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Proceedings Paper

Improving visualization by capturing domain knowledge
Author(s): Jonathan Meddes; Eric McKenzie
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Paper Abstract

An effective visualization system depends on a user's ability to interpret a visual representation and made valid inferences. This paper first summarizes the role of domain knowledge when interpreting a visualization. Once the visual perception system has interpreted the visual representation, the user transforms the data into information by the introduction of domain knowledge; these are the rules or items of knowledge that are relevant tot this visual representation allowing the user to make meaningful inferences. In the remainder of the paper we concentrate on a visualization architecture that encapsulates domain knowledge to improve user interpretation of a visual representation. We use an agent-based paradigm to provide a distributed model of computation which moves away from a heavyweight constrained based algorithm towards a lightweight distributed system that empower individual data items. Finally, we present DIME, an implementation based on this approach. DIME is an ongoing research project that tightly integrates data storage, knowledge capture, and information visualization in a 'visual environment'.

Paper Details

Date Published: 28 February 2000
PDF: 10 pages
Proc. SPIE 3960, Visual Data Exploration and Analysis VII, (28 February 2000); doi: 10.1117/12.378895
Show Author Affiliations
Jonathan Meddes, Univ. of Edinburgh (United Kingdom)
Eric McKenzie, Univ. of Edinburgh (United Kingdom)

Published in SPIE Proceedings Vol. 3960:
Visual Data Exploration and Analysis VII
Robert F. Erbacher; Philip C. Chen; Jonathan C. Roberts; Craig M. Wittenbrink, Editor(s)

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