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

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Multi-Dimensional Data Set Visualization in Portable Computing Environments
(2003-12-16) Romeo, Michael John; Christopher G. Healey, Committee Chair; Peng Ning, Committee Member; Alan L. Tharp, Committee Member
This thesis studies the issues involved with a graphical presentation of large, multi-dimensional data sets. In particular, it will explore the display of such data sets on low cost, limited capacity portable computing environments (e.g. personal digital assistants, cellular phones, portable gaming devices). After a background discussion of the issues involved with scientific visualization and large multi-dimensional data sets, a presentation of several portable computing environments will be discussed along with graphics implementation packages for those environments. This will be followed by a description and presentation of a working implementation, for Pocket PC handheld devices, along with a discussion of some extensions and further areas of study.
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.
Visualization for Combinatorial Auctions
(2006-07-07) Hsiao, Ping-Lin; Christopher G. Healey, Committee Chair; Peter Wurman, Committee Member; Alan L. Tharp, Committee Member
Visualization converts raw data into meaningful graphical images that allow users to rapidly identify and explore values, trends, and patterns in their datasets. Visualization techniques often apply spatial relationships to abstract data to represent the relationships between data elements in a meaningful way. Our specific interest in this thesis is visualizing combinatorial datasets that contain all subsets of a collection of base elements. The combinatorial datasets we study encapsulate results from combinatorial auctions where multiple items are sold simultaneously to multiple bidders. Different bidding strategies and the relationships between them are revealed in our visualizations. We propose a new 2D scheme for concisely visualizing combinatorial datasets. The visualization displays concentric rings composed of arcs, with each base element subset mapped to a single arc. Equal sized subsets are placed on a common ring. The outermost ring contains subsets of size one. Interior rings contain larger subsets. The rings are positioned to try to overlap common base elements as much as possible. This allows viewers to search a local region of the visualization to study the behavior of a given base element (i.e., a given item offered within the combinatorial auction). Additional visual features, including motion, color, and texture are applied to represent auction attributes like the identity of a bidder, which bids win in a particular stage of the auction, and so on. Our visualizations provide viewers with an efficient and effective way to observe how an auction progresses.