Intelligence Driven Intelligence Collection
As the number and type of sensors and associated platforms grows, the complexity of collection management skyrockets. Achieving the goal of developing more effective and efficient collection plans requires a more informed collection management information architecture. This architecture must not only optimize collection plans based on infor-mation requirements and priority, but it must leverage the data that already exist within the intelligence repositories. These data can be used in two ways. First, intelligence re-positories can be scanned to determine if information requirements can be satisfied using the data already collected instead of creating more collection requirements. Second, when collections are necessary, they can be done more effectively by leveraging the power of predictive analytics to anticipate where the targets will be. In this proposal, CCRi describes a three phase collection management architecture that will guide users to creating unambiguous, complete, and well structured collection re-quirements that are both machine interpretable as well as human readable. The system will seek opportunities to avoid collection using existing data, and then optimize collec-tions using state-of-the art optimization techniques in conjunction with predictive analytic outputs.
Small Business Information at Submission:
Director of Operations
Commonwealth Computer Research, Inc.
1422 Sachem Pl., Unit #1 Charlottesville, VA 22901
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