Data Characterization for Automatically Visualizing Heterogeneous Information

Automated graphical generation systems should be able to design effective presentations for heterogeneous (quantitative and qualitative) information in static or interactive environments. When building such a system, it is important to thoroughly understand the presentation-related characteristics of domain-specific information. We define a data-analysis taxonomy that can be used to characterize heterogeneous information. In addition to capturing the presentation-related properties of data, our characterization takes into account the user's information-seeking goals and visual-interpretation preferences. We use automatically generated examples from two different application domains to demonstrate the coverage of the proposed taxonomy and its utility for selecting effective graphical techniques.

The research described in this overview is supported in part by DARPA Contract DAAL01-94-K-0119, the Columbia Univeristy Center for Advanced Technology in High Performance Computing and Communications in Healthcare (funded by the New York State Science and Technology Foundation), the Columbia Center for Telecommunications Research under NSF Grant ECD-88-11111, ONR Contract N00014-94-1-0564, and a gift from Digital Image Division.

Copyright © 1996 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

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