Homeland situation awareness through mining and fusing heterogeneous information from intelligence databases and field sensors

One of the most felt issues in the defence domain is that of having huge quantities of data stored in databases and acquired from field sensors without being able to infer information from them. Usually, databases are continuously updated with observations, and are related to heterogeneous data. Deep and continuous analysis on the data could mine useful correlations, explain relations existing among data and cue searches for further evidences. The solution to the problem addressed before seems to deal both with the domain of data mining and with the domain of high level data fusion. The focus of this paper is the definition of an architecture for a system adopting data mining techniques to adaptively discover clusters of information and relation among them, to classify observations acquired and to use the model of knowledge and the classification derived in order to assess situations, threats and refine the search for evidences. Keywords: situation awareness; SAW; data mining; hidden Markov models; HMMs; agile modelling; intelligence databases; field sensors; data fusion; data clusters; information integration; classification; threats; defence industry; homeland security