Dissertation/Thesis Abstract

Scalable, situationally aware visual analytics and applications
by Eaglin, Todd, Ph.D., The University of North Carolina at Charlotte, 2017, 180; 10270103
Abstract (Summary)

There is a need to understand large and complex datasets to provide better situa- tional awareness in-order to make timely well-informed actionable decisions in critical environments. These types of environments include emergency evacuations for large buildings, indoor routing for buildings in emergency situations, large-scale critical infrastructure for disaster planning and first responders, LiDAR analysis for coastal planning in disaster situations, and social media data for health related analysis. I introduce novel work and applications in real-time interactive visual analytics in these domains. I also detail techniques, systems and tools across a range of disciplines from GPU computing for real-time analysis to machine learning for interactive analysis on mobile and web-based platforms.

Indexing (document details)
Advisor: Ribarsky, William
Commitee: Delmelle, Eric, Wang, Xiaoyu, Wartell, Zach
School: The University of North Carolina at Charlotte
Department: Computer Science
School Location: United States -- North Carolina
Source: DAI-B 78/09(E), Dissertation Abstracts International
Subjects: Computer science
Keywords: Disaster planning, Interactive visual analytics, Large datasets
Publication Number: 10270103
ISBN: 9781369707762
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy