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Proceedings Paper

Towards sustainable infrastructure management: knowledge-based service-oriented computing framework for visual analytics
Author(s): Rashna Vatcha; Seok-Won Lee; Ajeet Murty; William Tolone; Xiaoyu Wang; Wenwen Dou; Remco Chang; William Ribarsky; Wanqiu Liu; Shen-en Chen; Edd Hauser
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Paper Abstract

Infrastructure management (and its associated processes) is complex to understand, perform and thus, hard to make efficient and effective informed decisions. The management involves a multi-faceted operation that requires the most robust data fusion, visualization and decision making. In order to protect and build sustainable critical assets, we present our on-going multi-disciplinary large-scale project that establishes the Integrated Remote Sensing and Visualization (IRSV) system with a focus on supporting bridge structure inspection and management. This project involves specific expertise from civil engineers, computer scientists, geographers, and real-world practitioners from industry, local and federal government agencies. IRSV is being designed to accommodate the essential needs from the following aspects: 1) Better understanding and enforcement of complex inspection process that can bridge the gap between evidence gathering and decision making through the implementation of ontological knowledge engineering system; 2) Aggregation, representation and fusion of complex multi-layered heterogeneous data (i.e. infrared imaging, aerial photos and ground-mounted LIDAR etc.) with domain application knowledge to support machine understandable recommendation system; 3) Robust visualization techniques with large-scale analytical and interactive visualizations that support users' decision making; and 4) Integration of these needs through the flexible Service-oriented Architecture (SOA) framework to compose and provide services on-demand. IRSV is expected to serve as a management and data visualization tool for construction deliverable assurance and infrastructure monitoring both periodically (annually, monthly, even daily if needed) as well as after extreme events.

Paper Details

Date Published: 30 April 2009
PDF: 10 pages
Proc. SPIE 7346, Visual Analytics for Homeland Defense and Security, 734604 (30 April 2009); doi: 10.1117/12.819159
Show Author Affiliations
Rashna Vatcha, Univ. of North Carolina at Charlotte (United States)
Seok-Won Lee, Univ. of North Carolina at Charlotte (United States)
Ajeet Murty, Univ. of North Carolina at Charlotte (United States)
William Tolone, Univ. of North Carolina at Charlotte (United States)
Xiaoyu Wang, Univ. of North Carolina at Charlotte (United States)
Wenwen Dou, Univ. of North Carolina at Charlotte (United States)
Remco Chang, Univ. of North Carolina at Charlotte (United States)
William Ribarsky, Univ. of North Carolina at Charlotte (United States)
Wanqiu Liu, Univ. of North Carolina at Charlotte (United States)
Shen-en Chen, Univ. of North Carolina at Charlotte (United States)
Edd Hauser, Univ. of North Carolina at Charlotte (United States)


Published in SPIE Proceedings Vol. 7346:
Visual Analytics for Homeland Defense and Security
William J. Tolone; William Ribarsky, Editor(s)

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