Abstract
In telecommunication networks, alarms are usually useful for identifying faults, and therefore solving them. However, for large systems the number of alarms produced is so large that the current management systems are overloaded. One way of overcoming this problem is to filter and reduce the number of alarms before the faults can be located. In this paper, we describe a new approach for fault recognition and classification in telecommunication networks. We study and evaluate its performance using real-world data collected from 3G telecommunication networks.
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Bellec, JH., Kechadi, MT. (2006). Behavioural Proximity Approach for Alarm Correlation in Telecommunication Networks. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_64
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DOI: https://doi.org/10.1007/11925231_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
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