Topographical proximity for mining network alarm data

Abstract

Increasingly powerful fault management systems are required to ensure robustness and quality of service in today's networks. In this context, event correlation is of prime importance to extract meaningful information from the wealth of alarm data generated by the network. Existing sequential data mining techniques address the task of identifying possible correlations in sequences of alarms. The output sequence sets, however, may contain sequences which are not plausible from the point of view of network topology constraints. This paper presents the Topographical Proximity (TP) approach 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 improvement in overall system performance for imposing proximity constraints.