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Visualization and visual analysis of multifaceted scientific data: a survey.

Johannes Kehrer¹, Helwig Hauser

¹Department of Informatics, University of Bergen, Bergen, Norway. kehrer@VRVis.at

IEEE Transactions on Visualization and Computer Graphics

|April 18, 2012

Summary

This survey reviews visualization and visual analysis methods for complex scientific data. It categorizes approaches, highlighting challenges and opportunities for multifaceted, multirun, and multimodel data visualization.

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Area of Science:

Scientific visualization
Data analysis
Computational science

Background:

Scientific data is increasingly multifaceted, including spatiotemporal, multivariate, multimodal, multirun, and multimodel characteristics.
Heterogeneous data structures and varying dimensionality pose significant challenges for effective visualization.
Visualization and interaction are crucial for exploring, analyzing, and presenting complex scientific data.

Purpose of the Study:

To survey existing methods for visualization and interactive visual analysis of multifaceted scientific data.
To categorize current approaches based on a thorough literature review.
To identify research gaps and promising future directions in the field.

Main Methods:

Comprehensive literature review of scientific visualization and visual analysis techniques.
Categorization of identified methods based on data characteristics and challenges.
Analysis of existing solutions across various scientific disciplines.

Main Results:

A proposed categorization of visualization and visual analysis approaches for multifaceted data.
Identification of challenges in handling multirun, multimodel, and multimodal data.
Assessment of the suitability of current techniques for diverse scientific data types.

Conclusions:

New solutions are needed for multirun and multimodel data visualization.
Techniques supporting a multitude of data facets require further development.
Future research should focus on addressing the challenges posed by heterogeneous scientific data.