InsigHTable: Insight-driven Hierarchical Table Visualization with Reinforcement Learning
<|reference_start|>InsigHTable: Insight-driven Hierarchical Table Visualization with Reinforcement Learning: 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.<|reference_end|>
@article{li2024insightable:,
    title={InsigHTable: Insight-driven Hierarchical Table Visualization with
  Reinforcement Learning},
    author={Guozheng Li, Peng He, Xinyu Wang, Runfei Li, Chi Harold Liu, Chuangxin
  Ou, Dong He, Guoren Wang},
    journal={arXiv preprint arXiv:2405.17229},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17229},
    primaryClass={cs.HC}
}