Title: Homeland situation awareness through mining and fusing heterogeneous information from intelligence databases and field sensors
Authors: Giusj Digioia; Stefano Panzieri
Addresses: Computer and Automation Department, Roma Tre University, Via della Vasca Navale 79, 00146, Rome, Italy; Engineering Ingegneria Informatica SpA, Defense and Security Department, Via Riccardo Morandi 32, 00148 Ponte Galeria, Rome ' Computer and Automation Department, Roma Tre University, Via della Vasca Navale 79, 00146, Rome, Italy
Abstract: 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.
International Journal of System of Systems Engineering, 2013 Vol.4 No.3/4, pp.190 - 210
Received: 23 May 2013
Accepted: 03 Jul 2013
Published online: 28 Apr 2014 *