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14th symposium on large data analysis and visualization

作者： Olbrich, Stephan Beckert, Andreas Michel, Cecile Schroer, Christian Ehteram, Samaneh Schropp, Andreas Paetzold, Philipp Univ Hamburg Hamburg Germany Ctr Natl Rech Sci CNRS ArScAn Nanterre France Deutsch Elektronen Synchrotron DESY Hamburg Germany

ISBN: (纸本)9798331516932;9798331516925

Cuneiform is the earliest known system of writing, first developed for the Sumerian language of southern Mesopotamia in the second half of the 4th millennium BC. Cuneiform signs are obtained by impressing a stylus on fresh clay tablets. For certain purposes, e.g. authentication by seal imprint, some cuneiform tablets were enclosed in clay envelopes, which cannot be opened without destroying them. The aim of our interdisciplinary project is the non-invasive study of clay tablets. A portable X-ray micro-CT scanner is developed to acquire density data of such artifacts on a high-resolution, regular 3D grid at collection sites. The resulting volume data is processed through feature-preserving denoising, extraction of high-accuracy surfaces using a manifold dual marching cubes algorithm and extraction of local features by enhanced curvature rendering and ambient occlusion. For the non-invasive study of cuneiform inscriptions, the tablet is virtually separated from its envelope by curvature-based segmentation. The computational- and data-intensive algorithms are optimized for near-real-time offline usage with limited resources at collection sites. To visualize the complexity-reduced and octree-based compressed representation of surfaces, we develop and implement an interactive application. To facilitate the analysis of such clay tablets, we implement shape-based feature extraction algorithms to enhance cuneiform recognition. Our workflow supports innovative 3D display and interaction techniques such as autostereoscopic displays and gesture control.

关键词： Computed tomography Parallel data analysis Scientific visualization 3D presentation and interaction Application case study

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Parallel Assemblies Plot, a visualization tool to explore categorical and quantitative data: application to digital mobility outcomes 16

Parallel Assemblies Plot, a visualization tool to explore ca...

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ieee 16th Pacific visualization symposium (ieee PacificVis)

作者： Cantu, Alma Mico-Amigo, Maria Encarna Del Din, Silvia Fernstad, Sara Johansson Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England Newcastle Univ Translat & Clin Res Inst Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9798350321241

This paper presents a tool enabling the visual analysis of multivariate heterogeneous data. large amounts of measured and contextual data are being gathered for a large number of applications, increasing connectivity across different data types. While measured data are often quantitative, contextual data tend to be categorical. This results in datasets containing multivariate data with heterogeneous properties. Difference in the natures of these properties raises challenges when combining them for analysis. This paper presents the design of a tool that enables the exploration of multivariate heterogeneous data by combining the strengths of Parallel Coordinates and Parallel Sets. The design relied on the application domain of real-life mobility monitoring that is particularly affected by the challenge mentioned above. To validate the suggested approach this paper presents the result of a usability evaluation, which confirms that the presented design is as efficient as other exiting tools while providing more features for correlation analysis.

关键词： Heterogeneous data Multivariate data visualization Technique Scientific visualization

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2018 ieee 8th symposium on large data analysis and visualization, LDAV 2018

2018 IEEE 8th Symposium on Large Data Analysis and Visualiza...

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8th ieee symposium on large data analysis and visualization, LDAV 2018

ISBN: (纸本)9781538668733

The proceedings contain 20 papers. The topics discussed include: method for improving RadViz's navigation function based on focusing and filtering;parallel partial reduction for large-scale data analysis and visualization;in situ visualization and analysis to design large scale experiments in computational fluid dynamics;citation network visualization of reference papers based on influence groups;visual analysis of simulation uncertainty using cost-effective sampling;foundations of multivariate functional approximation for scientific data;lifted Wasserstein matcher for fast and robust topology tracking;and VOIDGA: a view-approximation oriented image database generation approach.

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Top Research Challenges and Opportunities for Near Real-Time Extreme-scale visualization of Scientific data 20

Top Research Challenges and Opportunities for Near Real-Time...

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20th ieee International Conference on E-Science (E-Science)

作者： Pugmire, David Moreland, Kenneth Athawale, Tushar M. Hammer, James Huang, Jian Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37830 USA Univ Tennessee Elect Engn & Comp Sci Knoxville TN USA

ISBN: (纸本)9798350365627;9798350365610

The rapid advancement in scientific simulations and experimental facilities has resulted in the generation of vast amounts of data at unprecedented scales. The analysis and visualization of large amounts of data is a challenge in and of itself, but the requirements for timeliness significantly magnify these difficulties. Near real-time visualization is critical to monitor and analyze the data produced by these large facilities, but current production tools are not well-suited to these requirements. In this position paper, we share our perspective on some of the challenges, and thus, opportunities for research that stand in the way of near-real-time visualization of large scientific data.

关键词： visualization Real-time Systems Humancomputer Interactions

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Speculative Progressive Raycasting for Memory Constrained Isosurface visualization of Massive Volumes 13

Speculative Progressive Raycasting for Memory Constrained Is...

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13th ieee symposium on large data analysis and visualization (LDAV)

作者： Usher, Will Dyken, Landon Kumar, Sidharth Intel Luminary Cloud Mountain View CA 94035 USA Univ Illinois Chicago IL USA

ISBN: (纸本)9798350330182

New web technologies have enabled the deployment of powerful GPU-based computational pipelines that run entirely in the web browser, opening a new frontier for accessible scientific visualization applications. However, these new capabilities do not address the memory constraints of lightweight end-user devices encountered when attempting to visualize the massive data sets produced by today's simulations and data acquisition systems. In this paper, we propose a novel implicit isosurface rendering algorithm for interactive visualization of massive volumes within a small memory footprint. We achieve this by progressively traversing a wavefront of rays through the volume and decompressing blocks of the data on-demand to perform implicit ray-isosurface intersections. The progressively rendered surface is displayed after each pass to improve interactivity. Furthermore, to accelerate rendering and increase GPU utilization, we introduce speculative ray-block intersection into our algorithm, where additional blocks are traversed and intersected speculatively along rays as other rays terminate to exploit additional parallelism in the workload. Our entire pipeline is run in parallel on the GPU to leverage the parallel computing power that is available even on lightweight end-user devices. We compare our algorithm to the state of the art in low-overhead isosurface extraction and demonstrate that it achieves 1.7x-5.7x reductions in memory overhead and up to 8.4x reductions in data decompressed.

关键词： isosurface rendering raycasting volume rendering

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Automatic Estimation for Visual Quality Changes of Street Space via Street-View Images and Multimodal large Language Models

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ieee ACCESS 2024年 12卷 87713-87727页

作者： Liang, Hao Zhang, Jiaxin Li, Yunqin Wang, Bowen Huang, Jingyong Nanjing Forestry Univ Coll Landscape Architecture Nanjing 210037 Peoples R China Nanchang Univ Architecture & Design Coll Nanchang 330031 Peoples R China Osaka Univ Grad Sch Engn Div Sustainable Energy & Environm Engn Osaka 5650871 Japan Osaka Univ Grad Sch Informat Sci & Technol Osaka 5650871 Japan

Estimating Visual Quality of Street Space (VQoSS) is pivotal for urban design, environmental sustainability, civic engagement, etc. Recent advancements, notably in deep learning, have enabled large-scale analysis. However, traditional deep learning approaches are hampered by extensive data annotation requirements and limited adaptability across diverse VQoSS tasks. Multimodal large Language Models (MLLMs) have recently demonstrated proficiency in various computer vision tasks, positioning them as promising tools for automated VQoSS assessment. In this paper, we pioneer the application of MLLMs to VQoSS change estimation, with our empirical findings affirming their effectiveness. In addition, we introduce Street Quality Generative Pre-trained Transformer (SQ-GPT), a model that distills knowledge from the current most powerful but inaccessible (not free) GPT-4V, requiring no human efforts. SQ-GPT approaches GPT-4V's performance and is viable for large-scale VQoSS change estimation. In a case study of Nanjing, we showcase the practicality of SQ-GPT and knowledge distillation pipeline. Our work promises to be a valuable asset for future urban studies research.

关键词： visualization Task analysis Estimation Deep learning Image color analysis data models Context modeling Smart cities large language models Smart city visual quality deep learning multimodal large language models

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data Movement Visualized: A Unified Framework for Tracking and Visualizing data Movements in Heterogeneous Architectures 17

Data Movement Visualized: A Unified Framework for Tracking a...

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17th Pacific visualization Conference (PacificVis)

作者： Kesavan, Suraj P. Bhatia, Harsh Dasu, Keshav Pearce, Olga Ma, Kwan-Liu Univ Calif Davis Davis CA 95616 USA Lawrence Livermore Natl Lab Livermore CA 94550 USA

ISBN: (纸本)9798350393811;9798350393804

Whereas rapidly increasing heterogeneous compute capabilities continue to facilitate further scalability, modern applications often instead get limited by suboptimal data movement, as more and more data needs to be shipped across different hardware components (i.e., CPUs, GPUs, and other types of accelerators). We posit that understanding and improving data movement in modern use-cases require a holistic understanding of the underlying Hardware usage as well as the Communication patterns within the overall context of the Application, or as we call it, the HAC domain. Collecting and correlating HAC data currently requires interacting with several profiling tools and libraries, resulting in a tedious workflow that is neither scalable nor portable. Furthermore, existing tools for visualizing data movement profiles also focus on these domains individually, rather than offering a holistic view. We present a unified framework for tracking and visualizing data movement trends in large-scale applications deployed on heterogeneous architectures. Our framework has two interoperable components. (1) DMTracker is a lean software layer that provides a simple interface for configurable HAC profiling of GPU-enabled applications and abstracts away the complexity in using several profiling tools, resulting in a streamlined and time-correlated event history across the HAC domains. (2) DMVis is a web-based tool that combines several linked visualizations to offer holistic visual insights into the runtime behavior and resources utilization of applications, proving pivotal in identifying computationally expensive tasks and data transfers across devices. In this paper, we present the design and prototype of our framework, developed in consultation with domain experts and demonstrated on two case studies, including one for a large language model training. Initial impressions from the experts indicate a positive turn in their usual workflow of observing and tuning the performance through im

关键词： data Movement GPU acceleration Heterogeneous architectures Machine learning Performance Performance analysis software visualization visual analytics

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Level Set Restricted Voronoi Tessellation for large scale Spatial Statistical analysis

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ieee Transactions on visualization and Computer Graphics 2023年第1期29卷 548-558页

作者： Neuroth, Tyson Rieth, Martin Aditya, Konduri Lee, Myoungkyu Chen, Jacqueline H. Ma, Kwan-Liu University of California Davis United States Sandia National Laboratories United States India Institute of Science India University of Alabama United States

Spatial statistical analysis of multivariate volumetric data can be challenging due to scale, complexity, and occlusion. Advances in topological segmentation, feature extraction, and statistical summarization have helped overcome the challenges. This work introduces a new spatial statistical decomposition method based on level sets, connected components, and a novel variation of the restricted centroidal Voronoi tessellation that is better suited for spatial statistical decomposition and parallel efficiency. The resulting data structures organize features into a coherent nested hierarchy to support flexible and efficient out-of-core region-of-interest extraction. Next, we provide an efficient parallel implementation. Finally, an interactive visualization system based on this approach is designed and then applied to turbulent combustion data. The combined approach enables an interactive spatial statistical analysis workflow for large-scale data with a top-down approach through multiple-levels-of-detail that links phase space statistics with spatial features. © 2022 ieee.

关键词： Numerical methods

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data Analytics and visualization of Adaptive Collaboration Simulations

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ieee TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第1期10卷 84-93页

作者： Wachowiak-Smolikova, Renata Zhu, Haibin Nipissing Univ Dept Comp Sci & Math North Bay ON P1B 8L7 Canada

Role-based collaboration (RBC) is an adaptive computational methodology that uses roles as underlying mechanisms to facilitate and analyze system behavior for entities that collaborate and coordinate their activities with or within these systems. In dynamic environments, including those that occur in large-scale simulations, visualization provides insights into complex systems behaviors. This article presents a visual analytics (VA) approach to studying dynamics involved in adaptive collaboration (AC) for large, multiagent simulation model using new open-source tools. The results show that time-varying systems can be steered for optimal performance and assessing adaptations using VA dashboards.

关键词： Collaboration data models data visualization Analytical models Adaptive systems Adaptation models data analysis Adaptive collaboration (AC) adaptive systems big data data analytics dynamic modeling role-based collaboration (RBC) visual analytics (VA)

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A User-Driven Sampling Model for large-scale Geographical Point data visualization via Convolutional Neural Networks

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ieee TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 2023年第5期53卷 885-894页

作者： Zhou, Zhiguang Zheng, Fengling Wen, Jin Chen, Yuanyuan Li, Xinyu Liu, Yuhua Wang, Yigang Chen, Wei Hangzhou Dianzi Univ Sch Media & Design Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci Hangzhou 310018 Peoples R China Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou 310018 Peoples R China Zhejiang Univ Technol Zhejiang Carbon Neutral Innovat Inst Hangzhou 310023 Peoples R China Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Peoples R China

Numerous sampling strategies have been proposed to reduce the visual clutter of large-scale geographical point data visualization, which focus on the preservation of original data features, such as randomness, spatial distribution, and associated relationship. However, user preferences and demands are not taken into account in the course of sampling, which will lead to the sampled results deviating from user requirements and impede personalized geospatial analysis in specific application scenarios. In this article, we propose a user-driven sampling model for the visual abstraction of the large-scale geographical point data based on convolutional neural networks (CNN). First, a blue noise sampling model is applied to partition the geographical space into local areas, and a set of visual interfaces are designed to present the data features of those points in the local areas, enabling users to visually select representative points according to their requirements. Then, user preferences are quantified with a CNN model based on the eigenvectors of the representative points, which are further utilized to guide the sampling courses of the other local areas. Thus, all the sampled points will retain the spatial distribution of original data points and fulfill the user preference as far as possible. In addition, we implement a visualization framework to integrate manual point selection, CNN training, automatic point sampling, and visual comparison, allowing users to easily obtain and evaluate the sampled points from the perspectives of data analysis and user requirements. Quantitative comparisons and case studies based on real-world datasets are conducted to demonstrate the effectiveness of our sampling model in the preservation of user preferences and visual exploration of large-scale geospatial point data.

关键词： Convolutional neural networks (CNN) geographical point data sampling user preference visual abstraction

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