Visualization and Visual Analysis of Multi-faceted Scientific Data: a Survey

Johannes Kehrer, Helwig Hauser

ARTICLE, IEEE Trans. Visualization and Computer Graphics (accepted for publication), 2012

• paper

Abstract

Visualization and visual analysis play important roles in exploring, analyzing and presenting scientific data. In many disciplines, data and model scenarios are becoming multi-faceted: data are often spatio-temporal and multi-variate; they stem from different data sources (multi-modal data), from multiple simulation runs (multi-run/ensemble data), or from multi-physics simulations of interacting phenomena (multi-model data resulting from coupled simulation models). Also, data can be of different dimensionality or structured on various types of grids that need to be related or fused in the visualization. This heterogeneity of data characteristics presents new opportunities as well as technical challenges for visualization research. Visualization and interaction techniques are thus often combined with computational analysis. In this survey, we study existing methods for visualization and interactive visual analysis of multi-faceted scientific data. Based on a thorough literature review, a categorization of approaches is proposed. We cover a wide range of fields and discuss to which degree the different challenges are matched with existing solutions for visualization and visual analysis. This leads to conclusions with respect to promising research directions, for instance, to pursue new solutions for multi-run and multi-model data as well as techniques that support a multitude of facets.

Published

IEEE Trans. Visualization and Computer Graphics (accepted for publication)

Media

BibTeX

@article{Kehrer12VisualizationAnd,
  author = {Johannes Kehrer and Helwig Hauser},
  title = {Visualization and Visual Analysis of Multi-faceted Scientific Data: a Survey},
  journal = {IEEE Trans. Visualization and Computer Graphics (accepted for publication)},
  year = {2012},
  abstract = {Visualization and visual analysis play important roles in exploring, 
  analyzing and presenting scientific data. In many disciplines, data and model 
  scenarios are becoming multi-faceted: data are often spatio-temporal and 
  multi-variate; they stem from different data sources (multi-modal data), 
  from multiple simulation runs (multi-run/ensemble data), or from 
  multi-physics simulations of interacting phenomena (multi-model data 
  resulting from coupled simulation models). Also, data can be of 
  different dimensionality or structured on various types of grids that 
  need to be related or fused in the visualization. This heterogeneity 
  of data characteristics presents new opportunities as well as 
  technical challenges for visualization research. Visualization and
  interaction techniques are thus often combined with computational 
  analysis. In this survey, we study existing methods for visualization 
  and interactive visual analysis of multi-faceted scientific data. Based 
  on a thorough literature review, a categorization of approaches is 
  proposed. We cover a wide range of fields and discuss to which degree 
  the different challenges are matched with existing solutions for 
  visualization and visual analysis. This leads to conclusions with 
  respect to promising research directions, for instance, to pursue new 
  solutions for multi-run and multi-model data as well as techniques 
  that support a multitude of facets.},


}