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Meta-Information Paper Nominated for Best Paper at ICCRTS 2007

Charles River Analytics received a nomination for Best Paper at the International Command and Control Research and Technology Symposium (ICCRTS) that took place from June 19-21, 2007, in Newport, Rhode Island. The paper, “Representing Meta-Information to Support C2 Decision Making” was nominated in Track 3, Modeling and Simulation.

Mike Farry, Scientist II at Charles River, described the conference: “The International Command and Control Research and Technology Symposium (ICCRTS) is sponsored by the Command and Control Research Program (CCRP) to bring together interested researchers to exchange ideas on command and control (C2), its measurement and assessment, and the impact of new technologies on the C2 process.”

The reference and abstract appear below.

Pfautz, J., Fouse, A., Farry, M., Bisantz, A., & Roth, E. (2007). “Representing Meta-Information to Support C2 Decision Making,” in Proceedings of the International Command and Control Symposium (ICCRTS ’07), Newport, Rhode Island (June).

Aggregating, assimilating, and understanding the ever-larger amounts of heterogeneous information present in network-centric environments presents distinct cognitive challenges to the command and control staff. Under previous efforts, we have detailed our efforts to analyze how qualifiers of information, or meta-information (e.g., uncertainty, recency, pedigree), impact information processing and situational awareness in an already challenging decision-making environment. To date, few existing systems explicitly support the management and representation of meta-information. Here, we describe several specific efforts to develop methods for the representation of meta-information in C2 decision-support tools, including methods to support asset allocation (e.g., for air-based ISR, for addressing ground-based threats, for neutralizing near-space or space-based threats). These methods include techniques for the visual portrayal of meta-information in C2 decision-making systems as well as approaches to the computation, when necessary, of that meta-information. In this paper, we discuss these methods within example domains, and discuss lessons learned for the design of future C2 decision-support systems.

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