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Ray-Based Exploration of large Time-Varying Volume data Using Per-Ray Proxy Distributions

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2020年第11期26卷 3299-3313页

作者： Wang, Ko-Chih Wei, Tzu-Hsuan Shareef, Naeem Shen, Han-Wei Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

The analysis and visualization of data created from simulations on modern supercomputers is a daunting challenge because the incredible compute power of modern supercomputers allow scientists to generate datasets with very high spatial and temporal resolutions. The limited bandwidth and capacity of networking and storage devices connecting supercomputers to analysis machines become the major bottleneck for data analysis such that simply moving the whole dataset from the supercomputer to a data analysis machine is infeasible. A common approach to visualize high temporal resolution simulation datasets under constrained I/O is to reduce the sampling rate in the temporal domain while preserving the original spatial resolution at the time steps. data interpolation between the sampled time steps alone may not be a viable option since it may suffer from large errors, especially when using a lower sampling rate. We present a novel ray-based representation storing ray based histograms and depth information that recovers the evolution of volume data between sampled time steps. Our view-dependent proxy allows for a good trade off between compactly representing the time-varying data and leveraging temporal coherence within the data by utilizing interpolation between time steps, ray histograms, depth information, and codebooks. Our approach is able to provide fast rendering in the context of transfer function exploration to support visualization of feature evolution in time-varying data.

关键词： Histograms Interpolation Rendering (computer graphics) Transfer functions data visualization data models Supercomputers data visualization time-varying data large data modeling scientific simulation

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Redirecting research in large-format displays for visualization

Redirecting research in large-format displays for visualizat...

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2nd symposium on large-Scale data analysis and visualization, LDAV 2012

作者： Moreland, Kenneth

ISBN: (纸本)9781467347334

large-format displays, in particular tiled displays, remain actively used and researched today after almost two decades since their conception. Many government, academic, and commercial entities have invested considerably in the construction and use of large-format displays. During this time we have developed new applications but have also discovered faulty assumptions. This position paper evaluates the most important lessons learned from early and recent research in large-format displays. Each lesson suggests a direction for future research, which generally takes the form of a more user- and application-centric focus. © 2012 ieee.

关键词： large format DISPLAYING visualization redirection

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CoDDA: A Flexible Copula-based Distribution Driven analysis Framework for large-Scale Multivariate data

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2019年第1期25卷 1214-1224页

作者： Hazarika, Subhashis Dutta, Soumya Shen, Han-Wei Chen, Jen-Ping Ohio State Univ GRAVITY Res Grp Dept Comp Sci & Engn Columbus OH 43210 USA Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43210 USA

CoDDA (Copula-based Distribution Driven analysis) is a flexible framework for large-scale multivariate datasets. A common strategy to deal with large-scale scientific simulation data is to partition the simulation domain and create statistical data summaries. Instead of storing the high-resolution raw data from the simulation, storing the compact statistical data summaries results in reduced storage overhead and alleviated I/O bottleneck. Such summaries, often represented in the form of statistical probability distributions, can serve various post-hoc analysis and visualization tasks. However, for multivariate simulation data using standard multivariate distributions for creating data summaries is not feasible. They are either storage inefficient or are computationally expensive to be estimated in simulation time (in situ) for large number of variables. In this work, using copula functions, we propose a flexible multivariate distribution-based data modeling and analysis framework that offers significant data reduction and can be used in an in situ environment. The framework also facilitates in storing the associated spatial information along with the multivariate distributions in an efficient representation. Using the proposed multivariate data summaries, we perform various multivariate post-hoc analyses like query-driven visualization and sampling-based visualization. We evaluate our proposed method on multiple real-world multivariate scientific datasets. To demonstrate the efficacy of our framework in an in situ environment, we apply it on a large-scale flow simulation.

关键词： In situ processing Distribution-based Multivariate Query-driven Copula

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Visual analysis of multivariate state transition graphs

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2006年第5期12卷 685-692页

作者： Pretorius, A. Johannes van Wijk, Jarke J. Tech Univ Eindhoven Dept Math & Comp Sci NL-5600 MB Eindhoven Netherlands

We present a new approach for the visual analysis of state transition graphs. We deal with multivariate graphs where a number of attributes are associated with every node. Our method provides an interactive attribute-based clustering facility. Clustering results in metric, hierarchical and relational data, represented in a single visualization. To visualize hierarchically structured quantitative data, we introduce a novel technique: the bar tree. We combine this with a node-link diagram to visualize the hierarchy and an arc diagram to visualize relational data. Our method enables the user to gain significant insight into large state transition graphs containing tens of thousands of nodes. We illustrate the effectiveness of our approach by applying it to a real-world use case. The graph we consider models the behavior of an industrial wafer stepper and contains 55 043 nodes and 289 443 edges.

关键词： graph visualization multivariate visualization interactive clustering state spaces transition systems finite state machines

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Smart Survey Tool: A Multi Device Platform for Museum Visitor Tracking and Tracking data visualization 12

Smart Survey Tool: A Multi Device Platform for Museum Visito...

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12th ieee Pacific visualization symposium (ieee PacificVis)

作者： Craig, Paul Wang, Yiwen Kim, Joon Sik Chen, Gang Liu, Yu Li, Jiabei Gao, Zhiqiang Du, Gao Xian Jiaotong Liverpool Univ Xian Shaanxi Peoples R China Nanjing Museum Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538692264

This paper describes the Smart Survey Tool, a novel multi-device application for museum visitor tracking and tracking data visualization. The application allows museum staff to capture detailed information describing how visitors move around an exhibition and interact with individual exhibits. They can then visualize the results of tracking either on a single mobile device or with multiple mobile devices connected to a large display. The platform uses orthogonal views of the exhibition space for tracking and visualization, with a chess-piece icon to represent visitors during tracking, and curved semi-transparent lines with animated semi-circles to communicate the path and direction of visitor movement. Our visualization is novel in its use of an orthogonal projection for pedestrian tracking and animation to communicate the flow of visitors around the exhibition space, as well as allowing users to dynamically switch between views representing different groups of visitors. The design of our application was informed through an extensive requirements analysis study conducted with Nanjing Museum and evaluated by conducting expert interviews with museum managers who considered that the application allowed for more effective and efficient recording and analysis of visitor tracking data.

关键词： Human-centered computing visualization visualization application domains Information visualization

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Lifted Wasserstein Matcher for Fast and Robust Topology Tracking 8

Lifted Wasserstein Matcher for Fast and Robust Topology Trac...

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8th ieee symposium on large-Scale data analysis and visualization (LDAV)

作者： Soler, Maxime Plainchault, Melanie Conche, Bruno Tierny, Julien Total SA Courbevoie France Sorbonne Univ CNRS Lab Informat Paris 6 F-75005 Paris France

ISBN: (纸本)9781538668733

This paper presents a robust and efficient method for tracking topological features in time-varying scalar data. Structures are tracked based on the optimal matching between persistence diagrams with respect to the Wasserstein metric. This fundamentally relies on solving the assignment problem, a special case of optimal transport, for all consecutive timesteps. Our approach relies on two main contributions. First, we revisit the seminal assignment algorithm by Kuhn and Munkres which we specifically adapt to the problem of matching persistence diagrams in an efficient way. Second, we propose an extension of the Wasserstein metric that significantly improves the geometrical stability of the matching of domain-embedded persistence pairs. We show that this geometrical lifting has the additional positive side-effect of improving the assignment matrix sparsity and therefore computing time. The global framework computes persistence diagrams and finds optimal matchings in parallel for every consecutive timestep. Critical trajectories are constructed by associating successively matched persistence pairs over time. Merging and splitting events are detected with a geometrical threshold in a post-processing stage. Extensive experiments on real-life datasets show that our matching approach is up to two orders of magnitude faster than the seminal Munkres algorithm. Moreover, compared to a modern approximation method, our approach provides competitive runtimes while guaranteeing exact results. We demonstrate the utility of our global framework by extracting critical point trajectories from various time-varying datasets and compare it to the existing methods based on associated overlaps of volumes. Robustness to noise and temporal resolution downsampling is empirically demonstrated.

关键词： Topological data analysis Optimal Transport Feature Tracking

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Visuals on the House: Optimizing HPC Workflows with No-Cost CPU visualization 14

Visuals on the House: Optimizing HPC Workflows with No-Cost ...

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14th symposium on large data analysis and visualization

作者： Mateevitsi, Victor A. Sewell, Andres Bode, Mathis Fischer, Paul Goebbert, Jens Henrik Insley, Joseph A. Kavroulakis, Ioannis Konioris, Damaskinos Lan, Yu-Hsiang Min, Misun Papageorgiou, Dimitrios Papka, Michael E. Petruzza, Steve Rizzi, Silvio Tomboulides, Ananias Argonne Natl Lab Argonne IL 60439 USA Utah State Univ Logan UT 84322 USA Forschungszentrum Julich Julich Germany Aristotle Univ Thessaloniki Thessaloniki Greece Univ Illinois Chicago IL 60680 USA Northern Illinois Univ De Kalb IL 60115 USA Univ Illinois Champaign IL USA

ISBN: (纸本)9798331516932;9798331516925

The rise of heterogeneous resources in modern High Performance Computing (HPC) systems has propelled the scientific community beyond the exascale threshold. To maximize simulation performance on HPCs, applications increasingly rely on device resources, such as GPUs, leading to under-utilization of host resources, particularly CPUs. In situ analysis and visualization techniques minimize data movement by operating on data in-memory, but this still involves blocking operations that incur a small penalty on simulation performance. We explore a novel instrumentation approach where GPU-based timestep data is copied from device memory to host memory, enabling CPUs to concurrently perform visualization and analysis tasks. This strategy allows simulations to continue uninterrupted by an in situ library's analysis and visualization processes.

关键词： High Performance Computing (HPC) In Situ visualization Computational Fluid Dynamics (CFD) GPU Computing Parallel Processing

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Real-Time GPU-Accelerated Social Media Sentiment Processing and visualization 17

Real-Time GPU-Accelerated Social Media Sentiment Processing ...

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21st ieee/ACM International symposium on Distributed Simulation and Real Time Applications (DS-RT)

作者： Ch'ng, Eugene Chen, Ziyang See, Simon Univ Nottingham NVIDIA Joint Lab Mixed Real Ningbo Zhejiang Peoples R China NVIDIA Technol APJ Ctr Singapore Singapore

ISBN: (纸本)9781538640289

data visualization is an important aspect of data analytics in an age where decisions are all based on information. Approaches in data visualization, particularly those that have the capability of processing large-scale textual datasets and visualize them as structured information in real-time can be useful for monitoring trends in social media. In this article, we present our GPU accelerated project, which uses CUDA to distribute and parallelize the processing and analysis of textual data in order to visualize information in real-time, or close to real-time as a foundational system for the future of real-time applications which monitors trends in social media, applicable to political elections, social media analytics, and other needs in computational social sciences which are time-critical.

关键词： GPU acceleration sentiment analysis real-time visualization big data social media twitter

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visualization of large-scale customer satisfaction surveys using a parallel coordinate tree

Visualization of large-scale customer satisfaction surveys u...

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9th Annual ieee symposium on Information visualization (INFOVIS 2003)

作者： Brodbeck, D Girardin, L Macrofocus GmbH Germany

ISBN: (纸本)0780381548

Satisfaction surveys are an important measurement tool in fields such as market research or human resources management. Serious studies consist of numerous questions and contain answers from large population samples. Aggregation on both sides, the questions asked as well as the answers received, turns the multidimensional problem into a complex system of interleaved hierarchies. Traditional ways of presenting the results are limited to one-dimensional charts and cross-tables. We developed a visualization method called the Parallel Coordinate Tree that combines multidimensional analysis with a tree structure representation. Distortion-oriented focus+context techniques are used to facilitate interaction with the visualization. In this paper we present a design study of a commercial application that we built, using this method to analyze and communicate results from large-scale customer satisfaction surveys.

关键词： parallel coordinates focus plus context hierarchical data satisfaction survey

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Attribute-Driven Edge Bundling for General Graphs with Applications in Trail analysis 8

Attribute-Driven Edge Bundling for General Graphs with Appli...

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ieee Pacific visualization symposium (PacificVis)

作者： Peysakhovich, Vsevolod Hurter, Christophe Telea, Alexandru ISAE Toulouse France DGAC Toulouse France Univ Groningen NL-9700 AB Groningen Netherlands

ISBN: (纸本)9781467368797

Edge bundling methods reduce visual clutter of dense and occluded graphs. However, existing bundling techniques either ignore edge properties such as direction and data attributes, or are otherwise computationally not scalable, which makes them unsuitable for tasks such as exploration of large trajectory datasets. We present a new framework to generate bundled graph layouts according to any numerical edge attributes such as directions, timestamps or weights. We propose a GPU-based implementation linear in number of edges, which makes our algorithm applicable to large datasets. We demonstrate our method with applications in the analysis of aircraft trajectory datasets and eye-movement traces.

关键词： I.3.3 [Computing Methodologies]: Computer Graphics—Picture/Image Generation I.3.6 [Computing Methodologies]: Computer Graphics—Methodology and Techniques Bundles Edge of a figure or solid edges Binding dataset trail Eye Movements Exploration Dense

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