Database Issues for Intelligence Analysis

Abstract

The use of database systems in police investigative work is well established and similar technology will be required to support counter terrorism. A key feature of terrorism is that it is predominantly a group activity. This has important implications for analysing terrorism-related intelligence in that systems must help analysts uncover patterns of activity which may be obscured by being distributed over a network of individuals or organisations. In this paper we argue that the record-oriented representation of data on which most current database technology is based is not well-suited for such applications. Another form of representation, known as graph-based databases, organises information as a network of connected facts and is better suited to supporting queries concerned with finding associations and links between various entities such as people, events, organisations and so forth. We briefly discuss a database system that has been built on top of a graph-oriented representation of data in order to show that such systems are feasible. Furthermore such systems can support a broader range of queries, in particular the kinds of queries that are likely to be required in intelligence analysis.

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2004
Accession Number
ADA458112

Entities

People

  • Graham Lee
  • Robert Ayres
  • Ross D. Harris
  • Steve J. Smith

Organizations

  • Cranfield University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Weapons Technologies

DTIC Thesaurus Topics

  • Computer Programming
  • Crime
  • Criminals
  • Databases
  • Domain Specific Programming Languages
  • Information Systems
  • Intelligence Analysis
  • Language
  • Link Analysis
  • National Security
  • Programming Languages
  • Relational Database Management Systems
  • Relational Databases
  • Social Networks
  • Social Sciences
  • Standards
  • Terrorists

Fields of Study

  • Computer science

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Neural Network Machine Learning.
  • Systems Analysis and Design