Data science workflows are human-centered processes involving on-demand programming and analysis. While programmable and interactive interfaces such as widgets embedded within computational notebooks are suitable for these workflows, they lack robust state management capabilities and do not support user-defined customization of the interactive components. The absence of such capabilities hinders workflow reusability and transparency while limiting the scope of exploration of the end-users. In response, we developed MAGNETON, a framework for authoring interactive widgets within computational notebooks that enables transparent, reusable, and customizable data science workflows. The framework enhances existing widgets to support fine-grained interaction history management, reusable states, and user-defined customizations. We conducted three case studies in a real-world knowledge graph construction and serving platform to evaluate the effectiveness of these widgets. Based on the observations, we discuss future implications of employing MAGNETON widgets for general-purpose data science workflows.

翻译：数据科学工作流是以人为中心的过程，涉及按需编程与分析。尽管计算型笔记本中嵌入的交互式组件（如控件）适用于此类工作流，但其缺乏稳健的状态管理能力，且不支持用户对交互组件进行自定义定制。这些能力的缺失阻碍了工作流的可复用性与透明性，同时限制了终端用户的探索范围。为此，我们开发了MAGNETON框架——一种用于在计算型笔记本中创建交互式控件的框架，能够支持透明、可复用且可定制的数据科学工作流。该框架增强现有控件，使其支持细粒度交互历史管理、可复用状态及用户自定义定制。我们通过三个案例研究（基于真实知识图谱构建与服务平台）评估了这些控件的有效性。基于观察结果，我们讨论了将MAGNETON控件应用于通用数据科学工作流的未来影响。