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A Scalable Streaming Framework for Video-Driven Collaborative Visualization Environments /

Abstract

This dissertation addresses a growing challenge of visual analytics for data-driven science by presenting new scalable visualization algorithms based on scalable streaming and video-driven approaches. Drawing data from modeling, simulation, sensing and data fusion to deliver a starting point for scientific discovery, we present a collaborative visual analytics framework providing the abilities to capture, compress, transport, decode, synchronize, display and interact with data at a massive scale on high resolution collaborative display environments. This framework allows scientists to connect to data when it is needed, where it is need, and in a format suitable for scientific discovery while providing a means to interactively define the processing and filtering parameters that guide visual interrogation. The presented framework uses low-latency video streaming and distributed video playback to display content on tiled display walls from a variety of sources and at very high resolutions. These techniques convert desktop-bound applications into low-bandwidth video streams, allowing them to be transmitted to and displayed on tiled display walls at multiple local and remote sites. This allows scientists from around the world to collaborate in real-time, using the tools and applications they are familiar with from desktops, wireless laptops, and tablets while leveraging high-resolution display walls to organize and compare all of this visual information. Scalable display methods are presented that use distributed decoding and rendering to display multiple high-resolution data streams simultaneously on tiled display walls. This is applied to live collaborative video streams, to cinema quality 4K resolution video, to stereoscopic telepresence, to extremely high resolution video, and to large particle datasets from molecular dynamics simulations. The combination of this scalable display method with the bandwidth reduction achieved by using compressed streams allows for the display and analysis of data at scales an order of magnitude larger than what were possible with previous techniques

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