What May Visualization Processes Optimize?

In this paper, we present an abstract model of visualization and inference processes, and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified fou...

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Bibliographic Details
Published inIEEE transactions on visualization and computer graphics Vol. 22; no. 12; pp. 2619 - 2632
Main Authors Min Chen, Golan, Amos
Format Journal Article
LanguageEnglish
Published United States IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Analytical models
Cost benefit analysis
cost-benefit ratio
Data analysis
Data models
Data visualization
entropy
Information theory
Mathematical analysis
Mathematical models
model of visualization
Object oriented modeling
Optimization
pipeline
Pipelines
process optimization
Scientific visualization
theory of visualization
Transformations
Visual analytics
Visualization
Workflow
Online AccessGet full text

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Abstract In this paper, we present an abstract model of visualization and inference processes, and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.
AbstractList In this paper, we present an abstract model of visualization and inference processes, and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.
Author Min Chen
Golan, Amos
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SubjectTerms Analytical models
Cost benefit analysis
cost-benefit ratio
Data analysis
Data models
Data visualization
entropy
Information theory
Mathematical analysis
Mathematical models
model of visualization
Object oriented modeling
Optimization
pipeline
Pipelines
process optimization
Scientific visualization
theory of visualization
Transformations
Visual analytics
Visualization
Workflow
Title What May Visualization Processes Optimize?
URI https://ieeexplore.ieee.org/document/7368928
https://www.ncbi.nlm.nih.gov/pubmed/26731770
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Volume 22
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