DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning

Dazhen Deng, Aoyu Wu, Huamin Qu, Yingcai Wu

View presentation:2022-10-20T15:00:00ZGMT-0600Change your timezone on the schedule page
2022-10-20T15:00:00Z
Exemplar figure, described by caption below
Screenshot of the DashBot Interface showing the generation of a dashboard named “Insights about wind in seattle-weather”. The interface consists of a table view (A), a topic list (B), a chart editor (C), a canvas view (D), and a recommendation view (E).

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Abstract

Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to configure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Specifically, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets.