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2013 (Vol. 6, Issue: 19)
Article Information:

Proposing a Framework for Exploration of Crime Data Using Web Structure and Content Mining

Amin Shahraki Moghaddam, Javad Hosseinkhani, Suriayati Chuprat, Hamed Taherdoost and Hadi Barani Baravati
Corresponding Author:  Amin Shahraki Moghaddam 

Key words:  Crime web mining, criminal network, forensics analysis, framework, social network, terrorist network, universal crawler
Vol. 6 , (19): 3617-3624
Submitted Accepted Published
January 02, 2013 February 18, 2013 October 20, 2013
Abstract:

The purpose of this study is to propose a framework and implement High-level architecture of a scalable universal crawler to maintenance the reliability gap and present the evaluation process of forensic data analysis criminal suspects. In Law enforcement agencies, criminal web data provide appropriate and anonymous information. Pieces of information implemented the digital data in the forensic analysis to accused social networks but the assessment of these information pieces is so difficult. In fact, the operator manually should pull out the suitable information from the text in the website and find the links and classify them into a database structure. In consequent, the set is ready to implement a various criminal network evaluation tools for testing. As a result, this procedure is not efficient because it has many errors and the quality of obtaining the analyzed data is based on the expertise and experience of the investigator subsequently the reliability of the tests is not constant. Therefore, the better result just comes from the knowledgeable operator. The objectives of this study is to show the process of investigating the criminal suspects of forensic data analysis to maintenance the reliability gap by proposing a structure and applying High-level architecture of a scalable universal crawler.
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  Cite this Reference:
Amin Shahraki Moghaddam, Javad Hosseinkhani, Suriayati Chuprat, Hamed Taherdoost and Hadi Barani Baravati, 2013. Proposing a Framework for Exploration of Crime Data Using Web Structure and Content Mining.  Research Journal of Applied Sciences, Engineering and Technology, 6(19): 3617-3624.
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ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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