Version 1
: Received: 13 September 2024 / Approved: 17 September 2024 / Online: 17 September 2024 (13:30:50 CEST)
How to cite:
Barrett, J.; Legg, P.; Smith, J.; Barnes, T.; Boyle, C. Trends and Challenges in Data Analytics and Machine Learning Using Call Detail Records in Telecom Systems. Preprints2024, 2024091334. https://doi.org/10.20944/preprints202409.1334.v1
Barrett, J.; Legg, P.; Smith, J.; Barnes, T.; Boyle, C. Trends and Challenges in Data Analytics and Machine Learning Using Call Detail Records in Telecom Systems. Preprints 2024, 2024091334. https://doi.org/10.20944/preprints202409.1334.v1
Barrett, J.; Legg, P.; Smith, J.; Barnes, T.; Boyle, C. Trends and Challenges in Data Analytics and Machine Learning Using Call Detail Records in Telecom Systems. Preprints2024, 2024091334. https://doi.org/10.20944/preprints202409.1334.v1
APA Style
Barrett, J., Legg, P., Smith, J., Barnes, T., & Boyle, C. (2024). Trends and Challenges in Data Analytics and Machine Learning Using Call Detail Records in Telecom Systems. Preprints. https://doi.org/10.20944/preprints202409.1334.v1
Chicago/Turabian Style
Barrett, J., Tim Barnes and Charles Boyle. 2024 "Trends and Challenges in Data Analytics and Machine Learning Using Call Detail Records in Telecom Systems" Preprints. https://doi.org/10.20944/preprints202409.1334.v1
Abstract
As telecommunication networks continue to grow, the volume, variety, and velocity of such `big data' poses an immense challenge for security and service analysts. A primary data format in such networks are Call Detail Records (CDRs), and more recently, eXtended Detail Records (XDRs), that capture a variety of information such as caller, receiver, duration, carrier, datetime, amongst other attributes. In this short paper, we investigate the current research trends and challenges that exist within this data-rich domain. As part of this, we look at the domain-specific tasks that analysts will seek to address, we look at how machine learning has been utilised to date within this domain, and we present our current findings on open research problems that are ripe for further investigation.
Keywords
call detail records; big data; mobile networks; machine learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
