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12th ieee symposium on large-Scale data analysis and visualization (LDAV)

作者： Nauleau, Florent Vivodtzev, Fabien Bridel-Bertomeu, Thibault Beaugendre, Heloise Tierny, Julien CEA Paris France Univ Bordeaux Bordeaux France CNRS Paris France

ISBN: (数字)9781665491563

ISBN: (纸本)9781665491563

This application paper presents a comprehensive experimental evaluation of the suitability of Topological data analysis (TDA) for the quantitative comparison of turbulent flows. Specifically, our study documents the usage of the persistence diagram of the maxima of flow enstrophy (an established vorticity indicator), for the topological representation of 180 ensemble members, generated by a coarse sampling of the parameter space of five numerical solvers. We document five main hypotheses reported by domain experts, describing their expectations regarding the variability of the flows generated by the distinct solver configurations. We contribute three evaluation protocols to assess the validation of the above hypotheses by two comparison measures: (i) a standard distance used in scientific imaging (the L-2 norm) and (ii) an established topological distance between persistence diagrams (the L-2-Wasserstein metric). Extensive experiments on the input ensemble demonstrate the superiority of the topological distance (ii) to report as close to each other flows which are expected to be similar by domain experts, due to the configuration of their vortices. Overall, the insights reported by our study bring an experimental evidence of the suitability of TDA for representing and comparing turbulent flows, thereby providing to the fluid dynamics community confidence for its usage in future work. Also, our flow data and evaluation protocols provide to the TDA community an application-approved benchmark for the evaluation and design of further topological distances.

关键词： Protocols data analysis Three-dimensional displays Computational modeling Clustering methods Hydrodynamics Numerical models

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Personal Augmented Reality for Information visualization on large Interactive Displays

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第2期27卷 1182-1192页

作者： Reipschlager, Patrick Flemisch, Tamara Dachselt, Raimund Tech Univ Dresden Interact Media Lab Dresden Germany Tech Univ Dresden Ctr Tactile Internet CeTi Dresden Germany Tech Univ Dresden Cluster Excellence Phys Lile Dresden Germany

In this work we propose the combination of large interactive displays with personal head-mounted Augmented Reality (AR) for information visualization to facilitate data exploration and analysis. Even though large displays provide more display space, they are challenging with regard to perception, effective multi-user support, and managing data density and complexity. To address these issues and illustrate our proposed setup, we contribute an extensive design space comprising first, the spatial alignment of display, visualizations, and objects in AR space. Next, we discuss which parts of a visualization can be augmented. Finally, we analyze how AR can be used to display personal views in order to show additional information and to minimize the mutual disturbance of data analysts. Based on this conceptual foundation, we present a number of exemplary techniques for extending visualizations with AR and discuss their relation to our design space. We further describe how these techniques address typical visualization problems that we have identified during our literature research. To examine our concepts, we introduce a generic AR visualization framework as well as a prototype implementing several example techniques. In order to demonstrate their potential, we further present a use case walkthrough in which we analyze a movie data set. From these experiences, we conclude that the contributed techniques can be useful in exploring and understanding multivariate data. We are convinced that the extension of large displays with AR for information visualization has a great potential for data analysis and sense-making.

关键词： data visualization data analysis Augmented reality Three-dimensional displays Navigation visualization Augmented Reality Information visualization InfoVis large Displays Immersive Analytics Physical Navigation Multiple Coordinated Views

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Analytical visualization of large-scale data of 3D Cultural Objects

Analytical Visualization of Large-scale data of 3D Cultural ...

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ieee Pacific visualization symposium (PacificVis)

作者： Tanaka, Satoshi Ritsumeikan Univ Kyoto Japan

ISBN: (纸本)9781479928736

For appreciating 3D cultural objects, e. g., artistic statues, traditional vehicles, and historical buildings or cities, visualization has mainly been used in the following styles: (1) realistic computer graphics, (2) artistic computer graphics, and (3) interactive virtual reality. However, for academic studying of 3D cultural objects, these styles are insufficient. Namely, we need more analytical styles that can extract invisible or veiled features of visualized targets. What makes such analytical visualization more important is recent rapid development of range-sensing technology to digitally archive real 3D objects. For example, modern laser scanning produces over 107 or 10(8) 3D point data for one measurement. To extract useful information from such large-scale and complex data, we need (A) precise 3D transparent visualization, (B) superposition of visual assistants, and (C) time-series visualization.

关键词： Computer Graphics [I.3.m]: Miscellaneous Image Processing and Computer Vision [I.4.8]: Scene analysis Range data

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Cinema Darkroom: A Deferred Rendering Framework for large-Scale datasets 10

Cinema Darkroom: A Deferred Rendering Framework for Large-Sc...

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10th ieee symposium on large data analysis and visualization (LDAV)

作者： Lukasczyk, Jonas Garth, Christoph Larsen, Matthew Engelke, Wito Hotz, Ingrid Rogers, David Ahrens, James Maciejewski, Ross Arizona State Univ Tempe AZ 85287 USA TU Kaiserslautern Kaiserslautern Germany Lawrence Livermore Natl Lab Livermore CA 94550 USA Linkoping Univ Linkoping Sweden Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781728184685

This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once-which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters-such as the applied color map or the strength of ambient occlusion-where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CD's ability to effectively decouple the complexity and size of the dataset from its visualization.

关键词： Deferred Rendering Image databases In Situ visualization Post Hoc analysis Image-Based Shading

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Big data Provenance analysis and visualization 15

Big Data Provenance Analysis and Visualization

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2015 15th ieee ACM International symposium on Cluster Cloud and Grid Computing (CCGrid 2015)

作者： Chen, Peng Plale, Beth Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA

ISBN: (纸本)9781479980062

Provenance captured from E-Science experimentation is often large and complex, for instance, from agent-based simulations that have tens of thousands of heterogeneous components interacting over extended time periods. The subject of study of my dissertation is the use of E-Science provenance at scale. My initial research studied the visualization of large provenance graphs and proposed an abstract representation of provenance that supports useful data mining. Recent work involves analyzing large provenance data generated from agent-based simulations on a single machine. In continuation, I propose stream processing techniques to support the continuous and real-time analysis of data provenance, which is captured from agent based simulations on HPC and thus has unprecedented volume and complexity.

关键词： data provenance big data visualization mining stream processing

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visualization and analysis for Near-Real-Time Decision Making in Distributed Workflows 30

Visualization and Analysis for Near-Real-Time Decision Makin...

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30th ieee International Parallel and Distributed Processing symposium (IPDPS)

作者： Pugmire, David Kress, James Choi, Jong Klasky, Scott Kurc, Tahsin Churchill, Randy Michael Wolf, Matthew Eisenhauer, Greg Childs, Hank Wu, Kesheng Sim, Alexander Gu, Junmin Low, Jonathan Oak Ridge Natl Lab Oak Ridge TN 37830 USA Univ Oregon Eugene OR 97403 USA SUNY Stony Brook Stony Brook NY USA Princeton Plasma Phys Lab POB 451 Princeton NJ 08543 USA Georgia Inst Technol Atlanta GA 30332 USA Lawrence Berkeley Natl Lab Berkeley CA 94720 USA ASTAR Computat Resource Ctr Singapore Singapore

ISBN: (纸本)9781509036820

data driven science is becoming increasingly more common, complex, and is placing tremendous stresses on visualization and analysis frameworks. data sources producing 10GB per second (and more) are becoming increasingly commonplace in both simulation, sensor and experimental sciences. These data sources, which are often distributed around the world, must be analyzed by teams of scientists that are also distributed. Enabling scientists to view, query and interact with such large volumes of data in near-real-time requires a rich fusion of visualization and analysis techniques, middleware and workflow systems. This paper discusses initial research into visualization and analysis of distributed data workflows that enables scientists to make near-real-time decisions of large volumes of time varying data.

关键词： data visualization

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Scalable Web-Embedded Volume Rendering 7

Scalable Web-Embedded Volume Rendering

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ieee 7th symposium on large data analysis and visualization (LDAV)

作者： Raji, Mohammad Hota, Alok Huang, Jian Univ Tennessee Knoxville TN 37996 USA

ISBN: (纸本)9781538606179

In this paper, we develop a method to encapsulate and embed interactive 3D volume rendering into the standard web Document Object Model (DOM). The package we implemented for this work is called Tapestry. Using Tapestry, data-intensive and interactive volume rendering can be easily incorporated into web pages. For example, we can enhance a Wikipedia page on supernova to contain several interactive 3D volume renderings of supernova volume data. There is no noticeable slowdown during the page load by the web browser. A user can choose to interact with any of the volume renderings of supernova at will. We refer to each embedded 3D visualization as a hyperimage. Hyperimages depend on scalable server-side support where volume rendering jobs are performed and managed elastically. We show the minimal code change required to embed hyperimages into previously static web pages. We also demonstrate the supporting Tapestry server's scalability along several dimensions: web page complexity, rendering complexity, frequency of rendering requests, and number of concurrent sessions. Using solely standard open-source components, this work proves that it is now feasible to make volume rendering a scalable web service that supports a diverse audience with varying use cases.

关键词： visualization system web applications volume rendering

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Visual Interactive Map Matching

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2018年第6期24卷 1881-1892页

作者： Krueger, Robert Simeonov, Georgi Beck, Fabian Ertl, Thomas Univ Stuttgart Inst Visualizat & Interact Syst D-70174 Stuttgart Germany Univ Duisburg Essen Paluno D-47057 Duisburg Germany

Map matching is the process of assigning observed geographic positions of vehicles and their trajectories to the actual road links in a road network. In this paper, we present Visual Interactive Map Matching, a visual analytics approach to fine-tune the data preprocessing and matching process. It is based on ST-matching, a state-of-the-art and easy-to-understand map matching algorithm. Parameters of the preprocessing step and algorithm can be optimized with immediate visual feedback. visualizations show current matching issues and performance metrics on a map and in diagrams. Manual and computer-supported editing of the road network model leads to a refined alignment of trajectories and roads. We demonstrate our approach with large-scale taxi trajectory data. We show that optimizing the matching on a subsample results in considerably improved matching quality, also when later scaled to the full dataset. An optimized matching ensures data faithfulness and prevents misinterpretation when the matched data might be investigated in follow-up analysis.

关键词： Map matching data cleaning data transformation and representation geographic visualization

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Spatial analysis of news sources

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2006年第5期12卷 765-771页

作者： Mehler, Andrew Bao, Yunfan Li, Xin Wang, Yue Skiena, Steven SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

People in different places talk about different things. This interest distribution is reflected by the newspaper articles circulated in a particular area. We use data from our large-scale newspaper analysis system (Lydia) to make entity datamaps, a spatial visualization of the interest in a given named entity. Our goal is to identify entities which display regional biases. We develop a model of estimating the frequency of reference of an entity in any given city from the reference frequency centered in surrounding cities, and techniques for evaluating the spatial significance of this distribution.

关键词： GIS geographic visualization text and document visualization information analytics WWW data visualization spidering newspapers

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A Compact Multivariate Histogram Representation for Query-driven visualization 5

A Compact Multivariate Histogram Representation for Query-dr...

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ieee 5th symposium on large data analysis and visualization (LDAV)

作者： Lu, Kewei Shen, Han-Wei Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781467385176

As the size of data continues to increase, distribution-based methods become increasingly more important for data summarization and queries. To represent the distribution from a dataset without relying on a particular parametric model, histograms are widely used in many applications as it is simple to create and efficient to query. For multivariate scientific datasets, however, storing multivariate histograms in the form of multi-dimensional arrays is very expensive as the size of the histogram grows exponentially with the number of variables. In this paper, we present a compact structure to store multivariate histograms to reduce its huge space cost while supporting different kinds of histogram queries efficiently. A data space transformation is employed first to transform the large multi-dimensional array to a much smaller array. Dictionaries are constructed to encode this transformation. Then, the multivariate histogram is represented as a sequence of index and frequency pairs where the indices are represented as bitstrings computed from a space filling curve traversal of the transformed array. With this compact representation, the storage cost for the histograms is reduced. Based on our representation, we also present several common types of queries such as histogram marginalization, bin-merging and computation of conditional probability. We parallelize both the histogram computation and queries to improve its efficiency. We present several query-driven visualization applications to explore and analyze multivariate scientific datasets. Experimental results to study the performance of our framework in terms of scalability and space cost are also discussed.

关键词： histograms Multivariate visualization dataset compact structures Conditional probability

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