2022 IEEE Visualization and Visual Analytics (VIS)最新文献_第2页

Let's Get Personal: Exploring the Design of Personalized Visualizations 让我们变得个性化:探索个性化可视化的设计

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-10-01 DOI: 10.1109/VIS54862.2022.00026

Beleicia B. Bullock, Shunan Guo, E. Koh, R. Rossi, F. Du, J. Hoffswell

引用次数: 0

TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization 探索和管理稀疏决策树与交互式可视化

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-09-19 DOI: 10.1109/VIS54862.2022.00021

Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, C. Rudin, M. Seltzer

引用次数: 6

RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups RMExplorer:一种可视化分析方法，用于探索疾病风险模型在人口亚组上的性能和公平性

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-09-14 DOI: 10.1109/VIS54862.2022.00019

B. Kwon, U. Kartoun, S. Khurshid, Mikhail Yurochkin, Subha Maity, Deanna G. Brockman, A. Khera, P. Ellinor, S. Lubitz, Kenney Ng

{"title":"RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups","authors":"B. Kwon, U. Kartoun, S. Khurshid, Mikhail Yurochkin, Subha Maity, Deanna G. Brockman, A. Khera, P. Ellinor, S. Lubitz, Kenney Ng","doi":"10.1109/VIS54862.2022.00019","DOIUrl":"https://doi.org/10.1109/VIS54862.2022.00019","url":null,"abstract":"Disease risk models can identify high-risk patients and help clinicians provide more personalized care. However, risk models de-veloped on one dataset may not generalize across diverse subpop-ulations of patients in different datasets and may have unexpected performance. It is challenging for clinical researchers to inspect risk models across different subgroups without any tools. Therefore, we developed an interactive visualization system called RMExplorer (Risk Model Explorer) to enable interactive risk model assessment. Specifically, the system allows users to define subgroups of patients by selecting clinical, demographic, or other characteristics, to ex-plore the performance and fairness of risk models on the subgroups, and to understand the feature contributions to risk scores. To demonstrate the usefulness of the tool, we conduct a case study, where we use RMExplorer to explore three atrial fibrillation risk models by applying them to the UK Biobank dataset of 445,329 individuals. RMExplorer can help researchers to evaluate the performance and biases of risk models on subpopulations of interest in their data.","PeriodicalId":190244,"journal":{"name":"2022 IEEE Visualization and Visual Analytics (VIS)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127194909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

VegaFusion: Automatic Server-Side Scaling for Interactive Vega Visualizations VegaFusion:交互式Vega可视化的自动服务器端缩放

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-08-13 DOI: 10.1109/VIS54862.2022.00011

Nicolas Kruchten, Jon Mease, Dominik Moritz

引用次数: 2

Droplet-Local Line Integration for Multiphase Flow 多相流的液滴局部线集成

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-07-27 DOI: 10.1109/VIS54862.2022.00036

Alexander Straub, Sebastian Boblest, G. Karch, F. Sadlo, T. Ertl

引用次数: 0

Beyond Visuals: Examining the Experiences of Geoscience Professionals With Vision Disabilities in Accessing Data Visualizations 超越视觉:检视有视觉障碍的地球科学专业人员获取数据可视化的经验

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-07-27 DOI: 10.1109/VIS54862.2022.00041

N. Cherukuru, D. Bailey, Tiffany Fourment, B. Hatheway, M. Holland, Matt Rehme

{"title":"Beyond Visuals: Examining the Experiences of Geoscience Professionals With Vision Disabilities in Accessing Data Visualizations","authors":"N. Cherukuru, D. Bailey, Tiffany Fourment, B. Hatheway, M. Holland, Matt Rehme","doi":"10.1109/VIS54862.2022.00041","DOIUrl":"https://doi.org/10.1109/VIS54862.2022.00041","url":null,"abstract":"Data visualizations are ubiquitous in all disciplines and have become the primary means of analysing data and communicating insights. However, the predominant reliance on visual encoding of data con-tinues to create accessibility barriers for people who are blind/vision impaired resulting in their under representation in Science, Tech-nology, Engineering and Mathematics (STEM) disciplines. This research study seeks to understand the experiences of professionals who are blind/vision impaired in one such STEM discipline (geo-sciences) in accessing data visualizations. In-depth, semi-structured interviews with seven professionals were conducted to examine the accessibility barriers and areas for improvement to inform acces-sibility research pertaining to data visualizations through a socio-technical lens. A reflexive thematic analysis revealed the negative impact of visualizations in influencing their career path, lack of data exploration tools for research, barriers in accessing works of peers and mismatched pace of visualization and accessibility research. The article also includes recommendations from the participants to address some of these accessibility barriers.","PeriodicalId":190244,"journal":{"name":"2022 IEEE Visualization and Visual Analytics (VIS)","volume":"3132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127470919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

VisQuiz: Exploring Feedback Mechanisms to Improve Graphical Perception VisQuiz:探索反馈机制以提高图形感知

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-07-19 DOI: 10.1109/VIS54862.2022.00028

R. Birchfield, Maddison Caten, Errica Cheng, Madyson Kelly, Truman Larson, Hoan Phan Pham, Yiren Ding, Noëlle Rakotondravony, Lane Harrison

{"title":"VisQuiz: Exploring Feedback Mechanisms to Improve Graphical Perception","authors":"R. Birchfield, Maddison Caten, Errica Cheng, Madyson Kelly, Truman Larson, Hoan Phan Pham, Yiren Ding, Noëlle Rakotondravony, Lane Harrison","doi":"10.1109/VIS54862.2022.00028","DOIUrl":"https://doi.org/10.1109/VIS54862.2022.00028","url":null,"abstract":"In this paper, we explore the design and evaluation of feedback for graphical perception tasks, called VisQuiz. Using a quiz-like metaphor, we design feedback for a typical visualization comparison experiment, showing participants their answer alongside the correct answer in an animated sequence in each trial, as well as summary feedback at the end of trial sections. To evaluate VisQuiz, we conduct a between-subjects experiment, including three stages of 40 trials each with a control condition that included only summary feedback. Results from $n=80$ participants show that once participants started receiving trial feedback (Stage 2) they performed significantly better with bubble charts than those in the control condition. This effect carried over when feedback was removed (Stage 3). Results also suggest an overall trend of improved performance due to feedback. We discuss these findings in the context of other visualization literacy efforts, and possible future work at the intersection of visualization, feedback, and learning. Experiment data and analysis scripts are available at the following repository https://osf.io/jys5d/","PeriodicalId":190244,"journal":{"name":"2022 IEEE Visualization and Visual Analytics (VIS)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130432129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles 基于深度学习的时变标量集合加速概率行军立方体

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-07-15 DOI: 10.1109/VIS54862.2022.00040

Mengjiao Han, Tushar M. Athawale, D. Pugmire, Chris R. Johnson

{"title":"Accelerated Probabilistic Marching Cubes by Deep Learning for Time-Varying Scalar Ensembles","authors":"Mengjiao Han, Tushar M. Athawale, D. Pugmire, Chris R. Johnson","doi":"10.1109/VIS54862.2022.00040","DOIUrl":"https://doi.org/10.1109/VIS54862.2022.00040","url":null,"abstract":"Visualizing the uncertainty of ensemble simulations is challenging due to the large size and multivariate and temporal features of en-semble data sets. One popular approach to studying the uncertainty of ensembles is analyzing the positional uncertainty of the level sets. Probabilistic marching cubes is a technique that performs Monte Carlo sampling of multivariate Gaussian noise distributions for positional uncertainty visualization of level sets. However, the technique suffers from high computational time, making interactive visualization and analysis impossible to achieve. This paper introduces a deep-learning-based approach to learning the level-set uncertainty for two-dimensional ensemble data with a multivariate Gaussian noise assumption. We train the model using the first few time steps from time-varying ensemble data in our workflow. We demonstrate that our trained model accurately infers uncertainty in level sets for new time steps and is up to 170X faster than that of the original probabilistic model with serial computation and 10X faster than that of the original parallel computation.","PeriodicalId":190244,"journal":{"name":"2022 IEEE Visualization and Visual Analytics (VIS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131692594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Toward Systematic Design Considerations of Organizing Multiple Views 多视图组织的系统设计思考

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-07-15 DOI: 10.1109/VIS54862.2022.00030

A. Shaikh, D. Koop, Hamed Alhoori, Maoyuan Sun

{"title":"Toward Systematic Design Considerations of Organizing Multiple Views","authors":"A. Shaikh, D. Koop, Hamed Alhoori, Maoyuan Sun","doi":"10.1109/VIS54862.2022.00030","DOIUrl":"https://doi.org/10.1109/VIS54862.2022.00030","url":null,"abstract":"Multiple-view visualization (MV) has been used for visual analytics in various fields (e.g., bioinformatics, cybersecurity, and intelligence analysis). Because each view encodes data from a particular per-spective, analysts often use a set of views laid out in 2D space to link and synthesize information. The difficulty of this process is impacted by the spatial organization of these views. For instance, connecting information from views far from each other can be more challenging than neighboring ones. However, most visual analysis tools currently either fix the positions of the views or completely delegate this organization of views to users (who must manually drag and move views). This either limits user involvement in managing the layout of MV or is overly flexible without much guidance. Then, a key design challenge in MV layout is determining the factors in a spatial organization that impact understanding. To address this, we review a set of MV-based systems and identify considerations for MV layout rooted in two key concerns: perception, which considers how users perceive view relationships, and content, which considers the relationships in the data. We show how these allow us to study and analyze the design of MV layout systematically.","PeriodicalId":190244,"journal":{"name":"2022 IEEE Visualization and Visual Analytics (VIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114285807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Oscar: A Semantic-based Data Binning Approach Oscar:一种基于语义的数据分组方法

2022 IEEE Visualization and Visual Analytics (VIS) Pub Date : 2022-07-15 DOI: 10.1109/VIS54862.2022.00029

V. Setlur, M. Correll, S. Battersby

引用次数: 3