Relation discovery of mobile network alarms with sequential pattern mining

In telecommunication network systems, there are a large number of interconnected components which also contain many subcomponents. Heavy rain, thunder or other factors can cause mal-function of the components or disconnections between the components which trigger alarms. Because of the interconnection of elements, triggered alarms may propagate to other components. This creates harsh challenges to network operators when it comes to root cause analysis. We address this issue by proposing a method on utilizing network alarms for automatic relation discovery between network nodes. By understanding how network elements or network problems are related to each other, a network operator can easily correlate the alarm events and treat clustered groups of alarms instead of specific events. In this study, we use the temporal and spatial aspects of alarm events to cluster network elements. Our results demonstrate that by analyzing the network alarms, a relationship graph showing the connections between different network elements and network problems can be automatically generated. Such relationship graphs can help network operators mining node dependencies and discovering insights within their network.