ALARM for early warning: A Lightweight Analysis for Recognition of Menace

Information overflow is a significant and as yet unresolved problem for military, homeland security and law enforcement. Furthermore, quantity is only one part of the intelligence problem: identifying which pieces of information belong together, and assessing the credibility of not only individual pieces of information but also of their correlations to one another complicates things. Automatically sifting, sorting and fusing information garnered from multiple sources into recognizable patterns of behavior and potential threats would provide a distinct operational advantage. This advantage would be clearly increased if the time needed for processing was close to real-time. A number of systems for deeper analysis of potential threats using technologies such as Bayesian networks exist, but tend to be time-intensive. This paper describes a near realtime solution for first-pass processing of inflowing information to provide early warning of developing threats.