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Intelligent Framework for Crime Analysis: A PhD Research Plan by Fabrizio Albertetti

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This research plan outlines an intelligent, process-driven knowledge extraction framework aimed at enhancing crime analysis. Directed by Prof. Kilian Stoffel at the University of Neuchatel, the project addresses new challenges in understanding criminal behavior through systematic study and data integration. Key objectives include handling uncertainty in forensic data, conducting analyses with partial knowledge, and combining multiple data sources for effective crime prevention and evaluation. Supported by the Swiss National Science Foundation, the project spans five years, starting in September 2011.

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Intelligent Framework for Crime Analysis: A PhD Research Plan by Fabrizio Albertetti

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  1. An Intelligent Process-driven Knowledge Extraction Framework for Crime Analysis PhD Thesis – Research Plan ALBERTETTI Fabrizio ThesisDirector: Prof. STOFFEL Kilian Information Management Institute University of Neuchatel Switzerland New Challenges in the European Area Young Scientist's 1st International Baku Forum May 20-25

  2. Agenda

  3. Interdisciplinaryproject: • Computational • Information Management Institute, University of Neuchatel • Forensics • Institut de Police Scientifique, University of Lausanne • Supported by the Swiss National Science Foundation(SNSF) • 5 yearsproject (?) – Started in Sept. 2011 Project context

  4. How do criminals think?Is crime rational?

  5. E.g., the routine activityapproach (Cohen & Felson, 1979) Figure: Routine Activity (popcenter.org) The Rationality of Crime

  6. "Crime analysis is the systematic study of crime and disorder problems as well as other police-related issues—including sociodemographic, spatial, and temporal factors—to assist the police in criminal apprehension, crime and disorder reduction, crime prevention, and evaluation." (Boba, 2005) Crime Analysis

  7. "Crime analysis is the systematic study of crime and disorder problems as well as other police-related issues—including sociodemographic, spatial, and temporal factors—to assist the police in criminal apprehension, crime and disorder reduction, crime prevention, and evaluation." (Boba, 2005) Crime Analysis

  8. The chainof events in crime prevention: From patterns to prevention(Ratcliffe, 2009) Computational Forensics !  Discovering Forensic Knowledge How can we prevent crime?

  9. To develop a framework : Objectives

  10. What is the nature of forensic data? • Uncertain • Incomplete • Inaccurate • Why? • Because it is based on hypotheses and conjectures • Because it stems mainly from latent marks • Becauseit reflects the effects and not the causes (abduction) Key Questions

  11. Challenges: • To conduct analyses and perform deduction/reasoning with partial knowledge, uncertainties and conjectures • To integratedomain intelligence for providingpractical and consistent results • To conduct analyses with a holistic view of the macro process, i.e. combining several mining outcomes based on crime analysis processes Key Questions

  12. Domain-Driven Data Mining KnowledgeRepresentation Computational Forensic Framework Fuzzy Logic Forensic Science Key Questions – Research Domains

  13. Computational forensics is still an emerging research area • Only a combination of several domains can answer crime analysis questions Conclusions

  14. Thank you An Intelligent Process-driven Knowledge Extraction Framework for Crime Analysis * PhD Thesis – Research Plan ALBERTETTI Fabrizio ThesisDirector: Prof. STOFFEL Kilian Information Management Institute University of Neuchatel Switzerland * This projectissupported by the Swiss National Science Foundation

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