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14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Zheng, Boyan Sadlo, Filip Heidelberg Univ Heidelberg Germany

ISBN: (纸本)9781665439312

In this paper, we study the visual design of hierarchical multivariate data analysis. We focus on the extension of four hierarchical univariate concepts-the sunburst chart, the icicle plot, the circular treemap, and the bubble treemap-to the multivariate domain. Our study identifies several advantageous design variants, which we discuss with respect to previous approaches. and whose utility we evaluate with a user study and demonstrate for different analysis purposes and different types of data.

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

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High-quality Approximation of Scientific data using 3D Gaussian Splatting 14

High-quality Approximation of Scientific Data using 3D Gauss...

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14th symposium on large data analysis and visualization

作者： Sewell, Andres Dyken, Landon Mateevitsi, Victor A. Usher, Will Amstutz, Jefferson Marrinan, thomas Reda, Khairi Rizzi, Silvio Insley, Joseph A. Papka, Michael E. Kumar, Sidharth Petruzza, Steve Utah State Univ Logan UT 84322 USA Argonne Natl Lab Argonne IL 60439 USA Univ Illinois Chicago IL USA Luminary Cloud San Mateo CA USA NVIDIA Santa Clara CA USA Univ St Thomas St Paul MN USA Indiana Univ Indianapolis IN USA Northern Illinois Univ De Kalb IL 60115 USA

ISBN: (纸本)9798331516932;9798331516925

this work explores the application of recent advancements in radiance field rendering, specifically 3D Gaussian splatting, to generate high-quality approximations of scientific data. In this technique, a 3D Gaussian splatting model is built from a 3D point cloud generated using structure-from-motion, or a randomly initialized one when using a NeRF as input. this point cloud serves as the basis for initializing a set of Gaussian primitives, which are then refined through machine learning to minimize differences between ground truth and rendered images. We modified this pipeline to train Gaussian models directly from scientific data, eliminating the need for structure-from-motion. We test exporting an isosurface as a point cloud, which is then used to train a Gaussian model representing the dataset's isosurface. We also experimented with using a cinema database to produce a 3D Gaussian model;however, this approach yielded less promising results due to sub-optimal point cloud initialization. Our findings highlight the potential of this technique for scientific datasets, suggesting it could enable efficient post-hoc visualization with reduced computational resources.

关键词： 3D Gaussian splatting scientific data data reconstruction machine learning

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Visualizing distortions and recovering topology in continuous projection techniques

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NEUROCOMPUTING 2007年第7-9期70卷 1304-1330页

作者： Aupetit, Michael CEA DAM Dept Anal Surveillance Environm F-91680 Bruyeres Le Chatel France

the visualization of continuous multi-dimensional data based on their projection to a 2-dimensional space is a way to detect visually interesting patterns, as far as the projection provides a faithful image of the original data. In order to evaluate this faithfulness, we propose to visualize any measure associated to a projected datum or to a pair of projected data, by coloring the corresponding Voronoi cell in the projection space. We also define specific measures and show how they allow estimating visually whether some part of the projection is or is not a reliable image of the original manifolds. It also helps to figure out what the original topology of the data is, telling where the high-dimensional. manifolds have been torn or glued during the projection. We experiment these techniques with the principal component analysis and the curvilinear component analysis applied to artificial and real databases. (c) 2007 Elsevier B.V. All rights reserved.

关键词： exploratory data analysis continuous projection high-dimensional data distortion visualization uncertainty visualization topology recovering Delaunay graph Voronoi cells

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KeywordMap: Attention-based Visual Exploration for Keyword analysis 14

KeywordMap: Attention-based Visual Exploration for Keyword A...

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14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Tu, Yamei Xu, Jiayi Shen, Han-Wei Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781665439312

With the high growth rate of text data, extracting meaningful information from a large corpus becomes increasingly difficult. Keyword extraction and analysis is a common approach to tackle the problem, but it is non-trivial to identify important words in the text and represent the multifaceted properties of those words effectively. Traditional topic modeling based keyword analysis algorithms require hyper-parameters which are often difficult to tune without enough prior knowledge. In addition, the relationships among the keywords are often difficult to obtain. In this paper, we utilize the attention scores extracted from Transformer-based language models to capture word relationships. We propose a domain-driven attention tuning method, guiding the attention to learn domain-specific word relationships. From the attention, we build a keyword network and propose a novel algorithm, Attention-based Word Influence (AWI), to compute how influential each word is in the network. An interactive visual analytics system, KeywordMap, is developed to support multi-level analysis of keywords and keyword relationships through coordinated views. We measure the quality of keywords captured by our AWI algorithm quantitatively. We also evaluate the usefulness and effectiveness of KeywordMap through case studies.

关键词： Text/Document data Machine Learning Task and Requirements analysis

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Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional data 14

Mapper Interactive: A Scalable, Extendable, and Interactive ...

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14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Zhou, Youjia Chalapathi, Nithin Rathore, Archit Zhao, Yaodong Wang, Bei Univ Utah Sci Comp & Imaging SCI Inst Salt Lake City UT 84112 USA

ISBN: (纸本)9781665439312

the mapper algorithm is a popular tool from topological data analysis for extracting topological summaries of high-dimensional datasets. In this paper, we present Mapper Interactive, a web-based framework for the interactive analysis and visualization of high-dimensional point cloud data. It implements the mapper algorithm in an interactive, scalable, and easily extendable way, thus supporting practical data analysis. In particular, its command-line API can compute mapper graphs for 1 million points of 256 dimensions in about 3 minutes (4 times faster than the vanilla implementation). Its visual interface allows on-the-fly computation and manipulation of the mapper graph based on user-specified parameters and supports the addition of new analysis modules with a few lines of code. Mapper Interactive makes the mapper algorithm accessible to nonspecialists and accelerates topological analytics workflows.

关键词： High Dimensional data Scalability Issues Topology based Techniques

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FiberStars: Visual Comparison of Diffusion Tractography data between Mutiple Subjects 14

FiberStars: Visual Comparison of Diffusion Tractography Data...

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14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Franke, Loraine Weidele, Daniel Karl, I Zhang, Fan Cetin-Karayumak, Suheyla Pieper, Steve O'Donnell, Lauren J. Rathi, Yogesh Haehn, Daniel Univ Massachusetts Boston MA 02125 USA IBM Res Armonk NY USA Harvard Med Sch Boston MA 02115 USA Isomics Inc Cambridge MA USA

ISBN: (纸本)9781665439312

Tractography from high-dimensional diffusion magnetic resonance imaging (dMRI) data allows brain's structural connectivity analysis. Recent dMRI studies aim to compare connectivity patterns across subject groups and disease populations to understand subtle abnormalities in the brain's white matter connectivity and distributions of biologically sensitive dMRI derived metrics. Existing software products focus solely on the anatomy, are not intuitive or restrict the comparison of multiple subjects. In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive visualization of brain fiber clusters combining existing 3D anatomy with compact 2D visualizations. With FiberStars, researchers can analyze and compare multiple subjects in large collections of brain fibers using different views. To evaluate the usability of our software, we performed a quantitative user study. We asked domain experts and non-experts to find patterns in a tractography dataset with either FiberStars or an existing dMRI exploration tool. Our results show that participants using FiberStars can navigate extensive collections of tractography faster and more accurately. All our research, software, and results are available openly.

关键词： Neuroscience Brain Fibers Diffusion MRI High-Dimensional data 3D visualization visualization techniques

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Unravelling the Human Perspective and Considerations for Urban data visualization 14

Unravelling the Human Perspective and Considerations for Urb...

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14th IEEE Pacific visualization symposium (IEEE PacificVis)

作者： Goodwin, Sarah Meier, Sebastian Bartram, Lyn Godwin, Alex Nagel, Till Doerk, Marian Monash Univ Clayton Vic Australia HafenCity Univ Hamburg Germany Simon Fraser Univ Burnaby BC Canada Amer Univ Washington DC 20016 USA Mannheim Univ Appl Sci Mannheim Germany Potsdam Univ Appl Sci Potsdam Germany

ISBN: (纸本)9781665439312

Effective use of data is an essential asset to modern cities. visualization as a tool for analysis, exploration, and communication has become a driving force in the task of unravelling our complex urban fabrics. this paper outlines the findings from a series of three workshops from 2018-2020 bringing together experts in urban data visualization with the aim of exploring multidisciplinary perspectives from the human-centric lens. Based on the rich and detailed workshop discussions identifying challenges and opportunities for urban data visualization research, we outline major human-centric themes and considerations fundamental for CityVis design and introduce a framework for an urban visualization design space.

关键词： Human-centered computing Interaction design Interaction design process and methods Human-centered computing visualization visualization design and evaluation methods

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基于CiteSpace的建筑施工安全行为可视化分析

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铁路节能环保与安全卫生 2025年第1期15卷 60-64页

作者：何岳嵩中铁十四局集团第二工程有限公司安全监督部山东泰安271000

在建筑施工领域进行安全行为的研究至关重要,传统研究方法主要以文字叙述和表格统计为主,难以捕捉到所有相关的信息和动态。利用CiteSpace软件进行可视化分析,可以帮助研究者从大量文献数据中提取有价值的信息,并构建出建筑施工安全行... 详细信息

在建筑施工领域进行安全行为的研究至关重要,传统研究方法主要以文字叙述和表格统计为主,难以捕捉到所有相关的信息和动态。利用CiteSpace软件进行可视化分析,可以帮助研究者从大量文献数据中提取有价值的信息,并构建出建筑施工安全行为的知识图谱,揭示出不同概念和主题之间的关联和互动,识别出建筑施工安全行为研究中的空白和热点,有助于指导未来的研究方向,推动建筑施工安全行为研究的深入发展。

关键词： CiteSpace 建筑施工安全行为可视化知识图谱

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Scalable Interactive Geo visualization Platform for GIS data analysis

Scalable Interactive Geo Visualization Platform for GIS Data...

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14th IEEE Intl Conf on Dependable, Autonomic and Secure Comp / 14th IEEE Intl Conf on Pervasive Intelligence and Comp / 2nd IEEE Intl Conf on Big data Intelligence and Comp / IEEE Cyber Sci and Technol Congress (DASC/PiCom/dataCom/CyberSciTech)

作者： Nair, Lekha R. Saleem, Sarath Shetty, Sujala D. BITS Pilani Dept Comp Sci Dubai Campus Dubai U Arab Emirates

ISBN: (纸本)9781509040650

visualization of geographic data finds enormous applications in location finding, disaster management, resource allocation, mobility pattern detection etc. In this Big data era, location information is embedded in most of the available data. While many general purpose visualization tools available today fail to scale when handling big data and also lack visually appealing output, this paper aims to design and implement a web based application that offers Geographic visualization platform for users to create and publish interactive geographic maps based on large static as well as streaming geo datasets. the application is capable of integrating, analyzing and visualizing Big data from a Geographic Information Systems (GIS) perspective and here the application is tested with urban data sets and twitter streaming data to perform visualization and data driven analysis of mobility patterns and social networking activity. the application can be deployed in the cloud and can be accessed by any browser on any smart device.

关键词： Geographic visualization GIS data Online Interactive visualization Big data visualization Cartography Maps

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Web-based visualization and Analytics of Petascale data: Equity as a Tide that Lifts All Boats 14

Web-based Visualization and Analytics of Petascale data: Equ...

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14th symposium on large data analysis and visualization

作者： Panta, Aashish Huang, Xuan McCurdy, Nina Ellsworth, David Gooch, Amy A. Scorzelli, Giorgio Torres, Hector Klein, Patrice Ovando-Montejo, Gustavo A. Pascucci, Valerio Univ Utah Salt Lake City UT 84112 USA NASA Ames Res Ctr Mountain View CA USA ViSOAR LLC Salt Lake City UT USA NASA Jet Prop Lab La Canada Flintridge CA USA CALTECH Pasadena CA 91125 USA

ISBN: (纸本)9798331516932;9798331516925

Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. this paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We also optimize network utilization while streaming from petascale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any internet connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA's use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We validate our approach by improving the ability of climate scientists to visually explore their data via two fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. these dashboards, released in simpli

关键词： data visualization Petascale analytics data accessibility Equity in science Cloud computing Petabytes

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