This work presents the TENSOR (clusTEriNg terroriSm actiOn pRediction) framework, a near real-time reasoning framework for early identification and prediction of potential threat situations (e.g. terrorist actions). The framework consists of three different modules with the aim of collecting and processing information of the surrounding environment from a variety of sources including physical sensors (e.g. surveillance cameras) and humans (e.g. police officers). The main objective of TENSOR is to show how patterns of strategic terroristic behaviors, identified analyzing large longitudinal data sets, can be linked to short term activity patterns identified analyzing feeds by “usual” surveillance technologies and that this fusion allows a better detection of terrorist threats. This information is processed at different abstraction levels and, thru the proposed layered architecture, TENSOR simulates the three main expert user roles (i.e. operational, tactical and strategic user roles), as indicated in the intelligence analysis domain literature. TENSOR transforms all the sensors gathered data into symbolic events of interest following a generic scenario-agnostic semantics for terrorist attacks described in literature as terrorist indicators. Thru different reasoning and fusion techniques, the framework proactively detects threats and depicts the situation in near real-time.
Sormani, Raul, Francesco Archetti, and Ilaria Giordani. 2016. "Criticality Assessment of Terrorism Related Events as Different Time Scales." Journal of Ambient Intelligence and Humanized Computing (October): 1-19. http://link.springer.com/article/10.1007/s12652-016-0416-x