Browsing by Author "Dr. Christopher Healey, Committee Chair"

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    Visualization Search Strategies
    (2004-11-22) Mehta, Reshma Girish; Dr. Munindar Singh, Committee Member; Dr. Peng Ning, Committee Member; Dr. Christopher Healey, Committee Chair
    Innovations in high performance computing and high bandwidth networks have led to the onset of data explosion. Along with large size, the datasets are typically multivariate. The need for effective exploration of this data has led to the area of multidimensional visualization. Research in low level human visual system has resulted in the construction of perceptual guidelines that can produce effective visualizations. However, application of these guidelines to a dataset requires users to be experts in the visualization domain. ViA is a semi-automated visualization assistant that uses perceptual guidelines along with a heuristic search algorithm to generate perceptually salient visualizations. This thesis aims to study the behavior of the current hint-based search strategy and determine its efficiency. We compare hint-based search with two generic heuristic search algorithms, simulated annealing and reactive tabu search, by adapting them to ViA's search domain. We use time efficiency, space efficiency, ability to find multiple optimal solutions and optimality as performance metrics. Further, in order to "see" the areas of the search space explored by each search algorithm, we have developed a focus + context visualization system using hyperbolic geometry.
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    Visualizing and comparing multivariate scalar data over a geographic map.
    (2009-10-05) Ramachandran, Karthik; Dr. Christopher Healey, Committee Chair; Dr. Robert St. Amant, Committee Member; Dr. Ben Watson, Committee Member
    Recent technological advances and innovations have given us ways to easily and quickly extract large sets of data, but the increasing amounts of raw information only highlight the lack of good visualization or pattern recognition techniques to interpret the data. The objective of the research is to build techniques to effectively visualize multivariate scalar entities over a topographical map. Our goals are; a. rapid interpretation of the magnitude of a scalar entity at a particular spatial location; b. rapid comparison of the magnitudes of different scalar entities ; c. rapid comparison of the scalar entities across different regions of the map. Based on past research, I chose to investigate creating a texture of symmetrical units called texels. Each texel contains a fixed number of color-mapped hexagonal blocks representing each scalar entity. Users can dynamically choose the static variables to be visualized and the size of the texels. The research started as an experiment to visualize the United States Election results to represent the degree of variation in the results and the votes shared among the contestants. In addition to the election data my technique has also been applied to the United States census data, geographical and meteorological data to highlight interesting results.