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Geometric Modeling and Imaging--New Trends (GMAI'06)
Visualization of Temporal-Oriented Datasets
London, England
July 05-July 06
ISBN: 0-7695-2604-7
ASCII Text
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Jun Gao, Peter Revesz, "Visualization of Temporal-Oriented Datasets," Geometric Modeling and Imaging--New Trends, pp. 57-62, Geometric Modeling and Imaging--New Trends (GMAI'06), 2006.
 
 
BibTex
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@article{ 10.1109/GMAI.2006.49,
author = {Jun Gao and Peter Revesz},
title = {Visualization of Temporal-Oriented Datasets},
journal ={Geometric Modeling and Imaging--New Trends},
volume = {0},
year = {2006},
isbn = {0-7695-2604-7},
pages = {57-62},
doi = {http://doi.ieeecomputersociety.org/10.1109/GMAI.2006.49},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Geometric Modeling and Imaging--New Trends
TI - Visualization of Temporal-Oriented Datasets
SN - 0-7695-2604-7
SP57
EP62
A1 - Jun Gao,
A1 - Peter Revesz,
PY - 2006
KW - null
VL - 0
JA - Geometric Modeling and Imaging--New Trends
ER -
 
Jun Gao, University of Nebraska-Lincoln, USA
Peter Revesz, Max Planck Institute for Computer Science, Germany
Visualization and interpolation naturally go together and strengthen each other. The paper presents an interpolation method as a pre-processing tool to generate the missing data to be visualized. The method is applied in election prediction, a typical temporal-oriented spatiotemporal dataset. The experimental results demonstrate the ability of the methods to accurately predict the presidential election.
Citation:
Jun Gao, Peter Revesz, "Visualization of Temporal-Oriented Datasets," gmai, pp.57-62, Geometric Modeling and Imaging--New Trends (GMAI'06), 2006
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