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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Veröffentlicht in:	IEEE transactions on visualization and computer graphics Jg. 13; H. 6; S. 1400 - 1407
Hauptverfasser:	Gosink, Luke, Anderson, John, Bethel, Wes, Joy, Kenneth
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2007 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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. 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 user's 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. 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 [abstract truncated by publisher]. 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 user's 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.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 user's 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.
Author	Bethel, Wes Gosink, Luke Anderson, John Joy, Kenneth
Author_xml	– sequence: 1 givenname: Luke surname: Gosink fullname: Gosink, Luke email: ljgosink@ucdavis.edu organization: Institute for Data Analysis and Visualization (IDAV), University of California, Davis – sequence: 2 givenname: John surname: Anderson fullname: Anderson, John email: janderson@ucdavis organization: Institute for Data Analysis and Visualization (IDAV), University of California, Davis – sequence: 3 givenname: Wes surname: Bethel fullname: Bethel, Wes email: ewbethel@lbl.gov organization: Computing Sciences Division and Scientific Visualization Group, Lawrence Berkeley National Laboratory – sequence: 4 givenname: Kenneth surname: Joy fullname: Joy, Kenneth email: kijoy@ucdavis.edu organization: Institute for Data Analysis and Visualization (IDAV), University of California, Davis
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SubjectTerms	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
Title	Variable Interactions in Query-Driven Visualization
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