Propagating Visual Designs to Numerous Plots and Dashboards
Saiful Khan, Phong Hai Nguyen, Alfie Abdul-Rahman, Benjamin Bach, Min Chen, Euan Freeman, Cagatay Turkay
摘要
In the process of developing an infrastructure for providing visualization and visual analytics (VIS) tools to epidemiologists and modeling scientists, we encountered a technical challenge for applying a number of visual designs to numerous datasets rapidly and reliably with limited development resources. In this paper, we present a technical solution to address this challenge. Operationally, we separate the tasks of data management, visual designs, and plots and dashboard deployment in order to streamline the development workflow. Technically, we utilize: an ontology to bring datasets, visual designs, and deployable plots and dashboards under the same management framework; multi-criteria search and ranking algorithms for discovering potential datasets that match a visual design; and a purposely-designed user interface for propagating each visual design to appropriate datasets (often in tens and hundreds) and quality-assuring the propagation before the deployment. This technical solution has been used in the development of the RAMPVIS infrastructure for supporting a consortium of epidemiologists and modeling scientists through visualization.
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- Calliope: Automatic Visual Data Story Generation from a SpreadsheetDanqing Shi, Xinyue Xu, Fuling Sun, Yang Shi 等IEEE VIS 2020 · 被引用 179 次
- PlotThread: Creating Expressive Storyline Visualizations using Reinforcement LearningTan Tang, Renzhong Li, Xinke Wu, Shuhan Liu 等IEEE VIS 2020 · 被引用 70 次
- Vis Ex Machina: An Analysis of Trust in Human versus Algorithmically Generated Visualization RecommendationsRachael Zehrung, Astha Singhal, Michael Correll, Leilani BattleCHI 2021 · 被引用 19 次
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