Automatic discovery of rules for predicting network management events

In order to discover behavior patterns, current algorithms only analyze historical data in terms of performance data or fault events, ignoring the temporal correlation among different types of information, including the configuration changes. A method is presented that can discover recurrent patterns from multiple flows of events, such as alarms and configuration events, as well as discrete information, such as traffic and usage, taking into account static and dynamic information concerning observed objects and their environments. This method can filter out theoretically useless patterns, using a novel technique for detecting chaos in sequences of events. The prediction accuracy of the discovered patterns has been measured using objects with dynamic behavior controlled by known and complex differential equations. The proposed mining method has been used for discovering and predicting alarms in a computer network composed of several Internet servers taking into account the alarm and configuration events history, as well as static information about these servers.