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Anomaly detection is a key feature of applications processing singularities using IoT sensor measures. In a recent project, we highlighted that to guarantee high quality detections, metadata providing spatio-temporal contexts on sensor measures are needed. These metadata hence become an integral component of the anomaly detection computation and need to be processed using a streaming approach. In this paper, we introduce Scouter, a generic tool that helps in capturing, analyzing, scoring and storing the contextual information of a given application domain. The processing depends on a semantic-based approach that exploits ontologies to score the relevancy of contextual information. The paper provides details on the system’s architecture, describes lessons learned and introduces practical aspects.

Scouter: A Stream Processing Web Analyzer to Contextualize Singularities