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Author: Jörg Brunsmann

Affiliation: Faculty of Mathematics and Computer Science and Distance University of Hagen, Germany

ISBN: 978-989-8425-33-1

Keyword(s): .

Abstract: Although pervasively deployed, sensors are currently neither highly interconnected nor very intelligent, since they do not know each other and pro-duce only raw data streams. This lack of interoperability and high-level reasoning capabilities are major obstacles for exploiting the full potential of sensor data streams. Since interoperability and reasoning processes require a common understanding, RDF based linked sensor data is used in the semantic sensor web to articulate the meaning of sensor data. This paper shows how to derive higher levels of streamed sensor data understanding by constructing reasoning know-ledge with SPARQL. In addition, it is demonstrated how to push these inferences to interested clients in different application domains like social media streaming, weather observation and intelligent product lifecycle maintenance. Finally, the paper describes how real-time pushing of inferences enables provenance tracking and how archiving of inferred events could support furt her decision making processes. (More)


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Paper citation in several formats:
Brunsmann, J. (2010). Archiving pushed Inferences from Sensor Data Streams. In Proceedings of the International Workshop on Semantic Sensor Web - SSW, (IC3K 2010) ISBN 978-989-8425-33-1, pages 38-46. DOI: 10.5220/0003116000380046

author={Jörg Brunsmann.},
title={Archiving pushed Inferences from Sensor Data Streams},
booktitle={Proceedings of the International Workshop on Semantic Sensor Web - SSW, (IC3K 2010)},


JO - Proceedings of the International Workshop on Semantic Sensor Web - SSW, (IC3K 2010)
TI - Archiving pushed Inferences from Sensor Data Streams
SN - 978-989-8425-33-1
AU - Brunsmann, J.
PY - 2010
SP - 38
EP - 46
DO - 10.5220/0003116000380046

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