Towards composite semantic reasoning for realtime network management data enrichment

Monitoring the massive volume of data streaming from managed nodes in Telecommunication networks reacting in a timely manner is increasingly critical for modern Telecommunications Operations Support Systems (OSS). Given the large number and the varieties of the nodes in a telecoms network, the streaming monitoring data is naturally diverse and the volume is often at scales of multiple millions data points each second. These data are well modelled using formal syntaxes (e.g. Management Information Bases), making formal semantics and automated reasoning a viable solution for Telecom data modeling and correlation. This paper proposes an approach that will leverage recent developments in Semantic Reasoning and Big Data. The paper introduces how we propose to use RDF stream reasoning methods for real time event correlation, combined with MapReduce technologies in order to decentralize the large number of reasoning and correlation tasks that need to be undertaken in real time. The proposed approach is currently being implemented and will be evaluated using the diverse data types and volumes that are expected.