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Evolving from Data to Knowledge Mining to Uncover Hidden Relationships

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Technology Development for Security Practitioners

Part of the book series: Security Informatics and Law Enforcement ((SILE))

Abstract

Nowadays, law enforcement agencies – LEAs – are forced to deal with extreme volumes of data, being in need to analyse from heterogeneous data sources, uncover hidden relationships, trends and patterns of incidents and ultimately reach solid evidence to be used in court. In this chapter, a system is presented that can assist LEA officers in fighting crime, that, following the collection of the primary data, it applies semantic reasoning tools that allow the system to relate pieces of data, based on their inner relationships, and extract new information based on the asserted facts and rules defined by the LEAs. Then, the results derived by the reasoners and the initial data are fused using appropriate tools including a trajectory, a person and an event fusion tool, to be finally visualized by the proposed bipartite graphs. The proposed system is expected to decrease the time required to solve a crime by LEA’s officers.

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Acknowledgement

This work has been performed under the H2020 786629 project MAGNETO, which has received funding from the European Union’s Horizon 2020 Programme. This chapter reflects only the authors’ view, and the European Commission is not liable to any use that may be made of the information contained therein.

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Authors and Affiliations

  1. Institute of Communication and Computer Systems, Athens, Greece

    Konstantinos Demestichas, Konstantina Remoundou, Ioannis Loumiotis & Evgenia Adamopoulou

  2. Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany

    Wilmuth Mueller, Dirk Pallmer & Dirk Mühlenberg

  3. ITTI SP. o.o., Poznań, Poland

    Rafal Kozik & Michael Choras

  4. Thales Research and Technology, Palaiseau, France

    David Faure, Roxana Horincar, Edward Benedict Brodie of Brodie & Charlotte Jacobe de Naurois

  5. Venaka Media, Kent, UK

    Krishna Chandramouli

  6. Siveco, Bucharest, Romania

    Alexandra Rosca

Authors
  1. Konstantinos Demestichas
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  2. Konstantina Remoundou
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  3. Ioannis Loumiotis
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  4. Evgenia Adamopoulou
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  5. Wilmuth Mueller
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  8. Rafal Kozik
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  9. Michael Choras
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  10. David Faure
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  11. Roxana Horincar
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  12. Edward Benedict Brodie of Brodie
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  13. Charlotte Jacobe de Naurois
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  14. Krishna Chandramouli
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  15. Alexandra Rosca
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Corresponding author

Correspondence to Konstantinos Demestichas .

Editor information

Editors and Affiliations

  1. CENTRIC (Centre of Excellence in Terrorism, Resilience, Intelligence and Organised Crime Research), Sheffield Hallam University, Sheffield, UK

    Babak Akhgar

  2. Center for Security Studies-KEMEA, Athens, Greece

    Dimitrios Kavallieros

  3. Crisis Management and Security Unit, Institute of Communication and Computer, Athens, Greece

    Evangelos Sdongos

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Demestichas, K. et al. (2021). Evolving from Data to Knowledge Mining to Uncover Hidden Relationships. In: Akhgar, B., Kavallieros, D., Sdongos, E. (eds) Technology Development for Security Practitioners. Security Informatics and Law Enforcement. Springer, Cham. https://doi.org/10.1007/978-3-030-69460-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-69460-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69459-3

  • Online ISBN: 978-3-030-69460-9

  • eBook Packages: EngineeringEngineering (R0)

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