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An Architecture for Semantically Enriched Data Stream Mining

Authors:
Andreas Textor
Fabian Meyer
Marcus Thoss
Jan Schaefer
Reinhold Kroeger
Michael Frey

Keywords: data stream mining, complex event processing, ontologies, semantic tagging, stream query language, IT management, ambient assisted living.

Abstract:
Data stream mining (DSM) techniques can be used to extract knowledge from continuous data streams. In this paper, we 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.

Pages: 107 to 113

Copyright: Copyright (c) IARIA, 2012

Publication date: September 23, 2012

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-242-4

Location: Barcelona, Spain

Dates: from September 23, 2012 to September 28, 2012