Nowhere is the need to understand large heterogeneous datasets more important than in disaster monitoring
and emergency response, where critical decisions have to be made in a timely fashion and the discovery of
important events requires an understanding of a collection of complex simulations. To gain enough insights
for actionable knowledge, the development of models and analysis of modeling results usually requires that
models be run many times so that all possibilities can be covered. Central to the goal of our research is,
therefore, the use of ensemble visualization of a large scale simulation space to appropriately aid decision makers
in reasoning about infrastructure behaviors and vulnerabilities in support of critical infrastructure analysis. This
requires the bringing together of computing-driven simulation results with the human decision-making process
via interactive visual analysis. We have developed a general critical infrastructure simulation and analysis
system for situationally aware emergency response during natural disasters. Our system demonstrates a scalable
visual analytics infrastructure with mobile interface for analysis, visualization and interaction with large-scale
simulation results in order to better understand their inherent structure and predictive capabilities. To generalize
the mobile aspect, we introduce mobility as a design consideration for the system. The utility and efficacy of
this research has been evaluated by domain practitioners and disaster response managers.
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.
KEYWORDS: Analytical research, Modeling and simulation, Geographic information systems, Networks, Systems modeling, Information security, Data modeling, Telecommunications, Control systems, Network security
Protecting critical infrastructure systems, such as electrical power grids, has become a primary concern for many
governments and organizations across a variety of stakeholder perspectives. Critical infrastructures involve multidimensional,
highly complex collections of technologies, processes, and people, and as such, are vulnerable to
potentially catastrophic failures on many levels. Moreover, cross-infrastructure dependencies can give rise to cascading
effects with escalating impact across multiple infrastructures. Critical infrastructure protection involves both
safeguarding against potential disaster scenarios and effective response in the aftermath of infrastructure failure. Our
research is developing innovative approaches to modeling critical infrastructures in order to support decision-making
during reconstitution efforts in response to infrastructure disruptions. By modeling the impact of infrastructure elements,
both within and across infrastructures, we can recommend focus areas for reconstitution resources across different
stakeholders in the context of their current goals. An interactive geovisualization interface provides a natural context for
this infrastructure analysis support. This paper presents an overview of our approach and the GIS modeling environment
under development for decision support in critical infrastructure reconstitution.
Bei Tseng Chu, William Tolone, Robert Wilhelm, M. Hegedus, J. Fesko, T. Finin, Yun Peng, Chris Jones, Junshen Long, Mike Matthews, J. Mayfield, J. Shimp, S. Su
Recent developments have made it possible to interoperate complex business applications at much lower costs. Application interoperation, along with business process re- engineering can result in significant savings by eliminating work created by disconnected business processes due to isolated business applications. However, we believe much greater productivity benefits can be achieved by facilitating timely decision-making, utilizing information from multiple enterprise perspectives. The CIIMPLEX enterprise integration architecture is designed to enable such productivity gains by helping people to carry out integrated enterprise scenarios. An enterprise scenario is triggered typically by some external event. The goal of an enterprise scenario is to make the right decisions considering the full context of the problem. Enterprise scenarios are difficult for people to carry out because of the interdependencies among various actions. One can easily be overwhelmed by the large amount of information. We propose the use of software agents to help gathering relevant information and present them in the appropriate context of an enterprise scenario. The CIIMPLEX enterprise integration architecture is based on the FAIME methodology for application interoperation and plug-and-play. It also explores the use of software agents in application plug-and- play.
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