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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Journal ArticleDOI
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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Proceedings ArticleDOI
Simultaneous classification of time-varying volume data based on the time histogram
TL;DR: This work proposes an approach which allows simultaneous classification of the entire time series and explores options for transfer function specification that are based, either directly or indirectly, on the time histogram.
Journal ArticleDOI
Visual Human+Machine Learning
TL;DR: A novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user and becomes computationally feasible due to a GPU implementation of the time-critical parts in the algorithm.
Journal ArticleDOI
Visual Exploration of Climate Variability Changes Using Wavelet Analysis
TL;DR: This paper will explore different techniques to visually assist the user in the analysis of variability and variability changes to allow for a holistic analysis of a global climate model data set consisting of several variables and extending over 250 years.
Journal ArticleDOI
Comparative Visualization for Parameter Studies of Dataset Series
TL;DR: A novel multi-image view and an edge explorer for comparing and visualizing gray values and edges of several datasets simultaneously and can be effective in various application areas like parameter studies of imaging modalities and dataset artifact detection are presented.
Journal ArticleDOI
Interactive Visual Analysis of Complex Scientific Data as Families of Data Surfaces
TL;DR: This work presents an advanced visual analysis approach that enables a thorough investigation of families of data surfaces with respect to pairs of independent dimensions and demonstrates the necessity for a flexible visual analysis system that integrates many different (linked) views for making sense of this highly complex data.