Strategies to Process Voluminous Data in Support of Counter-Terrorism

In this paper we present a survey of techniques and strategies that can be utilized to process high-volumes of data in support of counter-terrorism. Data reduction is a critical problem for counter-terrorism; there are large collections of documents that must be analyzed and processed, raising issues related to performance, lossless reduction, polysemy (i.e., the meaning of individual words being influenced by their surrounding words), and synonymy (i.e., the possibility of the same term being described in different ways). Our main objective in this paper is to provide a survey of data reduction strategies, ranging from data clustering to learning to latent semantic indexing