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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 Partitioning Strategies for Memory-Efficient Ray Tracing of Particles 10

Spatial Partitioning Strategies for Memory-Efficient Ray Tra...

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

作者： Gralka, Patrick Wald, Ingo Geringer, Sergej Reina, Guido Ertl, Thomas Univ Stuttgart Visualizat Res Ctr Stuttgart Germany NVIDIA Corp Santa Clara CA USA Univ Stuttgart Inst Visualizat & Interact Syst Stuttgart Germany

ISBN: (纸本)9781728184685

3D particle data is relevant for a wide range of scientific domains, from molecular dynamics to astrophysics. Simulations in these domains can produce datasets containing millions or billions of particles and rendering needs to be in high quality and interactive to support the scientists in exploring and understanding the structure of their data. One general baseline approach is to represent particles as spheres and employ ray tracing as a rendering technique. However, ray tracing requires the data to be organized in acceleration data structures like bounding volume hierarchies (BVH) to achieve interactive frame rates. Modern GPUs provide hardware acceleration for traversing such data structures but are more limited in memory than CPUs. In this paper, we evaluate different acceleration data structures for sphere-based datasets, including particle kD trees, with respect to their scalability regarding both memory size and speed, and we analyze how these data structures can benefit from hardware acceleration. We show that a bricking of data results in the most effective BVH, both fast to traverse utilizing hardware acceleration and with a reasonably small memory footprint. Additionally, we present a hybrid acceleration data structure that has negligible memory overhead and still ensures reasonable traversal speed. Based on our results, visualization tools and APIs for the ray tracing can provide overall better performance by adapting to the needs of particle-centric application scenarios.

关键词： Particle data acceleration data structure ray tracing

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visualization and pattern identification in large scale time series data

Visualization and pattern identification in large scale time...

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1st ieee symposium on large-Scale data analysis and visualization 2011, LDAV 2011

作者： Holtz, Steve Valle, Guillermo Howard, Jessica Morreale, Patricia Computer Science Kean University United States NJCSTM Kean University United States

ISBN: (纸本)9781467301541

visualization of massively large datasets presents two significant problems. First, the dataset must be prepared for visualization, and traditional dataset manipulation methods fail due to lack of temporary storage or memory. The second problem is the presentation of the data in the visual media, particularly real-time visualization of streaming time series data. An ongoing research project addresses both these problems, using data from two national repositories. This work is presented here, with the results of the current effort summarized and future plans, including 3D visualization, outlined. © 2011 ieee.

关键词： visualization

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Task-based Augmented Merge Trees with Fibonacci Heaps 7

Task-based Augmented Merge Trees with Fibonacci Heaps

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

作者： Gueunet, Charles Fortin, Pierre Jomier, Julien Tierny, Julien UPMC Univ Paris 06 Sorbonne Univ CNRS Kitware SASLIP6UMR 7606 Paris France UPMC Univ Paris 06 Sorbonne Univ CNRS LIP6UMR 7606 Paris France Kitware SAS Villeurbanne France

ISBN: (纸本)9781538606179

This paper presents a new algorithm for the fast, shared memory multi-core computation of augmented merge trees on triangulations. In contrast to most existing parallel algorithms, our technique computes augmented trees. This augmentation is required to enable the full extent of merge tree based applications, including data segmentation. Our approach completely revisits the traditional, sequential merge tree algorithm to re-formulate the computation as a set of independent local tasks based on Fibonacci heaps. This results in superior time performance in practice, in sequential as well as in parallel thanks to the OpenMP task runtime. In the context of augmented contour tree computation, we show that a direct usage of our merge tree procedure also results in superior time performance overall, both in sequential and parallel. We report performance numbers that compare our approach to reference sequential and multi-threaded implementations for the computation of augmented merge and contour trees. These experiments demonstrate the runtime efficiency of our approach as well as its scalability on common workstations. We demonstrate the utility of our approach in data segmentation applications. We also provide a lightweight VTK-based C++ implementation of our approach for reproduction purposes.

关键词： Algorithm design and analysis data analysis data visualization Feature extraction Electronic mail Runtime

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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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Distribution-based Particle data Reduction for In-situ analysis and visualization of large-scale N-body Cosmological Simulations 13

Distribution-based Particle Data Reduction for In-situ Analy...

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ieee Pacific visualization symposium (PacificVis)

作者： Li, Guan Xu, Jiayi Zhang, Tianchi Shan, Guihua Shen, Han-Wei Wang, Ko-Chih Liao, Shihong Lu, Zhonghua Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Ohio State Univ Columbus OH 43210 USA Chinese Acad Sci Natl Astron Observ Beijing Peoples R China Natl Taiwan Normal Univ Taipei Taiwan

ISBN: (数字)9781728156972

ISBN: (纸本)9781728156972

Cosmological N-body simulation is an important tool for scientists to study the evolution of the universe. With the increase of computing power, billions of particles of high space-time fidelity can be simulated by supercomputers. However, limited computer storage can only hold a small subset of the simulation output for analysis, which makes the understanding of the underlying cosmological phenomena difficult. To alleviate the problem, we design an in-situ data reduction method for large-scale unstructured particle data. During the data generation phase, we use a combined k-dimensional partitioning and Gaussian mixture model approach to reduce the data by utilizing probability distributions. We offer a model evaluation criterion to examine the quality of the probabilistic distribution models, which allows us to identify and improve low-quality models. After the in-situ processing, the particle data size is greatly reduced, which satisfies the requirements from the domain experts. By comparing the astronomical attributes and visualizations of the reconstructed data with the raw data, we demonstrate the effectiveness of our in-situ particle data reduction technique.

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

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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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Filtering edge for exploration of large graphs

Filtering edge for exploration of large graphs

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2013 3rd ieee symposium on large-Scale data analysis and visualization, LDAV 2013

作者： Huang, Xiaodi School of Computing and Mathematics Charles Sturt University Albury NSW 2640 Australia

ISBN: (纸本)9781479916597

Visual clutter in the layout of a large graph is mainly caused by the overwhelming number of edges. Filtering is one of ways to reduce the clutter. We regard a filtered graph as the compressed one of an original graph. Based on this view, a filtering approach is presented to reduce the visual clutter of a layout in a way that hidden patterns can be revealed gradually. The experiments have demonstrated the performance of the proposed approach in our prototype system. As evidenced by real examples, the system allows users to explore a graph at adjustable, continuous levels of details in an interactive way. This new approach is able to reveal more hidden patterns in graphs than existing approaches, providing a new way to gain insights into graph data. © 2013 ieee.

关键词： Clutter (information theory)

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Towards visualization Scalability through Time Intervals and Hierarchical Organization of Monitoring data 09

Towards Visualization Scalability through Time Intervals and...

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9th ieee/ACM International symposium on Cluster Computing and the Grid

作者： Schnorr, Lucas Mello Huard, Guillaume Alexandre Navaux, Philippe Olivier Univ Fed Rio Grande do Sul Inst Informat BR-91501970 Porto Alegre RS Brazil Univ Grenoble 1 LIG Lab CNRS INRIA Moais res Team F-38000 Grenoble France

ISBN: (纸本)9781424439355

Highly distributed systems such as Grids are used today to the execution of large-scale parallel applications. The behavior analysis of these applications is not trivial. The complexity appears because of the event correlation among processes, external influences like time-sharing mechanisms and saturation of network links, and also the amount of data that registers the application behavior Almost all visualization tools to analysis of parallel applications offer a space-time representation of the application behavior This paper presents a novel technique that combines traces front grid applications with a treemap visualization of the data. With this combination, we dynamically create an annotated hierarchical structure that represents the application behavior for the selected time interval. The experiments in the grid show that we can readily use our technique to the analysis of large-scale parallel applications with thousands of processes.

关键词： data visualization Scalability Monitoring large-scale systems Yarn Time sharing computer systems Application software Concurrent computing Distributed computing Grid computing

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High-Quality and Low-Memory-Footprint Progressive Decoding of large-Scale Particle data 11

High-Quality and Low-Memory-Footprint Progressive Decoding o...

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ieee 11th symposium on large data analysis and visualization (LDAV)

作者： Hoang, Duong Bhatia, Harsh Lindstrom, Peter Pascucci, Valerio Univ Utah SCI Inst Salt Lake City UT 84112 USA Lawrence Livermore Natl Lab CASC Livermore CA 94550 USA

ISBN: (纸本)9781665432832

Particle representations are used often in large-scale simulations and observations, frequently creating datasets containing several millions of particles or more. Due to their sheer size, such datasets are difficult to store, transfer, and analyze efficiently. data compression is a promising solution;however, effective approaches to compress particle data are lacking and no community-standard and accepted techniques exist. Current techniques are designed either to compress small data very well but require high computational resources when applied to large data, or to work with large data but without a focus on compression, resulting in low reconstruction quality per bit stored. In this paper, we present innovations targeting tree-based particle compression approaches that improve the tradeoff between high quality and low memory-footprint for compression and decompression of large particle datasets. Inspired by the lazy wavelet transform, we introduce a new way of partitioning space, which allows a low-cost depth-first traversal of a particle hierarchy to cover the space broadly. We also devise novel data-adaptive traversal orders that significantly reduce reconstruction error compared to traditional data-agnostic orders such as breadth-first and depth-first traversals. The new partitioning and traversal schemes are used to build novel particle hierarchies that can be traversed with asymptotically constant memory footprint while incurring low reconstruction error. Our solution to encoding and (lossy) decoding of large particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error heuristics can be supplied by the user. Finally, through extensive experimentation, we demonstrate the efficacy and the flexibility of the proposed techniques when combined as well as when used independently with existing approaches on a wide range of scientific particle datasets.

关键词： particle datasets data compression coarse approximation progressive decompression tree traversal multiresolution

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