Densityplot matrix display for large distributed data
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Computer Science—the theory, representation, processing, communication and use of information—is fundamentally transforming every aspect of human endeavor. The Department of Computer Science at Iowa State University advances computational and information sciences through; 1. educational and research programs within and beyond the university; 2. active engagement to help define national and international research, and 3. educational agendas, and sustained commitment to graduating leaders for academia, industry and government.
History
The Computer Science Department was officially established in 1969, with Robert Stewart serving as the founding Department Chair. Faculty were composed of joint appointments with Mathematics, Statistics, and Electrical Engineering. In 1969, the building which now houses the Computer Science department, then simply called the Computer Science building, was completed. Later it was named Atanasoff Hall. Throughout the 1980s to present, the department expanded and developed its teaching and research agendas to cover many areas of computing.
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1969-present
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- College of Liberal Arts and Sciences (parent college)
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Abstract
Data visualization techniques are essential in large data mining process because of their involvement of direct human interactions. Dynamic interaction is recognized as a critical component for very large, multidimensional data analysis. Most of the visualization tools available today can provide only static displays of large data set. We present a visualization tool, Limn Matrix, in this thesis. Limn Matrix uses scatterplot matrix display because of its power effectiveness in exploring relationships between multiple variables. Limn Matrix uses a density transformation to solve the overplotting problem associated with using scatterplots with large data sets. Overplotting counts are transformed to shades of grays in the density transformation. We developed an indexing structure for Limn Matrix to allow dynamic interactions between scatterplots. Records in a large data set are mapped to the density counts of the plots through these new indexes. By combining sampling techniques with our indices, Limn Matrix can reduce the number of data cases and provide support for real-time interaction with large, multidimensional data.