CAREER: Assisted Navigation in Large Visualization Spaces

North Carolina State University, Raleigh NC

Investigators

Abstract

This project is an investigation of methods for assisting with the navigation of large, complex information 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 an important 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 navigation techniques. Datasets from the oceanography and e-commerce domains will be used to test the system in a practical, 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 viewer preferences 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 emerging research 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.