WHATSNEXT：使用交互式、低代码笔记本进行富含指导的探索性数据分析
WHATSNEXT: Guidance-enriched Exploratory Data Analysis with Interactive, Low-Code Notebooks
AI总结：
针对计算笔记本结构混乱、低代码用户难以上手的问题，本文提出 WHATSNEXT 框架，通过下一步推荐面板和动态分析依赖树支持低代码可视化探索。
摘要：
诸如 Jupyter 之类的计算笔记本广泛用于探索性数据分析和洞察发现。尽管采用基于模块的结构，笔记本在视觉上呈现为一条单一脉络，其中交错排列着包含文本、代码、可视化和表格的单元格，这可能缺乏条理，并遮蔽用户的数据分析工作流。此外，编程专业知识有限的用户可能难以快速参与分析过程。在本工作中，我们设计并实现了一个交互式笔记本框架 WHATSNEXT，目标是通过基于洞察的用户指导来支持低代码可视化数据探索。具体而言，我们（1）重新设计了标准笔记本单元格，使其包含一个推荐面板，用于建议可能的下一步探索问题或可采取的分析操作；（2）创建了一种交互式动态树状可视化，以反映笔记本单元格之间的分析依赖关系，从而让用户能够轻松查看数据探索脉络的结构并回溯到先前步骤。
英文摘要：
Computational notebooks such as Jupyter are popular for exploratory data analysis and insight finding. Despite the module-based structure, notebooks visually appear as a single thread of interleaved cells containing text, code, visualizations, and tables, which can be unorganized and obscure users' data analysis workflow. Furthermore, users with limited coding expertise may struggle to quickly engage in the analysis process. In this work, we design and implement an interactive notebook framework, WHATSNEXT, with the goal of supporting low-code visual data exploration with insight-based user guidance. In particular, we (1) re-design a standard notebook cell to include a recommendation panel that suggests possible next-step exploration questions or analysis actions to take, and (2) create an interactive, dynamic tree visualization that reflects the analytic dependencies between notebook cells to make it easy for users to see the structure of the data exploration threads and trace back to previous steps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
