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A framework for information visualization spreadsheets
Publisher:
  • University of Minnesota
  • Computer Science Dept. 136 Lind Hall 207 Church Street Minneapolis, MN
  • United States
ISBN:978-0-599-20648-9
Order Number:AAI9921428
Pages:
146
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Abstract

Information has become interactive. Information visualization is the design and creation of interactive graphic depictions of information by combining principles in the disciplines of graphic design, cognitive science, and interactive computer graphics. As the volume and complexity of the data increases, users require more powerful visualization tools that allow them to more effectively explore large abstract datasets.

This thesis seeks to make information visualization more accessible to potential users by creating a “Visualization Spreadsheet”, where each cell can contain an entire set of data represented using interactive graphics. Just as a numeric spreadsheet enables exploration of numbers, a visualization spreadsheet enables exploration of visual forms of information. Unlike numeric spreadsheets, which store only simple data elements and formulas in each cell, a cell in the Visualization Spreadsheet can hold an entire abstract data set, selection criteria, viewing specifications, and other information needed for a full-fledged information visualization. Similarly, intra-cell and inter-cell operations are far more complex, stretching beyond simple arithmetic and string operations to encompass a range of domain-specific operators.

The complexity of operations and interactions requires a visualization framework that is easily understandable to both end-users and visualization designers. This thesis develops and discusses the general utility of a novel visualization framework, and validates the framework by applying it to various visualization techniques and showing several systems that illustrate some of these research issues. We show that the spreadsheet approach facilitates certain visual user tasks that are more difficult using other approaches. The underlying approach in our work allows domain experts to define new data types and data operations, and enables visualization experts to incorporate new visualizations, viewing parameters, and view operations.

Contributors
  • University of Minnesota Twin Cities
  • DeepMind Technologies Limited
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