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Mining social networks and security informatics /

Crime, terrorism and security are in the forefront of current societal concerns. This edited volume presents research based on social network techniques showing how data from crime and terror networks can be analyzed and how information can be extracted. The topics covered include crime data mining...

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Bibliographic Details
Corporate Authors: SpringerLink (Online service)
Group Author: Özyer, Tansel; Erdem, Zeki; Rokne, J. (Jon), 1941-; Khoury, Suheil
Published: Springer,
Publisher Address: Dordrecht :
Publication Dates: [2013].
Literature type: eBook
Language: English
Series: Lecture notes in social networks,
Subjects:
Data mining.
Computer security.
Computer Communication Networks.
Social Support.
Online social networks.
Computer networks > Security measures.
Electronic books.
Online Access: http://dx.doi.org/10.1007/978-94-007-6359-3
Summary: Crime, terrorism and security are in the forefront of current societal concerns. This edited volume presents research based on social network techniques showing how data from crime and terror networks can be analyzed and how information can be extracted. The topics covered include crime data mining and visualization; organized crime detection; crime network visualization; computational criminology; aspects of terror network analyses and threat prediction including cyberterrorism and the related area of dark web; privacy issues in social networks; security informatics; graph algorithms for soci
Carrier Form: 1 online resource (vi, 283 pages) : illustrations
Bibliography: Includes biblioigraphical references.
ISBN: 9789400763593 (electronic book)
940076359X (electronic book)
Index Number: QA76
CLC: TP393.08
Contents: Mining Social Networks and Security Informatics; Contents; A Model for Dynamic Integration of Data Sources; 1 Introduction; 1.1 What Is Data Integration?; 1.2 Is Data Integration a Hard Problem?; 2 Data Sources; 2.1 What Is Data Source?; 2.2 Data Source Types; 2.3 Data Quality and Completeness; 3 Dynamic Integration of Data Sources; 3.1 Data Structure Matching; 3.2 Unstructured Data Categorization; 3.3 Unstructured Data Feature Extraction; 3.4 Unstructured Data Matching; 3.5 Ontology; 3.6 Data Matching; 3.7 Metadata; 3.8 Data Fusion and Sharing; 4 A Sample Case; 5 Conclusions and Future Work

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