Pattern mining model for automatic network monitoring in heterogeneous wireless communication networks

The rapid development of network technology and its evolution towards heterogeneous networks has increased the demand for more efficient network monitoring and management capabilities in providing high quality communication services. Current research aims to support automatic monitoring and management of heterogeneous wireless communication networks. In this work sequential pattern mining is used to extract interesting event sequence patterns from Telecom Network Monitoring data, driven by the need to support ever increasing volumes of management data. In this paper, a novel model of pattern discovery is proposed. Existing algorithms (PrefixSpan and HTPM) are extended and integrated into the model for discovering interesting sequential patterns. Evaluations show that these algorithms are suitable as part of an architecture to support automatic network monitoring.