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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Deepak Pandey Rajeev Gupta M.M. Institute of Computer Technology & Business Management Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India Computer Sc. and Engineering M.M. Engineering College Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

This manuscript provides an in-depth analysis of Major League Baseball (MLB) team performance from 2005 to 2023 using data visualization in Tableau and proposed an hybrid data visualization model (VisMLB) for Major League Baseball matches. The objective is to identify key metrics, such as batting, fielding, and pitching, that influence team success, including championship wins. The authors analyze the total number of championships won by each team, compare top-performing teams based on a variety of performance metrics, and explore the relationship between On-Base Percentage (OBP) and Slugging Percentage (SLG) for home and visitor teams in relation to home victories. Using Tableau, feature importance is visualized, and predictive models are evaluated for accuracy, precision, and recall. The findings offer a comprehensive overview of MLB performance trends over nearly two decades.

关键词： Measurement Analytical models Solid modeling Accuracy Visual analytics data visualization Predictive models Solids data models Sports

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VisQ2SQL: Towards SQL-Driven data visualization via LLMs-Grounded Preference Learning

VisQ2SQL: Towards SQL-Driven Data Visualization via LLMs-Gro...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shengze Shi Tao Ren Jun Hu State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-to-visualization (Text2Vis) aims to democratize data insights for non-expert users by transforming natural language query (NLQ) into visualization specification (VS). In view of the high dependence of rule-based methods on predefined VS templates and poor NLQ understanding ability of small data-driven methods, recent works resort to leveraging pre-trained LLMs to perform NLQ-understanding and VS-generating in Text2Vis tasks via prompt-guided in-context learning. However, existing LLM-based methods still fall short of satisfactory end-to-end Text2Vis performances primarily owing to the limited ability of pre-trained LLMs in directly retrieving and operating NLQ-intended tabular data. Inspired by the SQL generating ability born with latest LLMs, this paper proposes the idea of harnessing LLMs for SQL-driven visualization data retrieval and operation. Nonetheless, there remains a nonneglectable gap between data visualization queries in Text2Vis tasks and SQL retrieving queries in LLM corpus. To fill the gap, this paper proposes a visualization Query to SQL (VisQ2SQL) framework to obtain NLQ-intended data, primarily by fine-tuning LLMs through preference learning data-retrieval SQLs induced from VS and those generated by LLMs. We conduct extensive experiments to demonstrate the superiority of VisQ2SQL over SOTA methods, and various ablation studies to verify the efficacy of VisQ2SQL.

关键词： Structured Query Language Natural languages data visualization Transforms Contrastive learning Speech enhancement Signal processing data retrieval Acoustics

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Accounting Management data visualization Technology Based on DCGAN

Accounting Management Data Visualization Technology Based on...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Wendi Li Yu Liu School of International Management Semyung University Jecheon North Chungcheong Province South Korea Department of Culture and Art Semyung University Jecheon North Chungcheong Province South Korea

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Big data processing and analysis have become significant challenges in assembling account management. visualization of data using conventional print hasn't quite fared well; it doesn't offer readability, which means it has not been effective in decision-making. We have integrated DCGAN technology with accounting management data to strengthen the visualization of the data-set. A 6,000-record data-set was put together, cleaned, and standards set. A DCGAN model was trained for 5,000 cycles with optimal generator and discriminator components. This means that, based on the output, the actual information content on the DCGAN images is about 92 % in comparison to classical visualization techniques, with more intuitive and clearer views. These new visualizations enable to back to better understanding of data and supporting of decisions in accounting management. The research unveils the opportunity for DCGAN technology to transform the industry of data visualization in view of improved readability and usability of accounting data for accounting decisions.

关键词： Industries Adaptation models Diversity reception data visualization Transforms data models Reliability Usability Integrated circuit modeling Standards

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ProteoArk: A One-Pot Proteomics data Analysis and visualization Tool for Biologists

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JOURNAL OF PROTEOME RESEARCH 2025年第3期24卷 1008-1016页

作者： Nisar, Mahammad Soman, Sreelakshmi Pathappillil Sreelan, Sourav John, Levin Pinto, Sneha M. Kandasamy, Richard Kumaran Subbannayya, Yashwanth Prasad, Thottethodi Subrahmanya Keshava Kanekar, Saptami Raju, Rajesh Balaya, Rex Devasahayam Arokia Yenepoya Univ Ctr Integrat Om Data Sci CIODS Mangalore 575018 Karnataka India Yenepoya Univ Ctr Syst Biol & Mol Med CSBMM Yenepoya Res Ctr Mangalore 575018 Karnataka India Univ Surrey Fac Hlth & Med Sci Sch Biosci Guildford GU2 7XH England Mayo Clin Dept Lab Med & Pathol Rochester MN 55905 USA Mayo Clin Dept Immunol Rochester MN 55905 USA Mayo Clin Dept Quantitat Hlth Sci Rochester MN 55905 USA

ProteoArk is a web-based tool that offers a range of computational pipelines for comprehensive analysis and visualization of mass spectrometry-based proteomics data. The application comprises four primary sections designed to address various aspects of mass spectrometry data analysis in a single platform, including label-free and labeled samples (SILAC/iTRAQ/TMT), differential expression analysis, and data visualization. ProteoArk supports postprocessing of Proteome Discoverer, MaxQuant, and MSFragger search results. The tool also includes functional enrichment analyses such as gene ontology, protein-protein interactions, pathway analysis, and differential expression analysis, which incorporate various statistical tests. By streamlining workflows and developing user-friendly interfaces, we created a robust and accessible solution for users with basic bioinformatics skills in proteomic data analysis. Users can easily create manuscript-ready figures with a single click, including principal component analysis, heatmaps (K-means and hierarchical), MA plots, volcano plots, and circular bar plots. ProteoArk is developed using the Django framework and is freely available for users [https://***/proteoark/]. Users can also download and run the standalone version of ProteoArk using Docker as described in the instructions [https://***/proteoark/dockerpage]. The application code, input data, and documentation are available online at https://***/ArokiaRex/proteoark. A tutorial video is available on YouTube: https://***/watch?v=WFMKAZ9Slq4&ab_channel=RexD.A.B.

关键词： mass spectrometry proteomics bioinformaticstools data analysis data visualization web application

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Co-producing new knowledge systems for resilient and just coastal cities: A social-ecological-technological systems framework for data visualization

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CITIES 2025年 156卷

作者： Feagan, Mathieu Munoz-Erickson, Tischa A. Hobbins, Robert Baja, Kristin Chester, Mikhail Cook, Elizabeth M. Grimm, Nancy Grove, Morgan Iwaniec, David M. Iyer, Seema McPhearson, Timon Mendez-Lazaro, Pablo Miller, Clark Sauter, Daniel Solecki, William Tomateo, Claudia Troxler, Tiffany Welty, Claire Univ Waterloo Fac Environm 200 Univ Ave West Waterloo ON N2L 3G1 Canada US Forest Serv Int Inst Trop Forestry USDA Jardin Bot Sur Calle Ceiba Rio Piedras PR 00926 USA Univ Colorado CU Denver Business Sch 1475 Lawrence St Denver CO 80202 USA Urban Sustainabil Directors Network Port Washington WI USA Arizona State Univ Sch Sustainable Engn & Built Environm Tempe AZ 85287 USA Columbia Univ Barnard Coll Dept Environm Sci 3009 Broadway New York NY 10027 USA Arizona State Univ Sch Life Sci Tempe AZ 85287 USA Urban Forests Human Hlth & Environm Qual Suite 3505523 Res Pk Dr Baltimore MD 21228 USA Georgia State Univ Urban Studies Inst Andrew Young Sch Policy Studies Atlanta GA 30303 USA Univ Baltimore 1420 N Charles St Baltimore MD 21201 USA New Sch Urban Syst Lab 79 Fifth Ave16th Fl New York NY 10003 USA Univ Puerto Rico Grad Sch Publ Hlth Environm Hlth Dept Med Sci Campus San Juan PR USA Arizona State Univ Sch Future Innovat Soc POB 875603 Tempe AZ 85287 USA New Sch Parsons Sch Design 2 West 13th StFloor 4 New York NY 10003 USA CUNY Hunter Coll 695 Pk Ave New York NY 10021 USA CUNY Grad Ctr Earth & Environm Sci Program New York NY USA Florida Int Univ 11200 SW 8th STAHC5 368 Miami FL 33199 USA Univ Maryland Baltimore Cty UMBC 1000 Hilltop CircleTRC 102 Baltimore MD 21250 USA

With increasing frequency and severity, coastal cities are facing the effects of extreme weather events, such as sea-level rise, storm surges, hurricanes, and various types of flooding. Recent urban resilience scholarship suggests that responding to the cascading complexities of climate change requires an understanding of cities as social-ecological-technological systems, or SETS. Advances in data visualization, sensors, and analytics are making it possible for urban planners to gain more comprehensive views of cities. Yet, addressing climate complexity requires more than deploying the latest technologies;it requires transforming the institutional knowledge systems upon which cities rely for preparation and response in a climate-changed future. While debates in the theory and practice of knowledge co-production offer a rich contextual starting point, there are few practical examples of what it means to co-produce new knowledge systems capable of steering urban resilience planning in fundamentally new directions. This paper helps address this gap by offering a case study approach to co-producing new knowledge systems for SETS data visualization in three US coastal cities. Through a series of innovation spaces - dialogues, labs, and webinars - with residents, data experts, and other city stakeholders from multiple sectors, we show how to apply a knowledge systems approach to better understand, represent, and support cities as SETS. To illustrate what a redesigned knowledge system for urban resilience planning entails, we document the key steps and activities that led to a new prototype SETS platform that works with a wider range of ways of knowing - including community-based expertise, interdisciplinary research contributions, and various municipal actors' know-how - to build anticipatory capacity for visualizing and navigating the complex dynamics of a climate-changed future. Our findings point to new roles for activity-based learning, conflict, and SETS visualizatio

关键词： Climate change Coastal cities Knowledge co-production Knowledge systems Social-ecological-technological systems Urban resilience data visualization

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Different yet complementary: A systematic literature review on data journalism in visualization research and journalism studies

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JOURNALISM 2025年第2期26卷 425-444页

作者： Morini, Francesca Sodertorn Univ Huddinge Sweden

This article surveys and compares literature on data journalism from two areas of inquiry: journalism studies and visualization research. As digital interfaces become an important access point for news, journalism and visualization scholars have begun to share a common research interest: data journalism. Given their radically different traditions and histories, these areas follow very different rules in how the topic is approached. The result is two parallel scholarships on data journalism with little points of contact. Arguably, developing research space for encounters and exchange of the two is an opportunity for expanding the academic discourse on data journalism. This study aims at opening this space of exchange through a systematic literature review. 121 articles, published between 2010 and 2023, are analyzed. Findings show that the two areas of research approach data journalism with very different aspirations. In relation to data journalism, journalism studies and visualization research could be compared with Lazersfeld's distinction between critical and administrative research. These aspects cause various differences at an epistemic level, namely what, how and when knowledge about data journalism is produced.

关键词： Literature review visualization research digital journalism studies data journalism data visualization

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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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data visualization of Anomaly Detection in Semiconductor Processing Tools

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2022年第2期35卷 186-197页

作者： Fan, Shu-Kai S. Tsai, Du-Ming Jen, Chih-Hung Hsu, Chia-Yu He, Fei Juan, Li-Ting Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan Yuan Ze Univ Dept Ind Engn & Management Chungli 32003 Taiwan Lunghwa Univ Sci & Technol Dept Informat Management Taoyuan 333 Taiwan Univ Sci & Technol Beijing Collaborat Innovat Ctr Steel Technol Beijing 100083 Peoples R China

Semiconductor manufacturing plays a crucial role in the world's economic growth and technology development and is the backbone of the high value-added electronic device manufacturing industry. In this paper, a new anomaly detection framework by means of data visualization is proposed for semiconductor manufacturing. Firstly, t-Distributed Stochastic Neighbor Embedding (t-SNE) in unsupervised learning is used to transform the high-dimensional raw trace data, corresponding to normal wafers, into a two-dimensional map, with the purpose of visually observing the distribution of normal wafers. The t-SNE algorithm cannot be used at run time for a new test sample since it requires the whole dataset for the embedding transformation, and is computationally very expensive. The Multilayer Perceptron (MLP) neural network is then applied as a regressor for the real-time t-SNE embedding of a new test data. The envelope of t-SNE score estimates for a set of normal wafers is circumscribed and used as the 2D control boundary based on the Delaunay Triangulation (D.T.). A new test sample with its MLP estimated embedding points outside the D.T boundary is identified as defective. Lastly, a real-world dataset in semiconductor manufacturing is used to illustrate the proposed data visualization tool for anomaly detection. The experimental results show that a multilayer perceptron in combination with t-SNE and Delaunay Triangulation performs very well for data visualization and automated detection of anomalies.

关键词： data visualization Manufacturing data models Random forests Feature extraction Semiconductor device modeling Monitoring Semiconductor manufacturing machine learning one-class model data visualization anomaly detection

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data visualization and Fault Detection Using Bi-Kernel t-Distributed Stochastic Neighbor Embedding in Semiconductor Etching Processes

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2022年第3期35卷 522-531页

作者： Zhang, Haili Wang, Pu Gao, Xuejin Qi, Yongsheng Gao, Huihui Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China China Elect Greatwall St Fab Informat Syst Co Ltd Shenzhen Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Lab Urban Mass Transit Beijing 100124 Peoples R China Inner Mongolia Univ Technol Sch Elect Power Hohhot 010051 Peoples R China

In semiconductor etching processes, fault detection monitors the quality of wafers. However, the detailed dynamics in batch data are ignored in many traditional methods. In this paper, sequential image-based data visualization and fault detection, using bi-kernel t-distributed stochastic neighbor embedding (t-SNE), is proposed for semiconductor etching processes. In the proposed method, multi-modals, multi-phases, and abnormal samples in batches are visualized in two-dimensional maps. First, the batch data are restructured into sequential images and input to a convolutional autoencoder (CAE) to learn the abstract representation. Then, bi-kernel t-SNE is applied to visualize the CAE codes in two-dimensional maps. To reduce the computational burden and overcome the out-of-sample projection diffusion in bi-kernel t-SNE, data subsampling is used in the training procedure. Finally, a one-class support vector machine is employed to calculate the visualization control boundary, and a batch-wise index is presented for fault wafer detection. To demonstrate the feasibility and effectiveness of the proposed method, it was applied to two wafer etching datasets. The results indicate that the proposed method outperforms state-of-the-art methods in data visualization and fault detection.

关键词： data visualization Training Fault detection Etching Kernel Indexes Transforms Convolutional autoencoder (CAE) data visualization fault detection semiconductor manufacturing t-distributed stochastic neighbor embedding (t-SNE)

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data visualization for the Understanding of COVID-19

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COMPUTING IN SCIENCE & ENGINEERING 2020年第6期22卷 81-86页

作者： Comba, Joao L. D. Univ Fed Rio Grande do Sul Porto Alegre RS Brazil

visualization techniques have been front-and-center in the efforts to communicate the science around COVID-19 to the very broad audience of policymakers, scientists, healthcare providers, and the general public. In this article, I summarize and illustrate with examples how visualization can help understand different aspects of the pandemic.

关键词： data Visualisation Diseases Epidemics Health Care Medical Computing data visualization COVID 19 Policy Makers Healthcare Providers Pandemic COVID 19 data visualization Pandemics Time Series Analysis

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