Context-aware Outlier Detection for Sensor Data Stream Processing.

Sensor data streaming platforms feed pervasive applications with data through continuous processing tasks. Outliers should be constantly removed from the data. The existence of ontologies and their semantics to express the different properties of sensing devices in semantic models can be leveraged towards solving the problem of outlier detection. We present an outlier detection data stream operator based on sensor ontology-based definitions. This outlier detection operator could be defined and adjusted to fit in different use cases with different requirements. We provide a proof of concept implementation of the operator using a Data Stream Management System and evaluate the effect of increasing outlier rates in data sets on the overall performance of the outlier detection.