DocumentCode :
2792819
Title :
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
Link To Document :
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