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

该项目解决了在数据分析和可视化过程中整合和传达不确定性的根本挑战。为了计算不确定性，该项目旨在开发一个通用的不确定性分析模型，该模型独立于可视化方法和应用领域。技术方法的一个重要和新颖的方面是将不确定性分为高和低概念层次，每一层次都有单独但互补的分析方法。 最终的目标是允许在任务和查询的水平上评估不确定性，并辅以有效的可视化。该项目主要研究三个问题：（1）发展一个混合概率-可能性不确定性分析框架，用于表示低水平的计算不确定性，沿着以各种视觉形式显示这种不确定性的方法;（2）用于表示处理人类输入和视觉/感知不确定性的高级任务相关不确定性的模糊分析的公式化，同时弥合低水平不确定性和高水平不确定性之间的差距;以及（3）研究可视化显示计算中不确定性演变的方法，实现不确定性导航，其中不确定性的探索和修改可以在相同的上下文中发生。该项目借鉴了可视化之外的许多研究领域，包括不确定性管理、模糊逻辑、信息论、数据分析、仿真、计算机视觉、人机交互、计算机图形学和高性能计算，其潜在影响扩展到这些领域和其他领域。可验证可视化的最终目标是有利于可视化和可视化分析，但也有利于在其他领域采用可视化，例如医学成像，计算生物学和可视化分析。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）传播给可视化社区及其他社区，其中将包括项目状态更新和图像、视频和原型软件等交付成果。与拟议的研究相辅相成的是一项教育议程，包括将研究成果纳入教学，为参与研究的学生安排在合作科学家实验室的暑期实习，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.