  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.
