EAGER: Large-Scale Real-Time Information Visualization on Immersive Platforms

University Of California-Santa Barbara, Santa Barbara CA

Investigators

Abstract

This proposal breaks new ground in the rapidly evolving field of Immersive Analytics. Virtual and Augmented Reality technologies are uniquely suited to enable large-scale data analysis that provides global overviews of data landscapes as well as fine-grain control and navigation. The proposed work will implement a scalable development environment for immersive analytics experiences, and evaluate different methods for authoring and collaboratively exploring very large datasets using Virtual Reality headsets, display walls, and curved immersive displays. The proposed work will pave the way for making immersive large-scale data exploration more accessible to large audiences, opening up possibilities of letting home virtual reality users participate in novel learning experiences. The PI will showcase the resulting immersive visualizations at regular outreach events to attract underrepresented minorities to novel human-computer interaction, and thus STEM research. Effective information visualization needs to be able to process and display large amounts of data in real-time to enable interactive analysis. The PI will build direct graphics processing unit (GPU) support into a modern information visualization library design, targeting one to two orders of magnitude acceleration in the rendering of large numbers of graphical elements, compared to current web-based visualization libraries. This computational scalability will enable interactive exploration of very large datasets, either displayed using a single visualization paradigm (e.g. node-link network) or multiple paradigms (e.g., map, network, sensor data, time series, etc.). Immersive information environments have the potential to support more effective capabilities for collaborative analysis than is possible with desktop or web-based platforms by placing groups of stakeholders in the middle of large-scale data visualizations to relate to others' first-person perspectives and interactively and jointly navigate through information spaces and control and highlight different aspects. The PI will design interactive collaborative data navigation and control tools that focus on this potential.