An Intelligent Process-driven Knowledge Extraction Framework for Crime

Analysis

PhD Thesis – Research PlanALBERTETTI Fabrizio

Thesis Director: Prof. STOFFEL KilianInformation Management Institute

University of NeuchatelSwitzerland

New Challenges in the European AreaYoung Scientist's 1st International Baku ForumMay 20-25

2

Context Objectives Research Challenges

Agenda

3Project context

» Interdisciplinary project:˃ Computational

+ Information Management Institute, University of Neuchatel

˃ Forensics+ Institut de Police Scientifique, University of Lausanne

» Supported by the Swiss National Science Foundation (SNSF)

» 5 years project (?) – Started in Sept. 2011

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How do criminals think?Is crime rational?

5The Rationality of Crime

» E.g., the routine activity approach (Cohen & Felson, 1979)

Figure: Routine Activity (popcenter.org)

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"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

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"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

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» The chain of events in crime prevention:

Prevention Proactivity Predictability Patterns

ComputationalForensics !

Discovering Forensic

Knowledge

How can we prevent crime?

From patterns to prevention (Ratcliffe, 2009)

9Objectives

» To develop a framework :

• For conducting analyses

• Driven by processes (using domain knowledge)

• Intelligent (assessing the results)

• Extracting knowledge from forensic data

10Key Questions

» 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˃ Because it reflects the effects and not the causes

(abduction)

11Key Questions

Challenges:» To conduct analyses and perform

deduction/reasoning with partial knowledge, uncertainties and conjectures

» To integrate domain intelligence for providing practical 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

DOMAIN-DRIVEN

DATA MINING

FORENSICSCIENCE

KNOWLEDGE REPRESENTATION

FUZZYLOGIC

COMPUTATIONAL

FORENSICFRAMEWORK

12Key Questions – Research Domains

13Conclusions

» Computational forensics is still an emerging research area

» Only a combination of several domains can answer crime analysis questions

Thank you

PhD Thesis – Research PlanALBERTETTI FabrizioThesis Director: Prof. STOFFEL KilianInformation Management InstituteUniversity of NeuchatelSwitzerland

* This project is supported by the Swiss National Science Foundation

An Intelligent Process-driven Knowledge Extraction Framework for Crime Analysis *