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2021 IEEE Visualization Conference (VIS)最新文献

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A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling 定性因果建模的混合主动视觉分析方法
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-09-08 DOI: 10.1109/VIS49827.2021.9623318
Fahd Husain, Pascale Proulx, Meng-Wei Chang, Rosa Romero Gómez, H. Vasquez
{"title":"A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling","authors":"Fahd Husain, Pascale Proulx, Meng-Wei Chang, Rosa Romero Gómez, H. Vasquez","doi":"10.1109/VIS49827.2021.9623318","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623318","url":null,"abstract":"Modeling complex systems is a time-consuming, difficult and fragmented task, often requiring the analyst to work with disparate data, a variety of models, and expert knowledge across a diverse set of domains. Applying a user-centered design process, we developed a mixed-initiative visual analytics approach, a subset of the Causemos platform, that allows analysts to rapidly assemble qualitative causal models of complex socio-natural systems. Our approach facilitates the construction, exploration, and curation of qualitative models bringing together data across disparate domains. Referencing a recent user evaluation, we demonstrate our approach’s ability to interactively enrich user mental models and accelerate qualitative model building.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133917340","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: 2
ConVIScope: Visual Analytics for Exploring Patient Conversations ConVIScope:用于探索患者对话的可视化分析
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-08-30 DOI: 10.1109/VIS49827.2021.9623269
Raymond Li, Enamul Hoque, G. Carenini, R. Lester, Raymond Chau
{"title":"ConVIScope: Visual Analytics for Exploring Patient Conversations","authors":"Raymond Li, Enamul Hoque, G. Carenini, R. Lester, Raymond Chau","doi":"10.1109/VIS49827.2021.9623269","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623269","url":null,"abstract":"The proliferation of text messaging for mobile health is generating a large amount of patient-doctor conversations that can be extremely valuable to health care professionals. We present ConVIScope, a visual text analytic system that tightly integrates interactive visualization with natural language processing in analyzing patient-doctor conversations. ConVIScope was developed in collaboration with healthcare professionals following a user-centered iterative design. Case studies with six domain experts suggest the potential utility of ConVIScope and reveal lessons for further developments.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114142745","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
A Visual Analytics System for Water Distribution System Optimization 配水系统优化的可视化分析系统
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-08-28 DOI: 10.1109/VIS49827.2021.9623272
Yiran Li, Erin Musabandesu, Takanori Fujiwara, F. Loge, K. Ma
{"title":"A Visual Analytics System for Water Distribution System Optimization","authors":"Yiran Li, Erin Musabandesu, Takanori Fujiwara, F. Loge, K. Ma","doi":"10.1109/VIS49827.2021.9623272","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623272","url":null,"abstract":"The optimization of water distribution systems (WDSs) is vital to minimize energy costs required for their operations. A principal approach taken by researchers is identifying an optimal scheme for water pump controls through examining computational simulations of WDSs. However, due to a large number of possible control combinations and the complexity of WDS simulations, it remains non-trivial to identify the best pump controls by reviewing the simulation results. To address this problem, we design a visual analytics system that helps understand relationships between simulation inputs and outputs towards better optimization. Our system incorporates interpretable machine learning as well as multiple linked visualizations to capture essential input-output relationships from complex WDS simulations. We demonstrate our system’s effectiveness through a practical case study and evaluate its usability through expert reviews. Our results show that our system can lessen the burden of analysis and assist in determining optimal operating schemes.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127874232","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
Intercept Graph: An Interactive Radial Visualization for Comparison of State Changes 截距图:用于比较状态变化的交互式径向可视化
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-08-19 DOI: 10.1109/VIS49827.2021.9623307
Shaolun Ruan, Yong Wang, Qiang Guan
{"title":"Intercept Graph: An Interactive Radial Visualization for Comparison of State Changes","authors":"Shaolun Ruan, Yong Wang, Qiang Guan","doi":"10.1109/VIS49827.2021.9623307","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623307","url":null,"abstract":"State change comparison of multiple data items is often necessary in multiple application domains, such as medical science, financial engineering, sociology, biological science, etc. Slope graphs and grouped bar charts have been widely used to show a “before-and-after” story of different data states and indicate their changes. However, they visualize state changes as either slope or difference of bars, which has been proved less effective for quantitative comparison. Also, both visual designs suffer from visual clutter issues with an increasing number of data items. In this paper, we propose Intercept Graph, a novel visual design to facilitate effective interactive comparison of state changes. Specifically, a radial design is proposed to visualize the starting and ending states of each data item and the line segment length explicitly encodes the “state change By interactively adjusting the radius of the inner circular axis, Intercept Graph can smoothly filter the large state changes and magnify the difference between similar state changes, mitigating the visual clutter issues and enhancing the effective comparison of state changes. We conducted a case study through comparing Intercept Graph with slope graphs and grouped bar charts on real datasets to demonstrate the effectiveness of Intercept Graph.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116612404","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: 2
Contrastive Identification of Covariate Shift in Image Data 对比识别图像数据中的变量偏移
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-08-18 DOI: 10.1109/VIS49827.2021.9623289
Matthew Lyle Olson, Thu Nguyen, Gaurav Dixit, Neale Ratzlaff, Weng-Keen Wong, Minsuk Kahng
{"title":"Contrastive Identification of Covariate Shift in Image Data","authors":"Matthew Lyle Olson, Thu Nguyen, Gaurav Dixit, Neale Ratzlaff, Weng-Keen Wong, Minsuk Kahng","doi":"10.1109/VIS49827.2021.9623289","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623289","url":null,"abstract":"Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data. However, detecting covariate shift is challenging, especially when the data consists of high-dimensional images, and when multiple types of localized covariate shift affect different subspaces of the data. Although automated techniques can be used to detect the existence of covariate shift, our goal is to help human users characterize the extent of covariate shift in large image datasets with interfaces that seamlessly integrate information obtained from the detection algorithms. In this paper, we design and evaluate a new visual interface that facilitates the comparison of the local distributions of training and test data. We conduct a quantitative user study on multi-attribute facial data to compare two different learned low-dimensional latent representations (pretrained ImageNet CNN vs. density ratio) and two user analytic workflows (nearest-neighbor vs. cluster-to-cluster). Our results indicate that the latent representation of our density ratio model, combined with a nearest-neighbor comparison, is the most effective at helping humans identify covariate shift.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130704491","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: 7
Fixation and Creativity in Data Visualization Design: Experiences and Perspectives of Practitioners 数据可视化设计中的固定与创造力:实践者的经验与观点
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-08-14 DOI: 10.1109/VIS49827.2021.9623297
Paul C. Parsons, P. Shukla, Chorong Park
{"title":"Fixation and Creativity in Data Visualization Design: Experiences and Perspectives of Practitioners","authors":"Paul C. Parsons, P. Shukla, Chorong Park","doi":"10.1109/VIS49827.2021.9623297","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623297","url":null,"abstract":"Data visualization design often requires creativity, and research is needed to understand its nature and means for promoting it. The current visualization literature on creativity is not well developed, especially with respect to the experiences of professional data visualization designers. We conducted semi-structured interviews with 15 data visualization practitioners, focusing on a specific aspect of creativity known as design fixation. Fixation occurs when designers adhere blindly or prematurely to a set of ideas that limit creative outcomes. We present practitioners’ experiences and perspectives from their own design practice, specifically focusing on their views of (i) the nature of fixation, (ii) factors encouraging fixation, and (iii) factors discouraging fixation. We identify opportunities for future research related to chart recommendations, inspiration, and perspective shifts in data visualization design.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122137862","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: 4
Narrative Sensemaking: Strategies for Narrative Maps Construction 叙事意义建构:叙事地图建构策略
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-08-13 DOI: 10.1109/VIS49827.2021.9623296
Brian Felipe Keith Norambuena, Tanushree Mitra, Chris North
{"title":"Narrative Sensemaking: Strategies for Narrative Maps Construction","authors":"Brian Felipe Keith Norambuena, Tanushree Mitra, Chris North","doi":"10.1109/VIS49827.2021.9623296","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623296","url":null,"abstract":"Narrative sensemaking is a fundamental process to understand sequential information. Narrative maps are a visual representation framework that can aid analysts in this process. They allow analysts to understand the big picture of a narrative, uncover new relationships between events, and model connections between storylines. As a sensemaking tool, narrative maps have applications in intelligence analysis, misinformation modeling, and computational journalism. In this work, we seek to understand how analysts construct narrative maps in order to improve narrative map representation and extraction methods. We perform an experiment with a data set of news articles. Our main contribution is an analysis of how analysts construct narrative maps. The insights extracted from our study can be used to design narrative map visualizations, extraction algorithms, and visual analytics tools to support the sensemaking process.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116684189","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: 2
Bayesian Modelling of Alluvial Diagram Complexity 冲积图复杂性的贝叶斯建模
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-08-13 DOI: 10.1109/VIS49827.2021.9623282
Anjana Arunkumar, Shashank Ginjpalli, Chris Bryan
{"title":"Bayesian Modelling of Alluvial Diagram Complexity","authors":"Anjana Arunkumar, Shashank Ginjpalli, Chris Bryan","doi":"10.1109/VIS49827.2021.9623282","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623282","url":null,"abstract":"Alluvial diagrams are a popular technique for visualizing flow and relational data. However, successfully reading and interpreting the data shown in an alluvial diagram is likely influenced by factors such as data volume, complexity, and chart layout. To understand how alluvial diagram consumption is impacted by its visual features, we conduct two crowdsourced user studies with a set of alluvial diagrams of varying complexity, and examine (i) participant performance on analysis tasks, and (ii) the perceived complexity of the charts. Using the study results, we employ Bayesian modelling to predict participant classification of diagram complexity. We find that, while multiple visual features are important in contributing to alluvial diagram complexity, interestingly the importance of features seems to depend on the type of complexity being modeled, i.e. task complexity vs. perceived complexity.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116818554","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: 4
How Learners Sketch Data Stories 学习者如何勾勒数据故事
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-08-10 DOI: 10.1109/VIS49827.2021.9623299
Rahul Bhargava, Dee Williams, C. D’Ignazio
{"title":"How Learners Sketch Data Stories","authors":"Rahul Bhargava, Dee Williams, C. D’Ignazio","doi":"10.1109/VIS49827.2021.9623299","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623299","url":null,"abstract":"Learning data storytelling involves a complex web of skills. Professional and academic educational offerings typically focus on the computational literacies required, but professionals in the field employ many non-technical methods; sketching by hand on paper is a common practice. This paper introduces and classifies a corpus of 101 data sketches produced by participants as part of a guided learning activity in informal and formal settings. We manually code each sketch against 12 metrics related to visual encodings, representations, and story structure. We find evidence for preferential use of positional and shape-based encodings, frequent use of symbolic and textual representations, and a high prevalence of stories comparing subsets of data. These findings contribute to our understanding of how learners sketch with data. This case study can inform tool design for learners, and help create educational programs that introduce novices to sketching practices used by experts.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123458677","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
Gemini2: Generating Keyframe-Oriented Animated Transitions Between Statistical Graphics Gemini2:在统计图形之间生成面向关键帧的动画过渡
2021 IEEE Visualization Conference (VIS) Pub Date : 2021-08-09 DOI: 10.1109/VIS49827.2021.9623291
Younghoon Kim, Jeffrey Heer
{"title":"Gemini2: Generating Keyframe-Oriented Animated Transitions Between Statistical Graphics","authors":"Younghoon Kim, Jeffrey Heer","doi":"10.1109/VIS49827.2021.9623291","DOIUrl":"https://doi.org/10.1109/VIS49827.2021.9623291","url":null,"abstract":"Complex animated transitions may be easier to understand when divided into separate, consecutive stages. However, effective staging requires careful attention to both animation semantics and timing parameters. We present Gemini2, a system for creating staged animations from a sequence of chart keyframes. Given only a start state and an end state, Gemini2 can automatically recommend intermediate keyframes for designers to consider. The Gemini2 recommendation engine leverages Gemini, our prior work, and GraphScape to itemize the given complex change into semantic edit operations and to recombine operations into stages with a guided order for clearly conveying the semantics. To evaluate Gemini2’s recommendations, we conducted a human-subject study in which participants ranked recommended animations from both Gemini2 and Gemini. We find that Gemini2’s animation recommendation ranking is well aligned with subjects’ preferences, and Gemini2 can recommend favorable animations that Gemini cannot support.","PeriodicalId":387572,"journal":{"name":"2021 IEEE Visualization Conference (VIS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124605215","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: 13
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