@misc{10481/81200,
year = {2022},
url = {https://hdl.handle.net/10481/81200},
abstract = {The development of autonomous or self-driving
networks is one of the main challenges faced by the telecommunication
industry. Future networks are expected to realise
a number of tasks, including network optimization and failure
recovery, with minimal human supervision. In this context,
the network community (manufacturers, operators, researchers,
etc.) is looking at Machine Learning (ML) methods with high
expectations. However, ML models can only be as good as the
data they are trained on, which means that autonomous networks
also require a sound autonomous procedure to assess, and if
possible improve, data quality. Although the application of ML
techniques in communication networks is ample in the literature,
analyzing the quality of the network data seems an ignored
problem. This paper presents work in progress on the application
of permutation testing to assess the quality of network datasets.
We illustrate our approach on a number of simple synthetic
datasets with pre-established quality and then we demonstrate
its application in a publicly available network dataset.},
organization = {This work is partially funded by the Agencia Estatal de
Investigaci´on in Spain, grant No PID2020-113462RB-I00,
and by the European Union’s Horizon 2020 research and
innovation programme under the Marie Skłodowska-Curie
grant agreement No 893146. We would like to thank Dominik
Soukup and Toma´sˇ Cˇ ejka for their useful feedback and Szymon
Wojciechowski for his support on the Weles tool.},
publisher = {Seventh IEEE/IFIP International Workshop on Analytics for Network and Service Management. NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium},
title = {Dataset Quality Assessment in Autonomous Networks with Permutation Testing},
author = {Camacho Páez, José and Wasielewska, Katarzyna},
}