Topographical proximity for mining network alarm data
Ann Devitt, Joseph Duffin, Robert Moloney · 2005
Increasingly powerful fault management systems are required to ensure robustness and quality of service in today’s net-works. In this context, event correlation is of prime impor-tance to extract meaningful information from the wealth of alarm data generated by the network. Existing sequential data mining techniques address the task of identifying possi-ble correlations in sequences of alarms. The output sequence sets, however, may contain sequences which are not plausi-ble from the point of view of network topology constraints. This paper presents the Topographical Proximity (TP) ap-proach which exploits topographical information embedded in alarm data in order to address this lack of plausibility in mined sequences. An evaluation of the quality of mined sequences is presented and discussed. Results show an im-provement in overall system performance for imposing prox-imity constraints.