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.

In recent years, the technology and applications of visualizations on large high-resolution displays (LHDs) have received widespread attention because of its perceptual benefits and improved productivity. However, existing work on LHD visualization lacks both comprehensive guidance for design requirements and tools developed for its specific usage scenarios. In this paper, we present the scenarios, design, and implementation of DataV, a Software-as-a-Service (SaaS) visual deployment tool that enables rapid construction and cross-platform publishing of interactive visualization on LHDs. Our framework can support rich components for the high-performance rendering of multi-source heterogeneous data. DataV provides a full-fledged toolchain to help the user efficiently specify layout and interactions. We present its accessibility and impressive visual effects with examples and comparison with Tableau, Power BI, VisComposer, and iVisDesigner. We also report the performance of using DataV for 3D map rendering by comparing it with deck.gl.