No abstract

Data available for intelligence analysis is often incomplete, ambiguous and voluminous. Also, the data may be unorganized, the details overwhelming, and considerable analysis may be required to uncover adversarial activities. This paper describes a simulation-based approach that helps analysts understand data and use it to predict future events and possible scenarios. In particular, the approach enables intelligence analysts to find, display and understand data relationships by connecting the dots of data to create network of information. The approach also generates alternative storylines, allowing analysts to view other possible outcomes. It facilitates the automation of reasoning and the detection of inconsistent data, which provides more reliable information for analysis. A case study using data from the TV series, 24, demonstrates the feasibility of approach and its application to intelligence analysis of WMD attacks against the critical infrastructure.