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About
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Team Members:
Bob Amar,
James Eagan,
John Stasko
While information visualization is quickly gaining prominence as a
tool of choice in scientific and business analysis, successful
decision-making and analysis with these tools tend to be more a matter
of serendipity and user knowledge rather than of intentional design and
specific analytic support. To supplement lower-level taxonomies of visual
tasks, such as "identify", "associate", and "correlate", we have posited
a set of higher-level knowledge precepts that help bridge the
analytic gaps between data representation and higher-level analytic
tasks, such as forecasting, learning new domains, and cost-benefit
analysis.
We are also currently thinking about the correspondence of lower-level
visual tasks to actual analyst questions and user interactions within
visualization tools. We believe that a more specific focus on analytic
activity within these lower-level tasks can lead to better task
conformance, better evaluation methods, and a better understanding of the
components of higher-level analytic activity.
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