将视觉设计扩展到多个图和仪表板
Propagating Visual Designs to Numerous Plots and Dashboards
摘要：
在为流行病学家和建模科学家开发可视化和可视化分析（VIS）工具的基础设施的过程中，我们遇到了一个技术挑战，即在有限的开发资源下快速可靠地将许多可视化设计应用于众多数据集。在本文中，我们提出了一种技术解决方案来应对这一挑战。在操作上，我们将数据管理、可视化设计、绘图和仪表板部署的任务分开，以简化开发工作流程。从技术上讲，我们利用：一个将数据集、视觉设计、可部署的图和仪表板置于同一管理框架下的本体;用于发现与视觉设计匹配的潜在数据集的多标准搜索和排名算法;以及一个用于将每个视觉设计传播到适当数据集（通常以数十和数百为单位）并在部署之前保证传播质量的可扩展设计用户界面。该技术解决方案已用于RAMPVIS基础设施的开发，以通过可视化支持流行病学家和建模科学家的联盟。
英文摘要：
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-design 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
