Immersive analytics in virtual reality

虚拟现实中的沉浸式分析

• 批准号: 513293-2017
• 负责人: Coady, Yvonne
• 金额: $ 1.46万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=618864
• 关键词: Immersive; analytics; virtual; reality

Immersive analytics combines data science, scientific visualization techniques, 3D interfaces, and mixed reality environments to support improved analytical reasoning and decision making when exploring complex datasets. Some examples of customized, high-end, blended reality environments include: CAVE2 at UIC, YURT at Brown, Reality Deck at SUNY, Augmentarium at University of Maryland, HIVE at Duke, the five-sided La Cueva Grande at LANL, AlloSphere at UCSB, GigaPixel Display Laboratory and Visionarium at Virginia Tech.In this project we will explore representative complex datasets with newly emerging, commodity-priced technologies such as Mixed Reality (MR), 3D input devices, and Natural Language Processing (NLP). Customer behaviour patterns, product analysis telemetry, and resource utilization in a data center are common themes in Canadian industry that could benefit from these results. Specifically, we plan to address open questions in an industry relevant problem at the intersection of systems, analytics, and information visualization by considering deployment strategies for containers in a data center. The three key questions this research will address are:1) What interaction mechanisms are most effective at promoting engagement?2) What are the most seamless ways of elucidating theinter-relationship of disparate data sets?3) What are the best models for group interactions in a virtual environment?

沉浸式分析结合了数据科学、科学可视化技术、3D界面和混合现实环境，在探索复杂数据集时支持改进的分析推理和决策制定。定制、高端、混合现实环境的一些例子包括：加州大学欧文分校的CAVE2、布朗大学的YURT、纽约州立大学的Reality Deck、马里兰大学的Augmentarium、杜克大学的HIVE、加州大学洛杉矶分校的五面La Cueva Grande、加州大学洛杉矶分校的AlloSphere、吉像素显示实验室和弗吉尼亚理工大学的Visionarium。在这个项目中，我们将探索具有代表性的复杂数据集，这些数据集采用了新兴的、商品价格的技术，如混合现实（MR）、3D输入设备和自然语言处理（NLP）。客户行为模式、产品分析遥测和数据中心的资源利用是加拿大工业界可以从这些结果中受益的常见主题。具体来说，我们计划通过考虑数据中心容器的部署策略来解决系统、分析和信息可视化交叉领域的行业相关问题中的开放性问题。该研究将解决的三个关键问题是：1)哪种互动机制最有效地促进用户粘性？2)阐明不同数据集之间相互关系的最无缝的方法是什么？3)虚拟环境中群体互动的最佳模式是什么？