Ma Ruixian, Mei Honghui, Guan Huihua, Huang Wei, Zhang Fan, Xin Chengye, Dai Wenzhuo, Wen Xiao, Chen Wei
IEEE Trans Vis Comput Graph. 2021 Sep;27(9):3717-3732. doi: 10.1109/TVCG.2020.2980227. Epub 2021 Jul 29.
Dashboard visualizations are widely used in data-intensive applications such as business intelligence, operation monitoring, and urban planning. However, existing visualization authoring tools are inefficient in the rapid prototyping of dashboards because visualization expertise and user intention need to be integrated. We propose a novel approach to rapid conceptualization that can construct dashboard templates from exemplars to mitigate the burden of designing, implementing, and evaluating dashboard visualizations. The kernel of our approach is a novel deep learning-based model that can identify and locate charts of various categories and extract colors from an input image or sketch. We design and implement a web-based authoring tool for learning, composing, and customizing dashboard visualizations in a cloud computing environment. Examples, user studies, and user feedback from real scenarios in Alibaba Cloud verify the usability and efficiency of the proposed approach.
仪表板可视化广泛应用于数据密集型应用程序，如商业智能、运营监控和城市规划。然而，现有的可视化创作工具在仪表板的快速原型制作方面效率低下，因为需要整合可视化专业知识和用户意图。我们提出了一种新颖的快速概念化方法，该方法可以从示例构建仪表板模板，以减轻设计、实现和评估仪表板可视化的负担。我们方法的核心是一种新颖的基于深度学习的模型，该模型可以识别和定位各类图表，并从输入图像或草图中提取颜色。我们设计并实现了一个基于网络的创作工具，用于在云计算环境中学习、组合和定制仪表板可视化。来自阿里云实际场景的示例、用户研究和用户反馈验证了所提方法的可用性和效率。
