Topographical Proximity: Exploiting Domain Knowledge for Sequential Data Mining

In today’s mobile telecommunications networks, in-creasingly powerful fault management systems are re-quired to ensure robustness and quality of service of the network. In this context, fault alarm correlation is of prime importance to extract meaningful information from the vast quantities of alarms generated by the net-work. Existing sequential data mining techniques ad-dress the task of identifying possible correlations in fre-quent sequences of telecoms alarms. These frequent se-quence sets, however, may contain sequences which are not plausible from the point of view of network topol-ogy constraints. This paper presents the Topographical Proximity (TP) approach which exploits the topograph-ical information encoded in telecommunication alarms in order to address this lack of plausibility in mined alarm sequences. An evaluation of the quality of mined sequences is presented and discussed. Results show an improvement in overall system performance for impos-ing proximity constraints.