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2023 ieee visualization in data science, vds 2023

ISBN: (纸本)9798350330205

The proceedings contain 5 papers. The topics discussed include: Aardvark: comparative visualization of data analysis scripts;NeighViz: towards better understanding of neighborhood effects on social groups with spatial data;a declarative specification for authoring metrics dashboards;visual comparison of text sequences generated by large language models;and HPCClusterScape: increasing transparency and efficiency of shared high-performance computing clusters for large-scale AI models.

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2023 ieee Scientific visualization Contest Winner: VisAnywhere: Developing Multiplatform Scientific visualization Applications

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2024年第5期44卷 93-103页

作者： Marrinan, Thomas Mateevitsi, Victor A. Moeller, Madeleine Kanayinkal, Alina Papka, Michael E. Univ St Thomas St Paul 55105 MN USA Argonne Natl Lab Lemont IL 60439 USA

Scientists often explore and analyze large-scale scientific simulation data by leveraging 2-D and 3-D visualizations. The data and tasks can be complex and therefore best supported using myriad display technologies, from mobile devices to large high-resolution display walls to virtual reality headsets. Using a simulation of neuron connections in the human brain provided for the 2023 ieee Scientific visualization Contest, we present our work leveraging various web technologies to create a multiplatform scientific visualization application. Users can spread visualization and interaction across multiple devices to support flexible user interfaces and both colocated and remote collaboration. Drawing inspiration from responsive web design principles, this work demonstrates that a single codebase can be adapted to develop scientific visualization applications that operate everywhere.

关键词： Neurons data visualization Collaboration Three-dimensional displays Calcium Web design Task analysis

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Aardvark: Comparative visualization of data Analysis Scripts

Aardvark: Comparative Visualization of Data Analysis Scripts

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ieee Conference on visualization in data science (vds)

作者： Faust, Rebecca Scheidegger, Carlos North, Chris Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Univ Arizona Dept Comp Sci HDC Lab Tucson AZ USA

ISBN: (纸本)9798350330205

Debugging programs is one of the most challenging and time consuming parts of programming. data science scripts present additional challenges as debugging often centers around more exploratory tasks, such as understanding the differences between results under different parameter settings. In fact, a common exploratory debugging practice is to run, modify, and re-run a script to observe the effects of the modification. Analysts perform this process frequently as they explore different settings and algorithms in their analysis. However, traditional debugging methods are not well suited to comparing across multiple executions of a script. They often require maintaining two instances of the debugging method and making manual, serial comparisons of program values. To address this gap, we present Aardvark, a comparative trace-based debugging method for identifying and visualizing the differences between two executions of data analysis scripts. Aardvark traces two consecutive instances of an analysis script, identifies the differences between them, and presents them through comparative visualizations. We present a prototype implementation in Python as well as an extension to support scripts in Jupyter notebooks. Finally, to demonstrate Aardvark, we provide two usage scenarios on real world analysis scripts.

关键词： Interactive visualization Program Traces Jupyter Debugging Comparison

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NeighViz: Towards Better Understanding of Neighborhood Effects on Social Groups with Spatial data

NeighViz: Towards Better Understanding of Neighborhood Effec...

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ieee Conference on visualization in data science (vds)

作者： Yu, Yue Wang, Yifang Yang, Qisen Weng, Di Zhang, Yongjun Wu, Xiaogang Wu, Yingcai Qu, Huamin Hong Kong Univ Sci & Technol Hong Kong Peoples R China Northwestern Univ Ctr Sci Sci & Innovat Evanston IL 60208 USA Zhejiang Univ State Key Lab CAD & CG Hangzhou Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Stonybrook Univ Stony Brook NY USA NYU New York NY USA

ISBN: (纸本)9798350330205

Understanding how local environments influence individual behaviors, such as voting patterns or suicidal tendencies, is crucial in social science to reveal and reduce spatial disparities and promote social well-being. With the increasing availability of large-scale individual-level census data, new analytical opportunities arise for social scientists to explore human behaviors (e.g., political engagement) among social groups at a fine-grained level. However, traditional statistical methods mostly focus on global, aggregated spatial correlations, which are limited to understanding and comparing the impact of local environments (e.g., neighborhoods) on human behaviors among social groups. In this study, we introduce a new analytical framework for analyzing multi-variate neighborhood effects between social groups. We then propose NeighViz, an interactive visual analytics system that helps social scientists explore, understand, and verify the influence of neighborhood effects on human behaviors. Finally, we use a case study to illustrate the effectiveness and usability of our system.

关键词： Neighborhood Effects Social Groups Spatial data Visual Analytics

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Communication-Efficient Verifiable data Streaming Protocol in the Multi-User Setting

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ieee TRANSACTIONS ON CLOUD COMPUTING 2023年第4期11卷 3341-3354页

作者： Jing, Xuan Miao, Meixia Wei, Jianghong Wang, Jianfeng Xidian Univ State Key Lab Integrated Serv Networks ISN Xian 710071 Peoples R China Xian Univ Posts & Telecommun Sch Cyberspace Secur Xian 710121 Peoples R China State Key Lab Math Engn & Adv Comp Zhengzhou 450002 Peoples R China

Verifiable data streaming (vds) protocols enable end users with limited storage space to continuously stream data items to an untrusted cloud server, while preserving the capacity of verifying the integrity of those retrieved data items for downstream tasks. Although there has been plenty of research around the construction of vds, we observe that they all focus on the scenario of single-user. When deploying these vds protocols into more common applications that involve multiple users' data (e.g., network data monitoring and stock trends analysis), the size of the proof used to prove the integrity of retrieved data items grows linearly with the number of involved users. This would bring tremendous communication overhead, especially for lightweight users. To this end, we initiate the study of vds protocols that are suitable for multi-user (or cross-user) setting. Specifically, we first introduce a new primitive called aggregatable chameleon vector commitment (ACVC) that allows to aggregate multiple proofs from different commitments into a single proof. Then, based on ACVC, we present a communication-efficient vds protocol for the multi-user setting. That is, when querying data items from multiple users, the size of corresponding proof is constant and independent of the number of involved users. Theoretical analysis indicates that the proposed vds protocol outperforms previous vds protocols in terms of communication overhead. We also implement the proposed ACVC, and conduct extensive experiments to demonstrate its practicability.

关键词： Cloud storage cross-user verification vector commitment verifiable data streaming

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Online News-Based Economic Sentiment Index

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ieee Transactions on Big data 2025年第3期11卷 1464-1474页

作者： Kang, Nathaniel Min, Dongeun Cho, Yonghun Ko, Dong-Whan Kim, Hyun Hak Choeh, Joon Yeon Im, Jongho Yonsei University Department of Statistics Data Science Seoul03722 Korea Republic of Korea National Defense University Nonsan-si Korea Republic of Kookmin University Department of Economics Seoul02707 Korea Republic of Sejong University Department of Software Seoul05006 Korea Republic of

The accurate prediction of industry trends has become increasingly challenging because of unforeseen events. To address this challenge, this study proposes a deep learning approach to generate an economic sentiment index by integrating Natural Language Processing (NLP) models and image-clustering techniques. We first employ sampling techniques to create standardized online news datasets. Feature engineering techniques from the Korean Bidirectional Encoder Representations from Transformers (KoBERT) model are then used to generate relevance and sentiment scores for the textual data. Further, to enhance visualization and clustering, we transform the textual data into joint plot images, which are grouped into distinct clusters based on news categories. Finally, using Multi-criteria Decision Analysis, the various scores and cluster information are synthesized to generate the final economic sentiment index. This approach improves visualization and enhances the interpretability of the generated index. The proposed algorithm is applied to construct a new economic sentiment index for the Information and Communications Technology (ICT) industry in South Korea. © 2015 ieee.

关键词： data fusion

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data visualization for Digital Twins

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COMPUTING IN science & ENGINEERING 2023年第2期25卷 58-63页

作者： Comba, Joao L. D. Santos, Nicolau O. Rivera, Jonathan C. Romeu, Regis K. Abel, Mara Univ Fed Rio Grande do Sul Inst Informat BR-91501970 Porto Alegre RS Brazil

visualization techniques are useful in the analysis and insight generation for applications in computing in science and engineering. In this article, we describe the importance of visualization to a digital twin (DT), a virtual representation of a physical object, process or system that can be applied for different tasks, such as data-driven simulation, analysis or monitoring. We illustrate tasks in DTs and give examples of how visualization techniques can be applied for DTs in different application areas.

关键词： data visualization Digital twins Task analysis Monitoring

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Preparing Future data visualization Designers for Professional Practice 1

Preparing Future Data Visualization Designers for Profession...

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1st ieee VIS Workshop on visualization Education, Literacy, and Activities (EduVis) part of the ieee VIS Conference

作者： Parsons, Paul Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9798350330304

As the professional field of data visualization grows, so does the importance of preparing students effectively for the demands of real-world practice. Computing education has historically sought to teach and evaluate abstract knowledge (e.g., theories, principles, guidelines, design patterns) and the application of such knowledge to given problems. However, situations faced in professional practice are often messy, dynamic, and uncertain, and do not lend themselves well to the clear and direct application of such knowledge. This leaves a gap between the knowledge learned in the classroom and what is required for skillful practice in professional settings. In this paper, I discuss some historical reasons for this dominant pedagogical perspective, some of the core features of professional practice that are not typically taught in classrooms, and ways in which data visualization design can be taught to be more resonant with the experience of professional practice.

关键词： data visualization

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Application of Mathematical Optimization in data visualization and Visual Analytics: A Survey

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ieee TRANSACTIONS ON BIG data 2023年第4期9卷 1018-1037页

作者： Sun, Guodao Zhu, Zihao Zhang, Gefei Xu, Chaoqing Wang, Yunchao Zhu, Sujia Chang, Baofeng Liang, Ronghua Zhejiang Univ Technol Hangzhou 310023 Zhejiang Peoples R China Zhejiang Univ City Coll Hangzhou 310015 Zhejiang Peoples R China

Mathematical optimization is the process of determining the set of globally or locally optimal parameters in a finite or infinite search space. It has been extensively employed in the research areas of computer science, engineering, operations research, and economics. The application of mathematical optimization has also been extended to data visualization, where it can enhance data processing, structure visualization, and facilitate exploration. However, the current state of summarization in the application of mathematical optimization in data visualization remains inadequate. In this article, we review and classify the existing techniques for advanced mathematical optimization in the fields of data visualization and visual analytics. The classification is conducted based on a classical visualization pipeline, including data enhancement and transformation, representation and rendering, as well as interactive exploration and analysis. We also discuss various mathematical optimization models and their solution methods to help readers gain a better understanding of the relationship among models, visualization, and application scenarios. We additionally provide an online exploration demo, which could enable users to interactively find relevant articles. Based on the limitations and potential trends revealed in the existing literature, we define future challenges in the cross-disciplinary of mathematical optimization and data visualization.

关键词： data visualization mathematical optimization scientific visualization visual analytics

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Nathan Selikoff: Explorations in Higher Dimensionality and Complexity

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ieee COMPUTER GRAPHICS AND APPLICATIONS 2023年第5期43卷 122-130页

作者： Selikoff, Nathan Campbell, Bruce D. Samsel, Francesca Data Visualizat & UI Engn Artist Founder Orlando FL 32801 USA Rhode Isl Sch Design Providence RI 02903 USA Univ Texas Texas Adv Comp Ctr Austin TX 78712 USA

While data scientists pursue new extremes of high-dimensional data, and data visualization professionals attempt to visualize within that space, Nathan Selikoff's work suggested a unique perspective on relationship among complexity, higher dimensions, and sensing what otherwise cannot be sensed. His long-tailed pursuit of visualizing complexity piqued our interest as we interviewed him for this article.

关键词： data visualization Complexity theory Sensors data science

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