Abstract
This is a survey article on recent developments in visualization of large data, especially that of multivariate volume data. We present two essential ingredients. The first one is the mathematical background, especially the singularity theory of differentiable mappings, which enables us to capture topological features of given multivariate data in a mathematically rigorous way. The second one is a new development in computer science, called the joint contour net, which can encode topological structures of a given set of multivariate data in an efficient and robust way. Some applications to real data analysis are also presented.
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Acknowledgments
The “Hurricane Isabel” data set was produced by the Weather Research and Forecast (WRF) model, courtesy of NCAR and the U.S. National Science Foundation (NSF). This research has been partially supported by JSPS KAKENHI Grant Number 25540041, and EPSRC EP/J013072/1.
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Saeki, O. et al. (2014). Visualizing Multivariate Data Using Singularity Theory. In: Wakayama, M., et al. The Impact of Applications on Mathematics. Mathematics for Industry, vol 1. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54907-9_4
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DOI: https://doi.org/10.1007/978-4-431-54907-9_4
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