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Albertetti, Fabrizio
Résultat de la recherche
The CriLiM Methodology: Crime Linkage with a Fuzzy MCDM Approach
2013-8-12, Albertetti, Fabrizio, Cotofrei, Paul, Grossrieder, Lionel, Ribaux, Olivier, Stoffel, Kilian
Grouping events having similarities has always been interesting for analysts. Actually, when a label is put on top of a set of events to denote they share common properties, the automation and the capability to conduct reasoning with this set drastically increase. This is particularly true when considering criminal events for crime analysts; conjunction, interpretation and explanation can be key success factors to apprehend criminals. In this paper, we present the CriLiM methodology for investigating both serious and high-volume crime. Our artifact consists in implementing a tailored computerized crime linkage system, based on a fuzzy MCDM approach in order to combine spatio-temporal, behavioral, and forensic information. As a proof of concept, series in burglaries are examined from real data and compared to expert results.
Crime Linkage: a Fuzzy MCDM Approach
2013-6-4, Albertetti, Fabrizio, Cotofrei, Paul, Grossrieder, Lionel, Ribaux, Olivier, Stoffel, Kilian
Grouping crimes having similarities has always been interesting for analysts. Actually, when a set of crimes share common properties, the capability to conduct reasoning and the automation with this set drastically increase. Conjunction, interpretation and explanation based on similarities can be key success factors to apprehend criminals. In this paper, we present a computerized method for high-volume crime linkage, based on a fuzzy MCDM approach in order to combine situational, behavioral, and forensic information. Experiments are conducted with series in burglaries from real data and compared to expert results.
From Police Reports to Data Marts: a Step Towards a Crime Analysis Framework
2012-11-11, Albertetti, Fabrizio, Stoffel, Kilian
Nowadays, crime analyses are often conducted with computational methods. These methods, using several different systems (such as decision support systems), need to handle forensic data in a specific way. In this paper we present a methodology to structure police report data for crime analysis. The proposed artifact is mainly about applying data warehousing concepts to forensic data in a crime analysis perspective. Moreover, a proof of concept is carried out with real forensic data to illustrate and evaluate our methodology. These experiments highlight the need of such framework for crime analysis.