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An Information-Theoretic View of Visual Analytics
January/February 2008 (vol. 28 no. 1)
pp. 18-23
ASCII Text
x 
 
Chaomei Chen, "An Information-Theoretic View of Visual Analytics," IEEE Computer Graphics and Applications, vol. 28, no. 1, pp. 18-23, January/February, 2008.
 
 
BibTex
x
 
@article{ 10.1109/MCG.2008.2,
author = {Chaomei Chen},
title = {An Information-Theoretic View of Visual Analytics},
journal ={IEEE Computer Graphics and Applications},
volume = {28},
number = {1},
issn = {0272-1716},
year = {2008},
pages = {18-23},
doi = {http://doi.ieeecomputersociety.org/10.1109/MCG.2008.2},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - IEEE Computer Graphics and Applications
TI - An Information-Theoretic View of Visual Analytics
IS - 1
SN - 0272-1716
SP18
EP23
EPD - 18-23
A1 - Chaomei Chen,
PY - 2008
KW - visual analytics
KW - theory
VL - 28
JA - IEEE Computer Graphics and Applications
ER -
 
Chaomei Chen, Drexel University
Visual analytics is an emerging discipline that helps connect dots. It facilitates analytical reasoning and decision making through integrated and highly interactive visualization of complex and dynamic data and situations. Solving mysteries is only part of the game. Visual analytics must augment analyst and decision-maker capabilities to assimilate complex situations and reach informed decisions. Information theory offers a framework for keeping focused on the right questions.

1. 18 T. Anderson, D. Schum, and W. Twining, Analysis of Evidence, 2nd ed., Cambridge Univ. Press, 2005.2. M. Gladwell, "Open Secrets: Enron, Intelligence, and the Perils of Too Much Information," New Yorker,8 Jan. 2007, http://newyorker.com/reporting/2007/01/08 070108fa_fact_gladwell.3. J.J. Thomas and K.A. Cook, Illuminating the Path: The Research and Development Agenda for Visual Analytics, National Visualization and Analytics Center, 2005.4. S. Kullback and R.A. Leibler, "On Information and Sufficiency," Annals Mathematical Statistics, vol. 22, no. 1, 1951, pp. 79–86.5. E.S. Soofi and J.J. Retzer, "Information Indices: Unification and Applications," J. Econometrics, vol. 107, no. 1, 2002, pp. 17–40.6. P. Pirolli, Information Foraging Theory: Adaptive Interaction with Information, Oxford Univ. Press, 2007.7. J. Gill, "An Entropy Measure of Uncertainty in Vote Choice," Electoral Studies, vol. 24, no. 3, 2005, pp. 371–392.8. L. Bartels, "Issue Voting Under Uncertainty: An Empirical Test," American J. Political Science, vol. 30, no. 4, 1988, pp. 709–728.9. L. Itti and P. Baldi, "A Principled Approach to Detecting Surprising Events in Video," Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), IEEE CS Press, 2005, pp. 631–637.10. R.S. Burt, "Structural Holes and Good Ideas," American J. Sociology, vol. 100, no. 2, 2004, pp. 349–399.

Index Terms:
visual analytics, theory
Citation:
Chaomei Chen, "An Information-Theoretic View of Visual Analytics," IEEE Computer Graphics and Applications, vol. 28, no. 1, pp. 18-23, Jan./Feb. 2008, doi:10.1109/MCG.2008.2
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