Browsing by Author "Alan L. Tharp, Committee Member"

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    Deriving Efficient SQL Sequences Via Prefetching
    (2008-01-04) Bilgin, Ahmet Soydan; Christopher G. Healey, Committee Member; Alan L. Tharp, Committee Member; Munindar P. Singh, Committee Co-Chair; Rada Y. Chirkova, Committee Co-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.
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    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.