RT Journal Article
T1 Automatic alarm prioritization by data mining for fault management in cellular networks.
A1 García, Antonio
A1 Toril-Genovés, Matías
A1 Oliver, Pablo
A1 Luna-Ramírez, Salvador
A1 Ortiz, Manuel
K1 Inteligencia artificial
K1 Programas de ordenadores - Depuración
AB Network management systems play an important role to deal with the large size and complexity of current cellular networks. Thus, operators and vendors focus much of their efforts on developing new techniques and tools for network management. One of the most critical processes in network management is fault management, since a failure in a network element might have a strong impact on user satisfaction due to service degradation. Unfortunately, cellular networks generate thousands of alarms daily, whichhave to be checked manually by operator personnel. With the latest advances in big data analytics, different methods for reducing the number of alarms to be monitored have been proposed in the literature. In this work, an automatic method for prioritizing alarms based on the need for specialized personnel is presented. The core of the method is an ensemble model built with supervised learning that estimates the probability that an alarm generates a trouble ticket. The model is trained with trouble ticket data from the network operation center. A performance comparison of four classical base classifiers (naïve Bayes, random forest, artificial neural network and support vector machine) for the ensemble is presented. The model is implemented in IBM SPSS Modeler and tested with a real alarm and trouble ticket dataset taken from a live cellular network. Results show that the proposed model correctly flags those alarms that need further analysis by the operator and filter out those alarms that do not have impact on network performance. The main contribution of this work is unveiling a new application (the automatic prioritization of alarms in a cellular network based on the need for specialized personnel) and presenting for the first time a performance comparison of base classifiers used for this purpose (since the required dataset is extremely difficult to find for privacy reasons).
PB Elsevier
YR 2020
FD 2020-05-30
LK https://hdl.handle.net/10630/30112
UL https://hdl.handle.net/10630/30112
LA eng
NO García, A. J., Toril, M., Oliver, P., Luna-Ramírez, S., & Ortiz, M. (2020). Automatic alarm prioritization by data mining for fault management in cellular networks. Expert Systems with Applications, 158, 113526.
NO Como parte de la política de Elsevier, la publicación tenía un embargo de 24 meses, que ya ha expirado.
NO Spanish Ministry of Science, Innovation and Universities (RTI2018-099148-B-I00,5GPLAN) and Ericsson Spain
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026