CGV: Small: A General Framework for Expressing, Navigating, and Querying Uncertainty in Data Analysis and Visualization Tasks

这个项目解决了在数据分析和可视化过程中纳入和传达不确定性方面的基本挑战。为了计算不确定度，该项目旨在开发一个独立于可视化方法和应用领域的不确定度分析通用模型。技术方法的一个重要而新颖的方面是将不确定性分为高概念层次和低概念层次，每一层次都有独立但互补的分析方法。最终目标是在有效可视化的辅助下，评估任务和询问层面的不确定性。该项目侧重于三个主要研究问题：(1)开发用于表示低级计算不确定性的混合概率-可能性不确定性分析框架，以及用于在各种视觉形态中显示这种不确定性的方法；(2)用于表示与任务相关的高级不确定性的模糊分析的公式，该分析处理人类输入和视觉/感知不确定性，同时弥合低级别不确定性和高级不确定性之间的差距；以及(3)研究如何直观地显示计算中不确定性的演变，从而实现不确定性导航，其中不确定性的探索和修改可以在同一背景下进行。该项目涉及可视化以外的许多研究领域，包括不确定性管理、模糊逻辑、信息论、数据分析、模拟、计算机视觉、人机交互、计算机图形学和高性能计算，其潜在影响延伸到这些领域和更远的领域。可验证可视化的最终目标是有益于可视化和视觉分析，但也促进了可视化在其他领域的采用，例如医学成像、计算生物学和视觉分析。项目成果将通过年度会议、讲习班和教程以及项目网站(http://vis.cs.ucdavis.edu/NSF/IIS1320229)，)传播给视觉界和视觉界，该网站将包括项目最新情况和可交付成果，如图像、视频和原型软件。作为对拟议研究的补充，还有一项教育议程，包括将研究成果纳入教学，为参与合作的科学家实验室的学生安排暑期实习，以及让研究生和本科生参与研究。

This project addresses fundamental challenges in incorporating and conveying uncertainty in the process of data analysis and visualization. In order to compute uncertainty, the project aims to develop a general model for uncertainty analysis that is independent of the visualization method and application domain. An important and novel aspect of the technical approach is the division of uncertainty into high and low conceptual levels, with separate but complementary methods of analysis for each level. The ultimate goal is to allow assessment of uncertainty at the level of tasks and queries, aided by effective visualization. The project focuses on three principal research problems: (1) development of a hybrid probability-possibility uncertainty analysis framework for representing low-level computational uncertainty, along with methods for displaying this uncertainty in various visual modalities; (2) formulation of a fuzzy analysis for representing high-level, task-related uncertainty that handles human input and visual/perceptual uncertainty, while bridging the gap between low-level uncertainty and high-level uncertainty; and (3) investigation into ways to visually display the evolution of uncertainty in computation, enabling uncertainty navigation, in which exploration and modification of uncertainty can occur in the same context. The resulting framework is expected to effectively enable verifiable visualization of uncertainty in data analysis.This project draws from many fields of research outside of visualization, including management of uncertainty, fuzzy logic, information theory, data analysis, simulation, computer vision, human-computer interaction, computer graphics and high performance computing and its potential impact extends to these areas and beyond. The ultimate goal of verifiable visualization is beneficial to visualization and visual analytics, but also facilitates the adoption of visualization in other fields, such as medical imaging, computational biology, and visual analytics to name a few. The project results will be disseminated to the visualization community and beyond through annual conferences, workshops, and tutorials, and also through the project website (http://vis.cs.ucdavis.edu/NSF/IIS1320229), which will include project status updates and deliverables such as images, videos, and prototype software. Complementing the proposed research is an educational agenda, consisting of integration of research results into teaching, arrangement of summer internships for participating students at the collaborating scientists' laboratories, and involvement of graduate and undergraduate students in research.