Variable Interactions in Query-Driven Visualization

Our ability to generate ever-larger, increasingly-complex data, has established the need for scalable methods that identify, and provide insight into, important variable trends and interactions. Query-driven methods are among the small subset of techniques that are able to address both large and hig...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics Vol. 13; no. 6; pp. 1400 - 1407
Main Authors: Gosink, Luke, Anderson, John, Bethel, Wes, Joy, Kenneth
Format: Journal Article
Language:English
Published: United States IEEE 01.11.2007
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Chemicals
Combustion
Correlation
Data visualization
Distribution functions
Fires
Histograms
Large-scale systems
Mathematical analysis
Mathematical models
Multivariate Data
Performance analysis
Query processing
Query-Driven Visualization
Throughput
Trends
Utilities
Visualization
ISSN:1077-2626, 1941-0506
Online Access:Get full text
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Summary:Our ability to generate ever-larger, increasingly-complex data, has established the need for scalable methods that identify, and provide insight into, important variable trends and interactions. Query-driven methods are among the small subset of techniques that are able to address both large and highly complex datasets. This paper presents a new method that increases the utility of query-driven techniques by visually conveying statistical information about the trends that exist between variables in a query. In this method, correlation fields, created between pairs of variables, are used with the cumulative distribution functions of variables expressed in a users query. This integrated use of cumulative distribution functions and correlation fields visually reveals, with respect to the solution space of the query, statistically important interactions between any three variables, and allows for trends between these variables to be readily identified. We demonstrate our method by analyzing interactions between variables in two flame-front simulations.
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ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2007.70519