Immersive analytics in virtual reality

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，以支持在探索复杂数据集时改进的分析推理和决策。一些定制的高端混合现实环境的例子包括：UIC的CAVE2、Brown的蒙古包、纽约州立大学的Reality Deck、马里兰大学的Augmentum、杜克大学的HIVE、LANL的五面La Cueva Grande、UCSB的AlloSphere、弗吉尼亚理工大学的Gigapixel Display实验室和Visionery。在这个项目中，我们将探索具有代表性的复杂数据集，使用新出现的商品价格技术，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行业中可能受益于这些结果的常见主题。具体地说，我们计划通过考虑数据中心中容器的部署策略来解决系统、分析和信息可视化交集的行业相关问题中的未决问题。这项研究将解决的三个关键问题是：1)什么交互机制在促进参与度方面最有效？2)解释不同数据集之间相互关系的最无缝方式是什么？3)虚拟环境中群体交互的最佳模型是什么？

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?