Gray Matter: A Platform to Provide Timely, Relevant Information for Proactive Decision Making
Navy SBIR FY2014.1

Sol No.: Navy SBIR FY2014.1
Topic No.: N141-071
Topic Title: Gray Matter: A Platform to Provide Timely, Relevant Information for Proactive Decision Making
Proposal No.: N141-071-0250
Firm: Charles River Analytics Inc.
625 Mount Auburn Street
Cambridge, Massachusetts 02138-4555
Contact: Brad Rosenberg
Phone: (617) 491-3474
Web Site:
Abstract: Military domains, such as naval operations, require agile decision-making by Warfighters as they monitor ongoing activities, assess the impact of events, and develop or adjust plans to achieve the overall mission. These missions and resulting tasks emerge from a consistently evolving battlespace, placing demands on the Warfighter to adapt to make sense of the wealth of available battlespace data, often in the form of text-based reports and message traffic. However, current information management tools available to Warfighters are limited to address only a prescribed set of tasks and only then in narrow contexts. Warfighters need dynamic information management tools that enable them to categorize, enrich, reason over, and manage information transactions to provide timely, relevant information for decision-making. To meet this goal, we propose to design and demonstrate the feasibility of Gray Matter, a platform for Warfighters to rapidly capture, reuse, and share workflows that can be discovered and applied based on anticipated information needs. Gray Matter provides a mashup authoring and execution capability that combines data access, information processing, communication, and visualization services, and the capacity to anticipate mashups that are contextually-relevant to the Warfighter.
Benefits: The research performed under this effort will have immediate and tangible benefit for a number of military Command and Control (C2) and intelligence, surveillance, and reconnaissance (ISR) systems, such as the Global Command and Control System (GCCS) and Distributed Common Ground System (DCGS) family of systems. This research will also have direct application to enhance our commercial AgentWorksT product to provide hybrid, computational processing of text-based information.