An Architecture for Semantically Enriched Data Stream Mining
Data Stream Mining (DSM) techniques can be used to extract knowledge from continuous data streams. In this paper, the authors present an approach providing a modelling and execution architecture for networks of DSM operators. It can be applied in resource-constrained environments, and provides capabilities for semantic enrichment of data streams. This allows processing of streams not only based on information contained in the streams, but also on their semantic contexts. The approach consists of a DSM runtime system, a concept for semantic tagging of stream elements, the integration of semantic information stores, and a domain-specific DSM network description language. A small ambient assisted living scenario is presented as an example application.