A rule induction approach to forecasting critical alarms in a telecommunication network

Chris Wrench, Frederic Theodor Stahl, Giuseppe Di Fatta, Vidhyalakshmi Karthikeyan, Detlef Nauck

In: 2019 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE International Conference on Data Mining Workshops (ICDMW-2019) befindet sich ICDM 2019 November 8-11 Beijing China IEEE 9/2019.


This paper proposes a white box method of predicting critical alarms so they can be mitigated and understood by engineers. Forecasting these alarms will avoid outages and maintain the agreed service level which is beneficial to both the provider of telecommunication services and the consumers. The paper evaluates several item set mining approaches on a set of alarms of the British Telecom (BT) national telecommunication network and proposes a novel transformation of the data to enable the discovery of patterns undetectable by current item set mining approaches. The result is a method for rule induction that predicts alarms with high precision using a wide range of features.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence