CrimeLink Explorer: Using domain knowledge to facilitate automated crime association analysis

Jennifer Schroeder, Jennifer Xu, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingChapter

16 Scopus citations

Abstract

Link (association) analysis has been used in law enforcement and intelligence domains to extract and search associations between people from large datasets. Nonetheless, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges and enable crime investigators to conduct automated, effective, and efficient link analysis, we proposed three techniques which include: the concept space approach, a shortest-path algorithm, and a heuristic approach that captures domain knowledge for determining importance of associations. We implemented a system called CrimeLink Explorer based on the proposed techniques. Results from our user study involving ten crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently. Additionally, subjects concluded that association paths found based on the heuristic approach were more accurate than those found based on the concept space approach.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsHsinchun Chen, Daniel D. Zeng, Therani Madhusudan, Richard Miranda, Jenny Schroeder, Chris Demchak
PublisherSpringer-Verlag
Pages168-180
Number of pages13
ISBN (Print)354040189X, 9783540401896
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2665
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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