Scalable visualization of discrete velocity decompositions using spatially organized histograms

Neuroth, Tyson; Sauer, Franz; Wang, Weixing; Ethier, S.; Ma, Kwan‐Liu

doi:10.1109/ldav.2015.7348073

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Lu et al introduced compact histograms representations based on bit-strings and space-filling curves [35]. Histograms have been used to summarize space phase distributions of statistical super-particles in particle-in-cell simulation data [28,39]. For simulation monitoring and anomaly detection in transonic jet engine simulations, distribution functions were used to quickly and compactly represent flow conditions, so that they could be monitored in real-time for human in the loop stall detection [19].…”

Section: Related Workmentioning

confidence: 99%

“…The aforementioned work [19,39,54], each included interactive visualization systems for local spatial statistical analysis with distribution functions computed over local regions, but were each designed with different goals or for application domains. Neuroth et al introduced a design which relied on a 2D decomposition for Tokamak data [39]; where symmetry justifies radial integration to project the data onto the 2D plane. They used a static Voronoi tessellation.…”

Section: Related Workmentioning

confidence: 99%

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Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis

Neuroth¹,

Rieth²,

Aditya³

et al. 2022

Preprint

Self Cite

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Fig. 1. Left to right illustrates steps of our methodology. We decompose volume data by novel geodesically based discrete centroidal Voronoi tessellations restricted by sequences of level sets. Then we aggregate statics within the resulting hierarchically structured segments. Finally, the data is reorganized according the hierarchical structure, and used for interactive spatial statistical analysis of large data at a reasonable cost.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis

Neuroth¹,

Rieth²,

Aditya³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…They demonstrated that topological analysis and uncertainty visualization can be performed with hixels. Neuroth et al [44] generated spatially organized velocity histograms both on-the-fly and in situ for interactive visualization and exploration of the underlying data. Our approach uses in situ regional multidimensional PDFs to provide post hoc analysis at a high granular level.…”

Section: Related Workmentioning

confidence: 99%

A User-Centered Design Study in Scientific Visualization Targeting Domain Experts

Sauer

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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The development and design of visualization solutions that are truly usable is essential for ensuring both their adoption and effectiveness. User-centered design principles, which focus on involving users throughout the entire development process, are well suited for visualization and have been shown to be effective in numerous information visualization endeavors. In this paper, we report a two year long collaboration with combustion scientists that, by applying these design principles, generated multiple results including an in situ visualization technique and a post hoc probability distribution function (PDF) exploration tool. Furthermore, we examine the importance of user-centered design principles and describe lessons learned over the design process in an effort to aid others who also seek to work with scientists for developing effective and usable scientific visualization solutions.

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“…Nevertheless, the "Overview-last" part of our argument is also implicitly supported by a several examples of scientific visualization. While sometimes nominally providing an overview according to the Shneiderman mantra, these examples relegate their overview to the bottom or side of the visual analysis interface, and allocate it significantly reduced display space, compared to the "detailed view" [7,19,43,49,52,67].…”

Section: Supporting Evidence From the Literaturementioning

confidence: 99%

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Luciani

Burks

Sugiyama³

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Visualization research often seeks designs that first establish an overview of the data, in accordance to the information seeking mantra: "Overview first, zoom and filter, then details on demand". However, in computational fluid dynamics (CFD), as well as in other domains, there are many situations where such a spatial overview is not relevant or practical for users, for example when the experts already have a good mental overview of the data, or when an analysis of a large overall structure may not be related to the specific, information-driven tasks of users. Using scientific workflow theory and, as a vehicle, the problem of viscous finger evolution, we advocate an alternative model that allows domain experts to explore features of interest first, then explore the context around those features, and finally move to a potentially unfamiliar summarization overview. In a model instantiation, we show how a computational back-end can identify and track over time low-level, small features, then be used to filter the context of those features while controlling the complexity of the visualization, and finally to summarize and compare simulations. We demonstrate the effectiveness of this approach with an online web-based exploration of a total volume of data approaching half a billion seven-dimensional data points, and report supportive feedback provided by domain experts with respect to both the instantiation and the theoretical model.

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Scalable visualization of discrete velocity decompositions using spatially organized histograms

Cited by 8 publications

References 16 publications

Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis

Level Set Restricted Voronoi Tessellation for Large scale Spatial Statistical Analysis

A User-Centered Design Study in Scientific Visualization Targeting Domain Experts

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

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