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Angaben zur Quelle [Bearbeiten]

Autor     Jennifer J. Xu, Hsinchun Chen
Titel    CrimeNet Explorer: A Framework for Criminal Network Knowledge Discovery
Zeitschrift    ACM Transactions on Information Systems
Datum    April 2005
Nummer    23 (2)
Seiten    201–226
Anmerkung    to be found as a reference in Nm, but with a wrong year
DOI    10.1145/1059981.1059984
URL    http://dl.acm.org/citation.cfm?id=1059984; http://comminfo.rutgers.edu/~muresan/IR/Docs/Articles/toisXu2005.pdf

Literaturverz.   

yes
Fußnoten    yes
Fragmente    1


Fragmente der Quelle:
[1.] Nm/Fragment 198 03 - Diskussion
Zuletzt bearbeitet: 2012-05-01 08:31:45 Hindemith
Fragment, Gesichtet, Nm, SMWFragment, Schutzlevel sysop, Verschleierung, Xu and Chen 2005b

Typus
Verschleierung
Bearbeiter
Graf Isolan
Gesichtet
Yes.png
Untersuchte Arbeit:
Seite: 198, Zeilen: 3-18
Quelle: Xu and Chen 2005b
Seite(n): 201, Zeilen: 5-15
Knowledge about the structure and organization of terrorist networks is important for both terrorism investigation and the

development of effective strategies to prevent terrorist attacks. However, except for network visualization, terrorist network analysis remains primarily a manual process. Existing tools do not provide advanced structural analysis techniques that allow for the extraction of network knowledge from terrorist organizations data. To help law enforcement and intelligence agencies discover terrorist network knowledge efficiently and effectively, in this research we propose a framework for automated network analysis, visualization and destabilization.

Based upon this framework, this project developed a system called iMiner that incorporates several advanced techniques: subgroups detection approach, discovering efficiency of a network, social network analysis methods, destabilizing strategies for terrorist networks including detection of hidden hierarchy.

Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not provide advanced structural analysis techniques that allow extraction of network knowledge from large volumes of criminal-justice data. To help law enforcement and intelligence agencies discover criminal network knowledge efficiently and effectively, in this research we proposed a framework for automated network analysis and visualization. The framework included four stages: network creation, network partition, structural analysis, and network visualization. Based upon it, we have developed a system called CrimeNet Explorer that incorporates several advanced techniques: a concept space approach, hierarchical clustering, social network analysis methods, and multidimensional scaling.
Anmerkungen

This is just the introduction from the major paper of Xu and Chen (2005b), in which they present their "CrimeNet Explorer" system, slightly rewritten to fit terrorist networks instead of criminal networks. The source is not named here.

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(Graf Isolan), Bummelchen

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