Marshalling Evidence Through Data Mining in Support of Counter Terrorism

In this paper we present an architecture to manage a large, distributed volume of evidence for counterterrorism applications. This approach facilitates the intelligence analyst’s train of thought by enabling hypotheses to be postulated and evaluated against the available evidence. The hypothesis creation is left to the human (as we believe only humans possess the flexibility to adapt hypothesis to the dynamic nature of the problem), but the system automates the gathering and linking of evidence that supports or negates the hypothesis. We do this by extensive use of data mining techniques in support of the process of query answering. The approach is incorporated into a distributed agent system that allows users to discover and compose agents for hypothesis processing.