Intelligent search of correlated alarms from database containing noise data

Alarm correlation plays an important role in improving the service and reliability in modern telecommunication networks. Most previous research of alarm correlation did not consider the effects of noise data in the database. This paper focuses on the method of discovering alarm correlation rules from a database containing noise data. We firstly define two parameters, win-freq and win-add, as the measures of noise data and then present a robust search algorithm to solve the problem. At different size of win-freq and win-add, the experiments on the alarm database containing noise data show that the robust search algorithm can discover more rules with the bigger size of win-add. We also compare, by experiments, two different interestingness measures of confidence and correlation.