Dashboards, which comprise multiple views on a single display, help analyze and communicate multiple perspectives of data simultaneously. However, creating effective and elegant dashboards is challenging since it requires careful and logical arrangement and coordination of multiple visualizations. To solve the problem, we propose a data-driven approach for mining design rules from dashboards and automating dashboard organization. Specifically, we focus on two prominent aspects of the organization: arrangement, which describes the position, size, and layout of each view in the display space; and coordination, which indicates the interaction between pairwise views. We build a new dataset containing 854 dashboards crawled online, and develop feature engineering methods for describing the single views and view-wise relationships in terms of data, encoding, layout, and interactions. Further, we identify design rules among those features and develop a recommender for dashboard design. We demonstrate the usefulness of DMiner through an expert study and a user study. The expert study shows that our extracted design rules are reasonable and conform to the design practice of experts. Moreover, a comparative user study shows that our recommender could help automate dashboard organization and reach human-level performance. In summary, our work offers a promising starting point for design mining visualizations to build recommenders.

翻译：仪表板通过在单一显示界面上整合多个视图，能够同时分析和传达数据的多个维度。然而，创建高效且优雅的仪表板颇具挑战性，因为这需要精心且合乎逻辑地布局与协调多个可视化元素。为解决这一问题，我们提出了一种数据驱动的方法，用于从仪表板中挖掘设计规则并实现仪表板的自动化组织。具体而言，我们聚焦于组织中的两个核心方面：布局（描述每个视图在显示空间中的位置、尺寸与排列方式）和协调（描述成对视图之间的交互关系）。我们构建了一个包含854个在线爬取仪表板的新数据集，并开发了特征工程方法，从数据、编码、布局和交互角度描述单个视图及视图间关系。进一步地，我们识别这些特征中的设计规则，并开发了一个仪表板设计推荐系统。通过专家评估和用户研究，我们验证了DMiner的实用性：专家评估表明，提取的设计规则合理且符合专家设计实践；对比用户研究则显示，我们的推荐系统能够辅助自动化仪表板组织，并达到人类水平的表现。总之，本研究为通过设计挖掘构建可视化推荐系统提供了有前景的起点。