Supporting Team-First Visual Analytics through Group Activity Representations

Sriram Karthik Badam, Zehua Zeng, Emily Wall, A. Endert, N. Elmqvist
{"title":"Supporting Team-First Visual Analytics through Group Activity Representations","authors":"Sriram Karthik Badam, Zehua Zeng, Emily Wall, A. Endert, N. Elmqvist","doi":"10.20380/GI2017.26","DOIUrl":null,"url":null,"abstract":"Collaborative visual analytics (CVA) involves sensemaking activities within teams of analysts based on coordination of work across team members, awareness of team activity, and communication of hypotheses, observations, and insights. We introduce a new type of CVA tools based on the notion of “team-first” visual analytics, where supporting the analytical process and needs of the entire team is the primary focus of the graphical user interface before that of the individual analysts. To this end, we present the design space and guidelines for team-first tools in terms of conveying analyst presence, focus, and activity within the interface. We then introduce InsightsDrive, a CVA tool for multidimensional data, that contains team-first features into the interface through group activity visualizations. This includes (1) in-situ representations that show the focus regions of all users integrated in the data visualizations themselves using color-coded selection shadows, as well as (2) ex-situ representations showing the data coverage of each analyst using multidimensional visual representations. We conducted two user studies, one with individual analysts to identify the affordances of different visual representations to inform data coverage, and the other to evaluate the performance of our team-first design with exsitu and in-situ awareness for visual analytic tasks. Our results give an understanding of the performance of our team-first features and unravel their advantages for team coordination.","PeriodicalId":93493,"journal":{"name":"Proceedings. Graphics Interface (Conference)","volume":"1 1","pages":"208-213"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Graphics Interface (Conference)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20380/GI2017.26","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

Abstract

Collaborative visual analytics (CVA) involves sensemaking activities within teams of analysts based on coordination of work across team members, awareness of team activity, and communication of hypotheses, observations, and insights. We introduce a new type of CVA tools based on the notion of “team-first” visual analytics, where supporting the analytical process and needs of the entire team is the primary focus of the graphical user interface before that of the individual analysts. To this end, we present the design space and guidelines for team-first tools in terms of conveying analyst presence, focus, and activity within the interface. We then introduce InsightsDrive, a CVA tool for multidimensional data, that contains team-first features into the interface through group activity visualizations. This includes (1) in-situ representations that show the focus regions of all users integrated in the data visualizations themselves using color-coded selection shadows, as well as (2) ex-situ representations showing the data coverage of each analyst using multidimensional visual representations. We conducted two user studies, one with individual analysts to identify the affordances of different visual representations to inform data coverage, and the other to evaluate the performance of our team-first design with exsitu and in-situ awareness for visual analytic tasks. Our results give an understanding of the performance of our team-first features and unravel their advantages for team coordination.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过小组活动表示支持团队优先的可视化分析
协作视觉分析(CVA)涉及分析师团队内基于团队成员工作协调、团队活动意识以及假设、观察和见解交流的感知活动。我们引入了一种基于“团队优先”视觉分析概念的新型CVA工具,其中支持整个团队的分析过程和需求是图形用户界面的主要重点,而不是单个分析师的重点。为此,我们提供了团队优先工具的设计空间和指导方针,以传达界面中分析师的存在、关注和活动。然后,我们介绍了InsightsDrive,这是一个用于多维数据的CVA工具,它通过小组活动可视化将团队优先的功能包含到界面中。这包括(1)使用颜色编码的选择阴影显示集成在数据可视化中的所有用户的焦点区域的原位表示,以及(2)使用多维视觉表示显示每个分析员的数据覆盖率的非原位表示。我们进行了两项用户研究,一项是与个人分析师一起确定不同视觉表示的可供性,以告知数据覆盖范围,另一项是评估我们团队首次设计的视觉分析任务的现场和现场感知性能。我们的研究结果让我们了解了团队优先特征的表现,并揭示了它们在团队协调方面的优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
2.20
自引率
0.00%
发文量
0
期刊最新文献
Towards Enabling Blind People to Fill Out Paper Forms with a Wearable Smartphone Assistant. BayesGaze: A Bayesian Approach to Eye-Gaze Based Target Selection. Personal+Context navigation: combining AR and shared displays in network path-following Interactive Exploration of Genomic Conservation AffordIt!: A Tool for Authoring Object Component Behavior in Virtual Reality
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1