Intelligent monitoring with background knowledge

Abstract

This paper describes the design and implementation of an intelligent monitoring system, that runs advanced inference mechanisms to correlate events from various sensors. Different to existing monitoring approaches, it exploits taxonomic background knowledge in form of ontological information to draw refined inferences. The monitoring system provides abstract knowledge exchange capabilities between different monitoring clients to support users during the setup and maintenance process. The system supports a variety of sensors and collectors, also including new sensors that can be mapped to the system conveniently.