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.

翻译：Jupyter等计算笔记本在探索性数据分析和洞察发现中广泛应用。尽管采用模块化结构，但笔记本视觉上呈现为包含文本、代码、可视化图表和数据表格的单一交错单元格线程，这种组织方式可能导致数据工作流混乱且难以理解。此外，编程经验有限的用户在快速参与分析过程中可能面临困难。本研究设计并实现了一个交互式笔记本框架WHATSNEXT，旨在通过基于洞察的用户引导，支持低代码的可视化数据探索。具体而言，我们（1）重新设计了标准笔记本单元格，加入推荐面板以建议下一步可能的探索性问题或分析操作；（2）创建了动态交互式树状可视化，反映单元格间的分析依赖关系，使用户能够直观了解数据探索线程的结构并回溯至先前步骤。