Browsing by Author "Dr. Thomas L. Honeycutt, Committee Member"

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    Analyzing Software Artifacts through Singular Value Decomposition to Guide Development Decisions
    (2007-09-25) Sherriff, Mark Stephen; Dr. Jason A. Osborne, Committee Member; Dr. Thomas L. Honeycutt, Committee Member; Dr. Mladen A. Vouk, Committee Member; Dr. Laurie A. Williams, Committee Chair
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    Interactive Visual Summarization for Visualizing Large, Multidimensional Datasets
    (2007-03-21) Kocherlakota, Sarat Mohan; Dr. Rada Y. Chirkova, Committee Member; Dr. Thomas L. Honeycutt, Committee Member; Dr. Christopher G. Healey, Committee Chair; Dr. Xiaosong Ma, Committee Member
    Because of its ability to help users analyze and explore data from a diverse set of domains, visualization is becoming integral to the knowledge discovery process. However, existing visualization techniques for displaying large, multidimensional datasets often produce detailed, cluttered images that overwhelm the user's ability to effectively absorb the underlying data. To visualize such datasets effectively we have developed a visual summarization framework that intelligently summarizes datasets by extracting its important and relevant characteristics prior to visualization. The summaries are then visualized both in place of the original data, or along with the original data. Our approach performs this summarization in three broad steps. First, size and dimensionality of the data are reduced meaningfully. Next, patterns and dependencies in the form of association rules, along with outliers are extracted from the reduced data. Finally, these summary characteristics are visualized using techniques that are aimed at enhancing the comprehension of the data. Summary characteristics, as well as summarization steps are also recorded. Our framework is designed to harness the benefits of both visual and non-visual methods to intuitively guide users to produce relevant data summaries. Initial results in applying our approach to practical datasets suggest that our approach could be used to generate effective visual summaries of large, multidimensional datasets from a wide variety of domains and applications.
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    Investigating Aesthetic Visualizations
    (2007-01-03) Tateosian, Laura Gray; Dr. James C. Lester, Committee Member; Dr. Thomas L. Honeycutt, Committee Member; Dr. Edward W. Davis, Committee Member; Dr. Christopher G. Healey, Committee Chair
    Visualizations enable scientists to inspect, interpret, and analyze large multi-dimensional data sets. Effective visualizations are designed to both orient and engage viewers by directing attention in response to a visual stimulus, and then encouraging a viewer's vision to linger at a given image location. Research into human visual perception provides information about how to orient viewers, using salient visual features, such as color, orientation, and flicker. Less is known about how to build engaging visualizations. Increasing the aesthetic merit of visualizations is a promising approach to increasing engagement. Intuition suggests that visualizations with a more aesthetic presentation style will be judged as more artistic, but this is an open problem. In this thesis, we explored an important question pertaining to creating aesthetic visualizations: Is it possible to affect the perceived artistic merit of a scientific visualization? To investigate this question, we developed three new painterly visualization techniques, designed to vary different visual qualities important to aesthetics: interpretational complexity (IC), indication and detail (ID), and visual complexity (VC). We conducted four experiments to investigate how these qualities affect the aesthetics. Observers were asked to rank IC, ID, and VC images, together with Master abstract and Impressionist paintings on five questions: artistic merit, pleasure, arousal, meaningfulness, and complexity. Although realistic Impressionist paintings consistently ranked as most artistic, computer visualizations were considered as artistic as and more pleasing than Master abstractionist artwork in certain situations. There was also a significant preference for aesthetic visualizations that used more sophisticated presentation styles. This provides strong evidence that our aesthetic techniques can increase the perceived artistic merit of a visualization, possibly leading to a significant improvement in the visualizations's ability to engage its viewers. We applied our experimental techniques to real meteorological and supernova data sets, to explore their capabilities in a real-world setting. Anecdotal feedback from a domain expert in astrophysics was strongly positive, further supporting the theory that enhancing the artistic merit of visualizations is a worthwhile contribution to the scientific community.