  Event management in sensor networks is a multidisciplinary field involving
several steps across the processing chain. In this paper, we discuss the major
steps that should be performed in real- or near real-time event handling
including event detection, correlation, prediction and filtering. First, we
discuss existing univariate and multivariate change detection schemes for the
online event detection over sensor data. Next, we propose an online event
correlation scheme that intends to unveil the internal dynamics that govern the
operation of a system and are responsible for the generation of various types
of events. We show that representation of event dependencies can be
accommodated within a probabilistic temporal knowledge representation framework
that allows the formulation of rules. We also address the important issue of
identifying outdated dependencies among events by setting up a time-dependent
framework for filtering the extracted rules over time. The proposed theory is
applied on the maritime domain and is validated through extensive
experimentation with real sensor streams originating from large-scale sensor
networks deployed in ships.
