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Detection of Anomalies in the Criminal Proceedings Based on the Analysis of Event Logs

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  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11832))

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Abstract

Process mining makes it possible to solve a task of finding and analyzing deviations in the process. System event logs record information about real process behavior. Weaknesses and errors of a workflow can be found during the analysis of logs. This is especially important in areas associated with significant responsibility and risk.

In this paper the focus is on the criminal procedure analysis via process mining methods. A model of this process allows for flexibility only in a strictly regulated framework. However, in practice undesired deviations appear and, therefore, need to be detected and prevented.

We adopted conformance checking techniques to determine the anomaly of the trace, taking into account the specifics of the process. We also did clustering of anomaly cases to reveal behavior patterns. They will be helpful for identification of potential causes of such anomalies.

This work is supported by the Basic Research Program at the National Research University Higher School of Economics.

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Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, Moscow, Russia

    Alexandra A. Kolosova & Irina A. Lomazova

Authors
  1. Alexandra A. Kolosova
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  2. Irina A. Lomazova
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Corresponding author

Correspondence to Alexandra A. Kolosova .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, Moscow, Russia

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre-lès-Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  12. Ca Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

Appendix

Appendix

See Table 3.

Table 3. Transitions used in the criminal procedure model
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Fig. 1.
figure 1

Model of the criminal proceedings

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Kolosova, A.A., Lomazova, I.A. (2019). Detection of Anomalies in the Criminal Proceedings Based on the Analysis of Event Logs. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Lecture Notes in Computer Science(), vol 11832. Springer, Cham. https://doi.org/10.1007/978-3-030-37334-4_36

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  • DOI: https://doi.org/10.1007/978-3-030-37334-4_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37333-7

  • Online ISBN: 978-3-030-37334-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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