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.
翻译:分析仪表板在商业情报中很受欢迎,有助于以多种图表进行洞察发现。然而,创建有效的仪表板要求用户具备足够的数据分析背景,熟悉诸如Power BI等专业工具。 要创建仪表板,用户就必须通过选择数据列和探索不同的图表组合来配置图表,以优化洞察力的交流,即试探和试探。最近的研究已开始使用深层次的学习方法来生成仪表板,以降低可视化创建的负担。然而,由于缺乏大规模和高质量的仪表板数据集,这种努力受到极大阻碍。在这项工作中,我们提议利用深层强化学习来生成分析仪表板,以便利用成熟的可视化知识和强化学习的估计能力。具体地说,我们利用可视化知识来构建一种培训环境,奖励各种代理人,以设计良好的代理网络来探索和模仿人类的探索行为。深层强化学习模式的效用通过粘合研究和用户研究得到证明。最后,我们的工作开辟了新的机会,在没有事先培训数据集的情况下开发有效的基于ML的可视化建议。