CRI: II-New: An Infrastructure of Display Devices to Study Visual Analytics Beyond the Desktop

CRI：II-新：用于研究桌面之外的视觉分析的显示设备基础设施

• 批准号: 1730396
• 负责人: Enrico Bertini
• 金额: $ 27.36万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1730396&HistoricalAwards=false
• 关键词: CRI; II; New; Infrastructure; Display

Visual Analytics studies how to help people make sense of complex data by providing interactive visual interfaces for data analysis and presentation. By making data visible and interactive, visual analytics enables scientists and researchers to detect patterns, formulate novel hypotheses and derive new insights on relevant scientific, social, and economic issues. Existing methods and technologies however focus exclusively on standard desktop interfaces and, as such, do not take advantage of the remarkable opportunities non-traditional display devices such as large screen displays, touch tables, and stereoscopic displays, can bring. In this project the research team explores these opportunities by developing methods for collaborative visual data analysis; real-time data monitoring in mission-critical settings; and immersive virtual navigation of "data spaces".The research the PI carries out includes: (1) user studies to understand how people solve data analysis problems in collaborative, data monitoring and immersive settings; (2) development of novel interaction techniques and applications for touch and 3D interfaces; (3) advanced evaluation methods that take advantage of rich user experience sensors such as EEG and eye-tracking devices. Research in these three directions will expand the horizon of visual analytics and increase our knowledge of how it can help people extract useful and actionable knowledge out of complex and large data sets.

可视化分析研究如何通过为数据分析和表示提供交互式可视化界面来帮助人们理解复杂的数据。通过使数据可见和互动，可视化分析使科学家和研究人员能够发现模式，制定新的假设，并对相关的科学，社会和经济问题得出新的见解。然而，现有的方法和技术只关注标准的桌面界面，因此，没有利用非传统显示设备（如大屏幕显示器、触摸桌和立体显示器）带来的巨大机遇。在这个项目中，研究团队通过开发协作可视化数据分析方法来探索这些机会；关键任务设置中的实时数据监控；以及“数据空间”的沉浸式虚拟导航。PI开展的研究包括：(1)用户研究，了解人们如何在协作、数据监测和沉浸式设置中解决数据分析问题；(2)开发触摸和3D界面的新型交互技术和应用；(3)利用脑电图、眼动追踪等丰富用户体验传感器的先进评价方法。这三个方向的研究将扩展可视化分析的视野，并增加我们对可视化分析如何帮助人们从复杂和大型数据集中提取有用和可操作知识的认识。

项目成果

Interactive Data Visualization in Jupyter Notebooks

Jupyter Notebooks 中的交互式数据可视化

• DOI: 10.1109/mcse.2021.3052619
• 发表时间: 2021
• 期刊: Computing in Science & Engineering
• 影响因子: 2.1
• 作者: Piazentin Ono, Jorge;Freire, Juliana;Silva, Claudio T.
• 通讯作者: Silva, Claudio T.

Urban Mosaic: Visual Exploration of Streetscapes Using Large-Scale Image Data

• DOI: 10.1145/3313831.3376399
• 发表时间: 2020-04
• 期刊: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
• 作者: Fábio Miranda;Maryam Hosseini;Marcos Lage;Harish Doraiswamy;G. Dove;Cláudio T. Silva

OpenSpace: A System for Astrographics

• DOI: 10.1109/tvcg.2019.2934259
• 发表时间: 2020-01
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: A. Bock;A. Ynnerman;Emil Axelsson;J. Costa;Gene Payne;M. Acinapura;V. Trakinski;C. Emmart

mTSeer: Interactive Visual Exploration of Models on Multivariate Time-series Forecast

• DOI: 10.1145/3411764.3445083
• 发表时间: 2021-05
• 期刊: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
• 作者: Ke Xu;Jun Yuan;Yifang Wang;Cláudio T. Silva;E. Bertini

HistoryTracker: Minimizing Human Interactions in Baseball Game Annotation

HistoryTracker：最大限度地减少棒球比赛注释中的人机交互

• DOI: 10.1145/3290605.3300293
• 发表时间: 2019
• 期刊: CHI '19 Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
• 作者: One, Jorge Piazentin;Gjoka, Arvi;Salamon, Justin;Dietrich, Carlos;Silva, Claudio T.
• 通讯作者: Silva, Claudio T.