Three Big Data Case Studies

  • Marcello TrovatiEmail author
  • Andy Baker
Part of the Simulation Foundations, Methods and Applications book series (SFMA)


The utilisation of Big Data within the criminology field has allowed a revaluation of the traditional assessment and investigation of available data sources, pushing criminological research towards new frontiers. However, the enormous amount of data, which is now available from numerous sources focusing on criminology, has created both challenges and opportunities in the discovery of innovative approaches to prevent, detect and predict crime.


Analytical Hierarchy Process Text Pattern Criminological Research Dependency Network Depression Assessment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Trovati M, Bessis N (2015) An influence assessment method based on co-occurrence for topologically reduced big data sets. Soft Computing Google Scholar
  2. 2.
    Bird S, Loper E, Klein E (2009) Natural language processing with python O’reilly media IncGoogle Scholar
  3. 3.
    Manning CD, Schutze H (1999) Foundations of statistical natural language processing. MIT Press, CambridgeGoogle Scholar
  4. 4.
    Hagberg AA, Schult DA, Swart PJ (2008) Exploring network structure, dynamics, and function using networkX. Proceedings of the 7th Python in Science Conference (SciPy 2008)Google Scholar
  5. 5.
    Johnson A, Holmes P, Craske L, Trovati M, Bessis N, Larcombe P (2015) A computational objectivity in depression assessment for unstructured large datasets. Proceedings of IBDS-2015Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceEdge Hill UniversityOrmskirkUK
  2. 2.College of Engineering and TechnologyUniversity of DerbyDerbyUK

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