本的问题，特别关注缺乏大型和高质量数据集的现状。通过提出推荐系统和可视化工具来辅助数据集创建，我们的实验结果表明，这些功能显著减轻了工作负担并提高了注释质量，具有促进业务流程管理的潜力。

辅助数据标注用于从文本文档提取业务流程信息