An Integrated Exploration Approach to Visualizing Multivariate Particle Data

Jones, Chad; Ma, Kwan‐Liu; Ethier, S.; Lee, Wei‐Li

doi:10.1109/mcse.2008.88

Cited by 11 publications

(12 citation statements)

References 7 publications

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“…In those designs, TFs are defined on the dimension axes by assigning numerical ranges [1], [4]. PCPs have also been used for feature selection in particle simulations [17], which is a similar process to TF design. Zhao and Kaufman [46] used PCPs for TF design, and showed the transferred voxel points on the dimension reduction plot.…”

Section: Related Workmentioning

confidence: 99%

Scalable Multivariate Volume Visualization and Analysis Based on Dimension Projection and Parallel Coordinates

Guo

Yuan

2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-In this paper, we present an effective and scalable system for multivariate volume data visualization and analysis with a novel transfer function interface design that tightly couples parallel coordinates plots (PCP) and MDS-based dimension projection plots. In our system, the PCP visualizes the data distribution of each variate (dimension) and the MDS plots project features. They are integrated seamlessly to provide flexible feature classification without context switching between different data presentations during the user interaction. The proposed interface enables users to identify relevant correlation clusters and assign optical properties with lassos, magic wand and other tools. Furthermore, direct sketching on the volume rendered images has been implemented to probe and edit features. With our system, users can interactively analyze multivariate volumetric data sets by navigating and exploring feature spaces in unified PCP and MDS plots. To further support large scale multivariate volume data visualization and analysis, Scalable Pivot MDS (SPMDS), parallel adaptive continuous PCP rendering, as well as parallel rendering techniques are developed and integrated into our visualization system. Our experiments show that the system is effective in multivariate volume data visualization and its performance is highly scalable for data sets with different sizes and number of variates.

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Section: Related Workmentioning

confidence: 99%

Scalable Multivariate Volume Visualization and Analysis Based on Dimension Projection and Parallel Coordinates

Guo

Yuan

2012

IEEE Trans. Visual. Comput. Graphics

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“…The simulation begins as 256 3 particles arranged evenly on a cubical grid. The particles are then moved by small amounts to establish the correct cosmological initial conditions which are constrained by observations of the cosmic microwave background and the distribution of galaxies on large scales.…”

Section: Cosmological Simulation Comparisonmentioning

confidence: 99%

“…The colored parallel coordinates allow the user to find correlations and patterns between dimensions that might be difficult to see using only a colored dimension in a spatial view. Using parallel coordinates as a selection tool and interface for a spatial view has been shown to be particularly effective for particle visualizations [3]. To make the parallel coordinates accurately render over sixteen million points, we used a 32-bit-per-channel frame buffer that allows for very low alpha values.…”

Section: Adding the Uncertainty Dimensionmentioning

confidence: 99%

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Multiple Uncertainties in Time-Variant Cosmological Particle Data

Haroz

Heitmann

2008

2008 IEEE Pacific Visualization Symposium

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Though the mediums for visualization are limited, the potential dimensions of a dataset are not. In many areas of scientific study, understanding the correlations between those dimensions and their uncertainties is pivotal to mining useful information from a dataset. Obtaining this insight can necessitate visualizing the many relationships among temporal, spatial, and other dimensionalities of data and its uncertainties. We utilize multiple views for interactive dataset exploration and selection of important features, and we apply those techniques to the unique challenges of cosmological particle datasets. We show how interactivity and incorporation of multiple visualization techniques help overcome the problem of limited visualization dimensions and allow many types of uncertainty to be seen in correlation with other variables.Animations as well as additional information are available at

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“…The approach described in Section 2 is a multivariate exploration interface that provides scientists a way of exploring and visualizing both physical and variable spaces from a global view to a refined selection of particles and field data over time [6].…”

Section: Introductionmentioning

confidence: 99%

Visual interrogation of gyrokinetic particle simulations

Jones¹,

Liu²,

Sanderson³

et al. 2007

J. Phys.: Conf. Ser.

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Abstract. Gyrokinetic particle simulations are critical to the study of anomalous energy transport associated with plasma microturbulence in magnetic confinement fusion experiments. The simulations are conducted on massively parallel computers and produce large quantities of particles, variables, and time steps, thus presenting a formidable challenge to data analysis tasks. We present two new visualization techniques for scientists to improve their understanding of the time-varying, multivariate particle data. One technique allows scientists to examine correlations in multivariate particle data with tightly coupled views of the data in both physical space and variable space, and to visually identify and track features of interest. The second technique, built into SCIRun, allows scientists to perform range-based queries over a series of time slices and visualize the resulting particles using glyphs. The ability to navigate the multiple dimensions of the particle data, as well as query individual or a collection of particles, enables scientists to not only validate their simulations but also discover new phenomena in their data.

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An Integrated Exploration Approach to Visualizing Multivariate Particle Data

Cited by 11 publications

References 7 publications

Scalable Multivariate Volume Visualization and Analysis Based on Dimension Projection and Parallel Coordinates

Scalable Multivariate Volume Visualization and Analysis Based on Dimension Projection and Parallel Coordinates

Multiple Uncertainties in Time-Variant Cosmological Particle Data

Visual interrogation of gyrokinetic particle simulations

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