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ieee International Geoscience and Remote Sensing symposium (IGARSS)

作者： Freitag, Brian M. Maskey, Manil Barciauskas, Aimee Solvsteen, Jonas Colliander, James Munroe, James NASA Marshall Space Flight Ctr Huntsville AL 35808 USA Dev Seed Washington DC USA 2i2c Code Sci & Soc Portland OR USA

ISBN: (纸本)9798350360332;9798350360325

Increasing data volumes and migration of data to the cloud introduces challenges related to scalability and complexity to users of National Aeronautics and Space Administration (NASA) Earth science data. The visualization, Exploration, and data analysis (VEDA) platform is an open-source modular cyberinfrastructure that leverages community standards and builds upon existing open-source capabilities for data services and geospatial data visualization. Four major components make up the VEDA platform - a cloud-optimized data store, backend data services, a web-based visualization and data-driven storytelling dashboard, and an analysis hub. The modular design of the VEDA platform enables the tailored use of the platform components or the platform in whole as has been demonstrated by other applications leveraging VEDA within NASA and the U.S. government.

关键词： data visualization data analysis data Science Open Science OGC STAC

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ManiVault: A Flexible and Extensible Visual Analytics Framework for High-Dimensional data

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第1期30卷 175-185页

作者： Vieth, Alexander Kroes, Thomas Thijssen, Julian van Lew, Baldur Eggermont, Jeroen Basu, Soumyadeep Eisemann, Elmar Vilanova, Anna Hollt, Thomas Lelieveldt, Boudewijn Delft Univ Technol Delft Netherlands Leiden Univ Med Ctr Leiden Netherlands TU Eindhoven Eindhoven Netherlands

Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural heritage. Tailor-made visual analytics software is developed for each specific application, limiting their applicability in other fields. However, as diverse as these fields are, their characteristics and requirements for data analysis are conceptually similar. Many applications share abstract tasks and data types and are often constructed with similar building blocks. Developing such applications, even when based mostly on existing building blocks, requires significant engineering efforts. We developed ManiVault, a flexible and extensible open-source visual analytics framework for analyzing high-dimensional data. The primary objective of ManiVault is to facilitate rapid prototyping of visual analytics workflows for visualization software developers and practitioners alike. ManiVault is built using a plugin-based architecture that offers easy extensibility. While our architecture deliberately keeps plugins self-contained, to guarantee maximum flexibility and re-usability, we have designed and implemented a messaging API for tight integration and linking of modules to support common visual analytics design patterns. We provide several visualization and analytics plugins, and ManiVault's API makes the integration of new plugins easy for developers. ManiVault facilitates the distribution of visualization and analysis pipelines and results for practitioners through saving and reproducing complete application states. As such, ManiVault can be used as a communication tool among researchers to discuss workflows and results.

关键词： data visualization Visual analytics Software Graphical user interfaces Task analysis Spatial databases Python High-dimensional data visualization framework Progressive analytics Prototyping system

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Visual Exploratory analysis for Designing large-Scale Network-on-Chip Architectures: A Domain Expert-Led Design Study

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第4期30卷 1970-1983页

作者： Wang, Shaoyu Yan, Hang Isaacs, Katherine E. Sun, Yifan Huazhong Univ Sci & Technol Wuhan 430074 Peoples R China Univ Utah Salt Lake City UT 84112 USA William & Mary Williamsburg VA 23185 USA

visualization design studies bring together visualization researchers and domain experts to address yet unsolved data analysis challenges stemming from the needs of the domain experts. Typically, the visualization researchers lead the design study process and implementation of any visualization solutions. This setup leverages the visualization researchers' knowledge of methodology, design, and programming, but the availability to synchronize with the domain experts can hamper the design process. We consider an alternative setup where the domain experts take the lead in the design study, supported by the visualization experts. In this study, the domain experts are computer architecture experts who simulate and analyze novel computer chip designs. These chips rely on a Network-on-Chip (NOC) to connect components. The experts want to understand how the chip designs perform and what in the design led to their performance. To aid this analysis, we develop Vis4Mesh, a visualization system that provides spatial, temporal, and architectural context to simulated NOC behavior. Integration with an existing computer architecture visualization tool enables architects to perform deep-dives into specific architecture component behavior. We validate Vis4Mesh through a case study and a user study with computer architecture researchers. We reflect on our design and process, discussing advantages, disadvantages, and guidance for engaging in a domain expert-led design studies.

关键词： data visualization Task analysis Computer architecture Network-on-chip Behavioral sciences Synchronization Collaboration design study network-on-chip performance analysis

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Evaluating Glyph Design for Showing large-Magnitude-Range Quantum Spins

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第4期30卷 1868-1884页

作者： Zhao, Henan Bryant, Garnett W. Griffin, Wesley Terrill, Judith E. Chen, Jian Univ Maryland Baltimore Cty Baltimore MD 21250 USA NIST Gaithersburg MD 20899 USA Stellar Sci Albuquerque NM 87110 USA Ohio State Univ Columbus OH 43210 USA

We present experimental results to explore a form of bivariate glyphs for representing large-magnitude-range vectors. The glyphs meet two conditions: (1) two visual dimensions are separable;and (2) one of the two visual dimensions uses a categorical representation (e.g., a categorical colormap). We evaluate how much these two conditions determine the bivariate glyphs' effectiveness. The first experiment asks participants to perform three local tasks requiring reading no more than two glyphs. The second experiment scales up the search space in global tasks when participants must look at the entire scene of hundreds of vector glyphs to get an answer. Our results support that the first condition is necessary for local tasks when a few items are compared. But it is not enough for understanding a large amount of data. The second condition is necessary for perceiving global structures of examining very complex datasets. Participants' comments reveal that the categorical features in the bivariate glyphs trigger emergent optimal viewers' behaviors. This work contributes to perceptually accurate glyph representations for revealing patterns from large scientific results. We release source code, quantum physics data, training documents, participants' answers, and statistical analyses for reproducible science at https://***/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580.

关键词： Task analysis Image color analysis visualization Three-dimensional displays data visualization Behavioral sciences Shape Bivariate glyph 3D glyph large-magnitude-range quantitative visualization separable and integral dimension pairs

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CommentVis: Unveiling Comment Insights Through Interactive visualization Tool 17

CommentVis: Unveiling Comment Insights Through Interactive V...

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17th Pacific visualization Conference (PacificVis)

作者： Yan, Guangjing Jang, Jinhwa Seo, Jinwook Seoul Natl Univ Seoul South Korea Samsung Elect Suwon South Korea

ISBN: (纸本)9798350393811;9798350393804

Recently, online forums have emerged as a stage for consumers to comment and share their reviews. These user comments serve as a valuable data source for marketing professionals and analysts. Nevertheless, conventional user interfaces often present an overwhelming volume of comments in a linear-structured list, significantly impeding the efficiency of marketing professionals in analyzing the feedback. In response to this challenge, we introduce CommentVis, an interactive visualization tool that helps users grasp the skeleton of comments' semantic distribution. Using the tool, analysts gain detailed information about comments with profound insights. The tool leverages state-of-the-art large-scale language models, optimizing the speed and depth of analysis of substantial text data that may take a long time for marketers. To illustrate the practical application of CommentVis, we present a usage scenario that demonstrates its effectiveness in real-world marketing analysis. The tool's impact and utility were further validated through a user study involving three marketing professionals in a global manufacturing company.

关键词： Human-centered computing-visualization-visualization systems and tools- Computing methodologies-Artificial intelligence-Natural language processing-

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Integrating GPU-Accelerated for Fast large-Scale Vessel Trajectories visualization in Maritime IoT Systems

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ieee TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第3期26卷 4048-4065页

作者： Liang, Maohan Liu, Kezhong Gao, Ruobin Li, Yan Natl Univ Singapore Dept Civil & Environm Engn Singapore 119077 Singapore Wuhan Univ Technol Sch Nav Wuhan 430062 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430072 Peoples R China

With the advancement of satellite communication technology, the maritime Internet of Things (IoT) has made significant progress. As a result, vast amounts of Automatic Identification System (AIS) data from global vessels are transmitted to various maritime stakeholders through Maritime IoT systems. AIS data contains a large amount of dynamic and static information that requires effective and intuitive visualization for comprehensive analysis. However, two major deficiencies challenge current visualization models: a lack of consideration for interactions between distant pixels and low efficiency. To address these issues, we developed a large-scale vessel trajectories visualization algorithm, called the Non-local Kernel Density Estimation (NLKDE) algorithm, which incorporates a non-local convolution process. It accurately calculates the density distribution of vessel trajectories by considering correlations between distant pixels. Additionally, we implemented the NLKDE algorithm under a Graphics Processing Unit (GPU) framework to enable parallel computing and improve operational efficiency. Comprehensive experiments using multiple vessel trajectory datasets show that the NLKDE algorithm excels in vessel trajectory density visualization tasks, and the GPU-accelerated framework significantly shortens the execution time to achieve real-time results. From both theoretical and practical perspectives, GPU-accelerated NLKDE provides technical support for real-time monitoring of vessel dynamics in complex water areas and contributes to constructing maritime intelligent transportation systems. The code for this paper can be accessed at: https://***/maohliang/GPU-NLKDE.

关键词： Trajectory data visualization Graphics processing units Artificial intelligence Convolution Real-time systems Heuristic algorithms Feature extraction data transfer data mining Trajectory visualization non-local convolution graphics processing unit parallel computing maritime Internet of Things

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Video visualization and Visual Analytics: A Task-Based and Application-Driven Investigation

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ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第11期34卷 11316-11339页

作者： Xia, Wang Sun, Guodao Li, Tong Chang, Baofeng Tang, Jingwei Zhang, Gefei Liang, Ronghua Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Video data refers to digital information in the form of a series of frames or images representing continuous motion captured by a video recording device. In various domains such as security, sports, education, and entertainment, a significant amount of video data is generated and stored daily. However, analyzing these videos manually is challenging due to their intrinsic characteristics, including large-scale, redundancy, contextual dependencies, and multimodality. Consequently, researchers have extensively explored visualization techniques to address these complexities. In this investigation, we review the state-of-the-art techniques in video visualization and visual analysis. Initially, we provide an overview of the design space for video visualization and visual analysis techniques. Subsequently, we organize and classify these techniques based on visual analysis tasks and application scenarios, providing detailed descriptions within each category. Drawing upon a comprehensive review of existing research, we provide a critical evaluation and propose potential opportunities for future research. Additionally, we have developed a web-based survey browser for convenient exploration of our created classification framework and the associated scholarly articles (https://***/VOVideo/).

关键词： Video visualization video analysis visual analytics video data survey

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Are LLMs ready for visualization? 17

Are LLMs ready for Visualization?

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17th Pacific visualization Conference (PacificVis)

作者： Vazquez, Pere-Pau UPC ViRVIG Grp Barcelona Spain

ISBN: (纸本)9798350393811;9798350393804

Generative models have received a lot of attention in many areas of academia and the industry. Their capabilities span many areas, from the invention of images given a prompt to the generation of concrete code to solve a certain programming issue. These two paradigmatic cases fall within two distinct categories of requirements, ranging from "creativity" to "precision", as characterized by Bing Chat, which employs ChatGPT-4 as its backbone. visualization practitioners and researchers have wondered to what end one of such systems could accomplish our work in a more efficient way. Several works in the literature have utilized them for the creation of visualizations. And some tools such as Lida, incorporate them as part of their pipeline. Nevertheless, to the authors' knowledge, no systematic approach for testing their capabilities has been published, which includes both extensive and in-depth evaluation. Our goal is to fill that gap with a systematic approach that analyzes three elements: whether large Language Models are capable of correctly generating a large variety of charts, what libraries they can deal with effectively, and how far we can go to configure individual charts. To achieve this objective, we initially selected a diverse set of charts, which are commonly utilized in data visualization. We then developed a set of generic prompts that could be used to generate them, and analyzed the performance of different LLMs and libraries. The results include both the set of prompts and the data sources, as well as an analysis of the performance with different configurations.

关键词： Human-centered computing-visualization-visualization techniques Human-centered computing-visualization-Empirical studies in visualization

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From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of large Foundation Models

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ieee TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2025年第5期37卷 2550-2568页

作者： Huang, Kung-Hsiang Chan, Hou Pong Fung, May Qiu, Haoyi Zhou, Mingyang Joty, Shafiq Chang, Shih-Fu Ji, Heng Salesforce AI Res San Francisco CA 94105 USA Univ Illinois Champaign IL 61820 USA Univ Calif Los Angeles Los Angeles CA 90095 USA Capital One Mclean VA 22102 USA NTU San Francisco CA 94105 USA Columbia Univ New York NY 10027 USA

data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Automatic chart understanding has witnessed significant advancements with the rise of large foundation models in recent years. Foundation models, such as large language models, have revolutionized various natural language processing tasks and are increasingly being applied to chart understanding tasks. This survey paper provides a comprehensive overview of the recent developments, challenges, and future directions in chart understanding within the context of these foundation models. We review fundamental building blocks crucial for studying chart understanding tasks. Additionally, we explore various tasks and their evaluation metrics and sources of both charts and textual inputs. Various modeling strategies are then examined, encompassing both classification-based and generation-based approaches, along with tool augmentation techniques that enhance chart understanding performance. Furthermore, we discuss the state-of-the-art performance of each task and discuss how we can improve the performance. Challenges and future directions are addressed, highlighting the importance of several topics, such as domain-specific charts, lack of efforts in developing evaluation metrics, and agent-oriented settings. This survey paper aims to provide valuable insights and directions for future research in chart understanding leveraging large foundation models.

关键词： visualization Cognition Surveys data models Measurement data mining Market research Feature extraction data visualization Complexity theory Chart understanding chart question answering chart captioning multimodality large vision-language models knowledgeable foundation model reasoning

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Visualizing large-Scale Spatial Time Series with GeoChron

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第1期30卷 1194-1204页

作者： Deng, Zikun Chen, Shifu Schreck, Tobias Deng, Dazhen Tang, Tan Xu, Mingliang Weng, Di Wu, Yingcai Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Zhejiang Univ Sch Art & Archaeol Hangzhou Peoples R China Graz Univ Technol Graz Austria Zhengzhou Univ Sch Comp & Artificial Intelligence Hangzhou Peoples R China Minist Educ Engn Res Ctr Intelligent Swarm Syst Hangzhou Peoples R China Natl Supercomp Ctr Zhengzhou Peoples R China

In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.

关键词： Spatiotemporal visualization spatial time series Storyline

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