CAREER: Assisted Navigation in Large Visualization Spaces

这个项目是一个调查的方法，以协助导航的大型，复杂的信息空间。虽然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一个导航系统上，该系统旨在帮助观众可视化，探索和分析大型多维数据集。方法来协助分析和导航这些类型的数据集被特别引用作为一个重要的开放性问题，由美国能源部/美国国家科学基金会联合小组在未来的研究在visualization.The工作将结合联合收割机详细的本地显示和高层次的全球概述的位置和结构的数据集内感兴趣的领域。局部视图将使用感知线索来利用低级人类视觉系统的能力。全球概览将分两个阶段进行。首先，感兴趣的元素将使用以下组合来识别：（1）由查看者提供的显式规则，以及（2）通过观察查看者选择什么、他们移动到哪里以及他们检查什么来构建的隐式规则。接下来，元素将被聚集到一个或多个感兴趣的区域中。使用图形构造技术，如平面三角剖分和最小生成树将被调查的元素连接在一起。一个基本的图表，它：（1）通过应用图遍历算法支持高效的导航;（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。一组验证实验将被设计来识别我们的导航技术的优势和局限性。来自海洋学和电子商务领域的数据集将被用来测试系统在apractical，真实世界的环境。第一组实验将与领域专家合作，部分是为了提供我们系统的轶事反馈，部分是为了确定可视化过程中执行的基本导航和探索任务。然后将这些任务整合到一个受控实验中，该实验研究我们的系统与没有导航辅助的系统相比的性能，以及专门设计用于显示这些类型的大型复杂数据集的现有焦点+上下文可视化技术。和观众偏好的自动推理提供了一个独特的机会，设计一个多学科的课程，包括计算机图形教学，该教育计划包括建设一个教学可视化实验室，确定来自学术界和工业界的合作者，设计一个独立的课程，并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，以及可视化特定的硬件和软件。优先事项是让学生接触新兴的研究问题和现实世界的可视化问题。这将部分通过建立课程项目来完成，这些课程项目将向学生介绍我们的可视化实验室以及位于我们校园内的学术和工业研究中心的积极研究项目。该课程将指导学生认知和感知心理学领域，这些领域影响科学可视化，计算机图形学，计算机视觉，和人工智能。其结果将是一系列课程，向心理学，教育学，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.