Embedding visual representations within original hierarchical tables can mitigate additional cognitive load stemming from the division of users' attention. The created hierarchical table visualizations can help users understand and explore complex data with multi-level attributes. However, because of many options available for transforming hierarchical tables and selecting subsets for embedding, the design space of hierarchical table visualizations becomes vast, and the construction process turns out to be tedious, hindering users from constructing hierarchical table visualizations with many data insights efficiently. We propose InsigHTable, a mixed-initiative and insight-driven hierarchical table transformation and visualization system. We first define data insights within hierarchical tables, which consider the hierarchical structure in the table headers. Since hierarchical table visualization construction is a sequential decision-making process, InsigHTable integrates a deep reinforcement learning framework incorporating an auxiliary rewards mechanism. This mechanism addresses the challenge of sparse rewards in constructing hierarchical table visualizations. Within the deep reinforcement learning framework, the agent continuously optimizes its decision-making process to create hierarchical table visualizations to uncover more insights by collaborating with analysts. We demonstrate the usability and effectiveness of InsigHTable through two case studies and sets of experiments. The results validate the effectiveness of the deep reinforcement learning framework and show that InsigHTable can facilitate users to construct hierarchical table visualizations and understand underlying data insights.

翻译：在原始层次化表格中嵌入视觉表征可以减轻因用户注意力分散而产生的额外认知负荷。所创建的层次化表格可视化可以帮助用户理解和探索具有多层级属性的复杂数据。然而，由于层次化表格的转换和嵌入子集的选择存在多种选项，层次化表格可视化的设计空间变得非常庞大，构建过程也显得繁琐，阻碍了用户高效构建蕴含丰富数据洞察的层次化表格可视化。我们提出了InsigHTable，一个混合主动、洞察驱动的层次化表格转换与可视化系统。我们首先定义了层次化表格内部的数据洞察，该定义考虑了表格表头中的层次结构。由于层次化表格可视化的构建是一个序贯决策过程，InsigHTable集成了一个包含辅助奖励机制的深度强化学习框架。该机制解决了构建层次化表格可视化过程中奖励稀疏的挑战。在此深度强化学习框架内，智能体通过与分析师协作，持续优化其决策过程，以创建能够揭示更多洞察的层次化表格可视化。我们通过两个案例研究和多组实验展示了InsigHTable的可用性和有效性。结果验证了深度强化学习框架的有效性，并表明InsigHTable能够帮助用户构建层次化表格可视化并理解潜在的数据洞察。