Journal ArticleDOI
Visualization and Visual Analysis of Multifaceted Scientific Data: A Survey
Johannes Kehrer,Helwig Hauser +1 more
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TLDR
This survey studies existing methods for visualization and interactive visual analysis of multifaceted scientific data and suggests new solutions for multirun and multimodel data as well as techniques that support a multitude of facets.Abstract:
Visualization and visual analysis play important roles in exploring, analyzing, and presenting scientific data. In many disciplines, data and model scenarios are becoming multifaceted: data are often spatiotemporal and multivariate; they stem from different data sources (multimodal data), from multiple simulation runs (multirun/ensemble data), or from multiphysics simulations of interacting phenomena (multimodel 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 multifaceted 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 multirun and multimodel data as well as techniques that support a multitude of facets.read more
Citations
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Visualizing High-Dimensional Data: Advances in the Past Decade
TL;DR: This work provides guidance for data practitioners to navigate through a modular view of the recent advances in high-dimensional data visualization, inspiring the creation of new visualizations along the enriched visualization pipeline, and identifying future opportunities for visualization research.
Journal ArticleDOI
Optimal Symmetric Multimodal Templates and Concatenated Random Forests for Supervised Brain Tumor Segmentation (Simplified) with ANTsR
Nicholas J. Tustison,K. L. Shrinidhi,Max Wintermark,Christopher R. Durst,Benjamin M. Kandel,James C. Gee,Murray Grossman,Brian B. Avants +7 more
TL;DR: This work introduces a framework for supervised segmentation based on multiple modality intensity, geometry, and asymmetry feature sets that interface the supervised learning capabilities of the random forest model with regularized probabilistic segmentation using the recently developed ANTsR package.
Visualizing High-Dimensional Data: Advances in the Past Decade.
TL;DR: A comprehensive survey of advances in high-dimensional data visualization that focuses on the past decade is provided in this article, with guidance for data practitioners to navigate through a modular view of the recent advances, inspiring the creation of new visualizations along the enriched visualization pipeline, and identifying future opportunities for visualization research.
Journal ArticleDOI
Toward a Quantitative Survey of Dimension Reduction Techniques
TL;DR: This work characterize the input data space, projection techniques, and the quality of projections, by several quantitative metrics, and samples these three spaces according to these metrics, aiming at good coverage with bounded effort.
Journal ArticleDOI
SmartAdP: Visual Analytics of Large-scale Taxi Trajectories for Selecting Billboard Locations
TL;DR: This study attempts to employ visual analytics that combines the state-of-the-art mining and visualization techniques to tackle the problem of formulating solutions immediately and comparing them rapidly for billboard placements using large-scale GPS trajectory data.
References
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Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns
Gennady Andrienko,Natalia Andrienko,Sebastian Bremm,Tobias Schreck,T. von Landesberger,Peter Bak,Daniel A. Keim +6 more
TL;DR: The use of a framework based on the “Self‐Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives for discovery of previously unknown patterns in 41‐years time series of 7 crime rate attributes in the states of the USA is described.
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Result-Driven Exploration of Simulation Parameter Spaces for Visual Effects Design
Stefan Bruckner,Torsten Möller +1 more
TL;DR: This paper utilizes sampling and spatio-temporal clustering techniques to generate a concise overview of the achievable variations and their temporal evolution in a three-dimensional scene description and allows the user to explore the simulation space in a goal-oriented manner.