Integrating Digital Forensics and Digital Discovery to Improve E-mail Communication Analysis in Organisations

  • Mithileysh SathiyanarayananEmail author
  • Odunayo Fadahunsi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 766)


In Digital Forensics and Digital Discovery, e-mail communication analysis has become an important part of the litigation process. Integrating these two can improve e-mail communication analysis in organisations and help both legal and technical professionals achieve goals of conducting analysis in a manner that is legally defensible and forensically sound. In this forensic discovery process, digital evidence plays an increasingly vital role in the court to prove or disprove an individual or a group of individual’s actions in order to secure a conviction. However, e-mail investigations are becoming increasingly complex and time consuming due to the multifaceted large data involved, and investigators find themselves unable to explore and conduct analysis in an appropriately efficient and effective manner. This situation has prompted the need for improved e-mail communication analysis that can be capable of handling large and complex investigations to detect suspicious activities. So, our interactive visualisations aims to improve digital forensics discovery ability to search and analyse a vast amount of e-mail information quickly and efficiently.


Visualisation Digital discovery Digital forensics E-mail communication 


  1. 1.
    Casey, E.: Handbook of Digital Forensics and Investigation. Academic, Cambridge (2009)Google Scholar
  2. 2.
    Casey, E.: Digital Evidence and Computer Crime: Forensic Science, Computers, and the Internet. Academic, Cambridge (2011)Google Scholar
  3. 3.
    Attfield, S., Blandford, A.: Discovery-led refinement in e-discovery investigations: sensemaking, cognitive ergonomics and system design. Artif. Intell. Law 18(4), 387–412 (2010)CrossRefGoogle Scholar
  4. 4.
    Socha, G., Gelbmann, T.: The electronic discovery reference model (edrm). (2009)
  5. 5.
  6. 6.
    Stasko, J., Görg, C., Liu, Z.: Jigsaw: supporting investigative analysis through interactive visualization. Inf. Vis. 7(2), 118–132 (2008)CrossRefGoogle Scholar
  7. 7.
  8. 8.
  9. 9.
  10. 10.
  11. 11.
    Collins, C., Carpendale, S., Penn, G.: Docuburst: visualizing document content using language structure. In: Computer Graphics Forum, vol. 28, no. 3, pp. 1039–1046. Wiley Online Library (2009)Google Scholar
  12. 12.
    Klimt, B., Yang, Y.: The enron corpus: A new dataset for email classification research. In: Machine Learning: ECML 2004, pp. 217–226. Springer, Berlin (2004)CrossRefGoogle Scholar
  13. 13.
    Sathiyanarayanan, M., Turkay, C.: Is multi-perspective visualisation recommended for e-discovery email investigations? (2016)Google Scholar
  14. 14.
    Sathiyanarayanan, M., Turkay, C.: Determining and visualising e-mail subsets to support e-discovery (2016)Google Scholar
  15. 15.
    Sathiyanarayanan, M., Turkay, C., Fadahunsi, O.: Design and implementation of small multiples matrix-based visualisation to monitor and compare email socio-organisational relationships (2018)Google Scholar
  16. 16.
    Sathiyanarayanan, M., Turkay, C.: Challenges and opportunities in using analytics combined with visualisation techniques for finding anomalies in digital communications (2017)Google Scholar
  17. 17.
    Lam, H., Bertini, E., Isenberg, P., Plaisant, C., Carpendale, S.: Empirical studies in information visualization: seven scenarios. IEEE Trans. Vis. Comput. Graph. 18(9), 1520–1536 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Mithileysh Sathiyanarayanan
    • 1
    Email author
  • Odunayo Fadahunsi
    • 1
  1. 1.City, University of LondonLondonEngland, UK

Personalised recommendations