Abstract
The Georgia Tech Research Institute's goal in the Novel Intelligence in Massive Data (NIMD) Program, is to investigate certain aspects of an intelligence analyst's preferences and analytic strategies used in the process of discovering new knowledge. We are analyzing search strategies used by analysts in an attempt to understand their current task model. Based on this understanding, we will design and prototype a software tool that applies case-based reasoning in combination with other advanced reasoning techniques to help analysts perform knowledge discovery. The main objective of our work is the development, validation and incremental improvement of a set of knowledge discovery automation aids that significantly reduces the manual searches done by intelligence analysts and increases the quality and quantity of derived intelligence. We believe that these technical improvements will depend on our explicit understanding of the cognitive issues implied by the use of advanced reasoning techniques, integrated into a next generation NIMD prototype.
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