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Article: Automated criminal link analysis based on domain knowledge

TitleAutomated criminal link analysis based on domain knowledge
Authors
Issue Date2007
PublisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.asis.org/Publications/JASIS/jasis.html
Citation
Journal Of The American Society For Information Science And Technology, 2007, v. 58 n. 6, p. 842-855 How to Cite?
AbstractLink (association) analysis has been used in the criminal justice domain to search large datasets for associations between crime entities in order to facilitate crime investigations. However, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges, this article proposes several techniques for automated, effective, and efficient link analysis. These techniques include the co-occurrence analysis, the shortest path algorithm, and a heuristic approach to identifying associations and determining their importance. We developed a prototype system called CrimeLink Explorer based on the proposed techniques. Results of a user study with 10 crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently than traditional single-level link analysis tools. Moreover, subjects believed that association paths found based on the heuristic approach were more accurate than those found based solely on the co-occurrence analysis and that the automated link analysis system would be of great help in crime investigations.
Persistent Identifierhttp://hdl.handle.net/10722/85990
ISSN
2015 Impact Factor: 2.452
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorSchroeder, Jen_HK
dc.contributor.authorXu, Jen_HK
dc.contributor.authorChen, Hen_HK
dc.contributor.authorChau, Men_HK
dc.date.accessioned2010-09-06T09:11:33Z-
dc.date.available2010-09-06T09:11:33Z-
dc.date.issued2007en_HK
dc.identifier.citationJournal Of The American Society For Information Science And Technology, 2007, v. 58 n. 6, p. 842-855en_HK
dc.identifier.issn1532-2882en_HK
dc.identifier.urihttp://hdl.handle.net/10722/85990-
dc.description.abstractLink (association) analysis has been used in the criminal justice domain to search large datasets for associations between crime entities in order to facilitate crime investigations. However, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges, this article proposes several techniques for automated, effective, and efficient link analysis. These techniques include the co-occurrence analysis, the shortest path algorithm, and a heuristic approach to identifying associations and determining their importance. We developed a prototype system called CrimeLink Explorer based on the proposed techniques. Results of a user study with 10 crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently than traditional single-level link analysis tools. Moreover, subjects believed that association paths found based on the heuristic approach were more accurate than those found based solely on the co-occurrence analysis and that the automated link analysis system would be of great help in crime investigations.en_HK
dc.languageengen_HK
dc.publisherJohn Wiley & Sons, Inc. The Journal's web site is located at http://www.asis.org/Publications/JASIS/jasis.htmlen_HK
dc.relation.ispartofJournal of the American Society for Information Science and Technologyen_HK
dc.rightsJournal of the American Society for Information Science and Technology. Copyright © John Wiley & Sons, Inc.en_HK
dc.titleAutomated criminal link analysis based on domain knowledgeen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1532-2882&volume=58&issue=6&spage=842&epage=855&date=2007&atitle=Automated+Criminal+Link+Analysis+Based+on+Domain+Knowledgeen_HK
dc.identifier.emailChau, M: mchau@hkucc.hku.hken_HK
dc.identifier.authorityChau, M=rp01051en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1002/asi.20552en_HK
dc.identifier.scopuseid_2-s2.0-34247141976en_HK
dc.identifier.hkuros137552en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34247141976&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume58en_HK
dc.identifier.issue6en_HK
dc.identifier.spage842en_HK
dc.identifier.epage855en_HK
dc.identifier.isiWOS:000246379800007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridSchroeder, J=7403194269en_HK
dc.identifier.scopusauthoridXu, J=36006847900en_HK
dc.identifier.scopusauthoridChen, H=35213102500en_HK
dc.identifier.scopusauthoridChau, M=7006073763en_HK

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