Browsing by Author "Kocherlakota, Sarat Mohan"

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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.
Perception Driven Search Strategies For Effective Multi-Dimensional Visualization
(2003-02-13) Kocherlakota, Sarat Mohan; Thomas L. Honeycutt, Committee Member; Robert St. Amant, Committee Member; Christopher G. Healey, Committee Chair
Tracking and analysing large amounts of information in many different application areas is a critical problem. One approach to address this problem, is the use of multi-dimensional visualizations to represent large datasets. Visualizations can be constructed effectively by the use of visual features and properties like color and texture. Our objective is to construct multi-dimensional visualizations using perceptually salient visual features which support rapid visual analysis and exploration of large datasets. We use a visualization system called ViA use to construct effective visualizations. We present a search technique incorporated in ViA, that finds effective attribute-feature mappings to represent multi-dimensional datasets in a perceptually salient fashion. ViA evaluates the salience of attribute-feature mappings using evaluation engines. These evaluation engines also suggest hints that recommend how the mapping can be improved perceptually. The search technique we developed, uses dataset properties, and the hints generated by the evaluation engines to quickly and efficiently produce perceptually salient mappings. Perceptual guidlines were established from studies and experiments on human perception. ViA works as a semi-automated visualization system that uses effective search technique to find salient mappings. Applying ViA to practical datasets indeed proves the effectiveness of ViA. We think ViA can also produce salient visualizations in a variety domain areas since the guidelines for generation of effective visualizations are based on human perception.
Summarization techniques for visualization of large, multidimensional datasets
(North Carolina State University. Dept. of Computer Science, 2005) Kocherlakota, Sarat Mohan; Healey, Christopher G.