CAREER: Assisted Navigation in Large Visualization Spaces

这个项目是对帮助导航大而复杂的信息空间的方法的研究。尽管这些研究的结果与许多研究领域相关，但人们的兴趣将集中在构建导航系统上，该系统旨在帮助观众可视化、探索和分析大型多维数据集。帮助分析和导航这些类型的数据集的方法被美国能源部/国家科学基金会联合小组特别提到为未来可视化研究的一个重要的未决问题。这项工作将结合详细的局部显示和对数据集中感兴趣区域的位置和结构的高级全球概述。局部视觉将使用感知线索来利用低级人类视觉系统的能力。全球概览将分两个不同的阶段进行。首先，将使用以下组合来识别感兴趣的元素：(1)由观众提供的显性规则，以及(2)通过观察观众选择什么、他们移动到哪里以及他们检查什么而建立的隐含规则。接下来，这些元素将被聚集到一个或多个感兴趣的区域中。将研究平面三角剖分和最小生成树等图形构造技术的使用，以将元素链接在一起。(1)通过图形遍历算法的应用来支持高效导航的底层图形，以及(2)提供有效的全局概览来可视化感兴趣的区域以及它们之间存在的关系的图形。将设计一组验证实验来确定我们的导航技术的优点和局限性。来自海洋学和电子商务领域的数据集将用于在实际的真实环境中测试该系统。第一组实验将与领域专家合作，部分是为了提供关于我们系统的轶事反馈，部分是为了识别在可视化过程中执行的基本导航和探索任务。这些任务将被整合到一个对照实验中，研究我们的系统相对于没有导航辅助的系统的性能，以及专门为显示这些类型的复杂数据集而设计的现有Focus+上下文可视化技术。可视化、导航、感知和观众偏好的自动推理方面的研究为设计包括计算机图形学和科学可视化、认知心理学和各种现实世界应用领域的教学在内的多学科课程提供了独特的机会。教育计划包括建设一个教学可视化实验室，从学术界和工业界寻找合作者，设计一个自成一体的课程课程，以及创建一系列真实世界的可视化项目，以鼓励学生参与我们的研究项目。该实验室将包括最先进的图形工作站，以及特定于可视化的硬件和软件。优先考虑的是让学生接触到新出现的研究问题和现实世界的可视化问题。这在一定程度上是通过建立课程项目，在我们的可视化实验室以及位于我们校园的学术和工业研究中心向学生介绍积极的研究项目。课程将在影响科学可视化、计算机图形学、计算机视觉和人工智能的认知和知觉心理学领域指导学生。结果将是一系列课程，向心理学、工程学、自然科学和其他学科的学生介绍感知和辅助计算技术的理论和实践问题，以及它们与科学可视化和计算机图形学的关系。

This project is an investigation of methods for assisting with the navigation of large, complexinformation spaces. Although results from these studies are relevant to a number of research areas, interest will be focused on construction of a navigation system designed to help viewers visualize,explore, and analyze large, multidimensional datasets. Methods to assist with the analysis and navigation of these types of datasets was specifically cited as animportant open problem by the joint DOE/NSF panel on future research in visualization.The work will combine a detailed local display and a high-level global overview of the locations and structure of areas of interest within the dataset. The local view will use perceptual cues to harness the abilities of the low-level human visual system. The global overview will be built in two separate stages. First, elements of interest will be identified using a combination of: (1) explicit rules provided by the viewer, and (2) implicit rules built by watching what viewers select, where they move, and what they examine. Next, the elements will be clustered into one or more areas of interest. The use of graph construction techniques like planar triangulations and minimum spanning trees will be investigated to link the elements together. An underlying graph that: (1) supports efficient navigation via the application of graph traversal algorithms, and (2) provides an effective global overview to visualize the areas of interest and the relationships that exist between them will be sought.A set of validation experiments will be designed to identify the strengths and limitations of our navigationtechniques. Datasets from the oceanography and e-commerce domains will be used to test the system in apractical, real-world environment. The first set of experiments will work with domain experts, in part to provide anecdotal feedback on our system, and in part to identify fundamental navigation and exploration tasks performed during visualization. These tasks will then be integrated into a controlled experiment that studies the performance of our system vis-a-vis a system without navigation aids, and existing focus+context visualization techniques specifically designed to display these types of large, complex datasets.The research in visualization, navigation, perception, and automated inference of viewerpreferences provides a unique opportunity to design a multidisciplinary course curriculum that includes instruction in computer graphics and scientific visualization, cognitive psychology, and a variety of real-world application areas.The education plan includes the construction of an instructional visualization laboratory, the identification of collaborators from academia and industry, the design of a self-contained course curriculum, and the creation of a collection of real-world visualization projects to encourage student participation in our research programs. The laboratory will include state-of-the-art graphics workstations, as well as visualization-specific hardware and software.The priority is to expose students to emergingresearch issues and real-world visualization problems. This will be accomplished in part by building course projects that introduce students to active research programs, both in our visualization laboratory and in academic and industrial research centers located on our campus.The curriculum will instruct students in areas of cognitive and perceptual psychology that impact scientific visualization, computer graphics, computer vision, and artificial intelligence.The result will be a collection of courses that introduce students of psychology, egineering, natural science and other disciplines to both the theoretical and practical issues of perception and assisted computation techniques, and their relationship to scientific visualization and computer graphics.