Strategies to Process Voluminous Data in Support of Counter-Terrorism

Author

Rajasekaran, S. ; Ammar, R. ; Demurjian, S. ; Greenshields, I. ; Abdel-Raouf, A. ; Doan, T. ; Lian, J. ; Song, M. ; Mohamed, A.

Author_Institution

Dept. of Comput. Sci. & Eng., Connecticut Univ., Storrs, CT

fYear

2005

fDate

5-12 March 2005

Firstpage

1

Lastpage

10

Abstract

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

Keywords

data analysis; data reduction; document handling; indexing; terrorism; counter-terrorism; data analysis; data clustering; data processing; data reduction; latent semantic indexing; polysemy; synonymy; Biographies; Computer science; Data engineering; Data security; Geometry; Indexing; Large scale integration; Performance analysis; Performance loss; XML;

fLanguage

English

Publisher

ieee

Conference_Titel

Aerospace Conference, 2005 IEEE

Conference_Location

Big Sky, MT

Print_ISBN

0-7803-8870-4

Type

conf

DOI

10.1109/AERO.2005.1559622

Filename

1559622