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Machine learning for discovery analytics to support criminal investigations

: Müller, Wilmuth; Pallmer, Dirk; Mühlenberg, Dirk; Loumiotis, Ioannis; Remoundou, Konstantina; Kosmides, Pavlos; Demestichas, Konstantinos


Ahmad, Fauzia (Ed.) ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Big Data II: Learning, Analytics, and Applications , California, United States : 27 April - 8 May 2020, Online Only, United States
Bellingham, WA: SPIE, 2020 (Proceedings of SPIE 11395)
ISBN: 978-1-5106-3567-8
ISBN: 978-1-5106-3568-5
Paper 1139504, 11 pp.
Conference "Big Data - Learning, Analytics, and Applications" <2, 2020>
European Commission EC
H2020; 786629; MAGNETO
Multimedia Analysis and Correlation Engine for Organised Crime Prevention and Investigation
Conference Paper
Fraunhofer IOSB ()
machine learning; classification; person fusion; money transactions; law enforcement agencies

Over the last decades, criminal activities have progressively expanded into the information technology (IT) world, adding to the “traditional” criminal activities, ignoring political boundaries and legal jurisdictions. Building upon the possibilities of technologies like Big Data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support LEAs in their critical need to exploit all available resources and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions for information fusion and classification tools intended to support LEA’s investigations. The Person Fusion Tool will be responsible for finding in an underlying knowledge graph different person instances that refer to the same person and fuse these instances. The general approach, the similarity metrics, the architecture of the tool and design choices as well as measures to improve the efficiency of the tool will be presented. The tool for classifying money transfer transactions uses decision trees. This is due to a requirement of easy explainability of the classification results, which is demanded from the ethical and legal perspective of the MAGNETO project. The design of the tool, the selected implementation and an evaluation based on anonymized financial data records will be presented.