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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Journal ArticleDOI
Investigating and reflecting on the integration of automatic data analysis and visualization in knowledge discovery
Enrico Bertini,Denis Lalanne +1 more
TL;DR: The article categorizes the observed techniques in classes and proposes, on the basis of the extracted patterns, a series of potential extensions not found in literature, to analyze the discovery process by comparing the analysis steps from the perspective of information visualization and data mining.
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Multiscale Time Activity Data Exploration via Temporal Clustering Visualization Spreadsheet
Jonathan Woodring,Han-Wei Shen +1 more
TL;DR: A method to explore data at different temporal resolutions to discover and highlight data based upon time-varying trends, using the wavelet transform along the time axis to transform data points into multi-scale time series curve sets.
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Visualization for the Physical Sciences
Dan R. Lipsa,Robert S. Laramee,Simon Cox,Jonathan C. Roberts,Rick Walker,Michelle A. Borkin,Hanspeter Pfister +6 more
TL;DR: This work presents the first survey of its kind that provides a comprehensive view of existing work on visualization for the physical sciences, and introduces novel classification schemes based on application area, data dimensionality and main challenge addressed.
Journal ArticleDOI
Interactive Visual Analysis of Families of Function Graphs
TL;DR: An approach to visual analysis of an especially challenging set of problems that exhibit a complex internal data structure and supports iterative visual analysis by providing means to create complex, composite brushes that span multiple views and that are constructed using different combination schemes.
Journal ArticleDOI
Brushing of Attribute Clouds for the Visualization of Multivariate Data
TL;DR: This work proposes a transformation of the high-dimensional data in attribute space to 2D that results in a point cloud, called attribute cloud, such that points with similar multivariate attributes are located close to each other, based on ideas from multivariate density estimation and manifold learning.