Using importance flooding to identify interesting networks of criminal activity

Byron Marshall, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In spite of policy concerns and high costs, the law enforcement community is investing heavily in data sharing initiatives. Cross-jurisdictional criminal justice information (e.g., open warrants and convictions) is important, but different data sets are needed for investigational activities where requirements are not as clear and policy concerns abound. The community needs sharing models that employ obtainable data sets and support real-world investigational tasks. This work presents a methodology for sharing and analyzing investigation-relevant data. Our importance flooding application extracts interesting networks of relationships from large law enforcement data sets using user-controlled investigation heuristics and spreading activation. Our technique implements path-based interestingness rules to help identify promising associations to support creation of investigational link charts. In our experiments, the importance flooding approach outperformed relationship-weight-only models in matching expert-selected associations. This methodology is potentially useful for large cross-jurisdictional data sets and investigations.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages14-25
Number of pages12
Volume3975 LNCS
StatePublished - 2006
EventIEEE International Conference on Intelligence and Security Informatics, ISI 2006 - San Diego, CA, United States
Duration: May 23 2006May 24 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3975 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

OtherIEEE International Conference on Intelligence and Security Informatics, ISI 2006
CountryUnited States
CitySan Diego, CA
Period5/23/065/24/06

Fingerprint

Flooding
Law enforcement
Law Enforcement
Chemical activation
Sharing
Criminal Law
Information Dissemination
Data Sharing
Methodology
Costs
Chart
Experiments
Activation
Weights and Measures
Costs and Cost Analysis
Datasets
Heuristics
Path
Requirements
Model

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Marshall, B., & Chen, H. (2006). Using importance flooding to identify interesting networks of criminal activity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3975 LNCS, pp. 14-25). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3975 LNCS).

Using importance flooding to identify interesting networks of criminal activity. / Marshall, Byron; Chen, Hsinchun.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3975 LNCS 2006. p. 14-25 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3975 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Marshall, B & Chen, H 2006, Using importance flooding to identify interesting networks of criminal activity. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 3975 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3975 LNCS, pp. 14-25, IEEE International Conference on Intelligence and Security Informatics, ISI 2006, San Diego, CA, United States, 5/23/06.
Marshall B, Chen H. Using importance flooding to identify interesting networks of criminal activity. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3975 LNCS. 2006. p. 14-25. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Marshall, Byron ; Chen, Hsinchun. / Using importance flooding to identify interesting networks of criminal activity. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 3975 LNCS 2006. pp. 14-25 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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