Linked Open Data in Sensor Data Mashups
Phuoc, Danh Le Hauswirth, Manfred
Phuoc, Danh Le
Hauswirth, Manfred
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Identifiers
http://hdl.handle.net/10379/1113
https://doi.org/10.13025/21425
https://doi.org/10.13025/21425
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Publication Date
2009
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Type
Workshop paper
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Citation
Danh Le Phuoc, Manfred Hauswirth "Kerry Taylor, Arun Ayyagari, David De Roure (editors) "Linked Open Data in Sensor Data Mashups", Proceedings of the 2nd International Workshop on Semantic Sensor Networks (SSN09), in conjunction with ISWC 2009, Vol-522, CEUR, 2009.
Abstract
Sensors and the real-time data they produce are novel sources of information which need to be integrated into the Semantic Web at very large scale. Most of the time such data is locked inside specific applications and only accessible within organizational boundaries. Publishing and integrating sensor data across these islands is difficult and labor- intensive. In this paper we present an approach and an infrastructure which makes sensor data available following the linked open data principle and enables the seamless integration of such data into mashups. Sensor Masher publishes sensor data as Web data sources which can then easily be integrated with other (linked) data sources and sensor data. Raw sensor readings and sensors can be semantically described and an- notated by the user. These descriptions can then be exploited in mashups and in linked open data scenarios and enable the discovery and integration of sensors and sensor data at large scale. The user-generated mashups of sensor data and linked open data can in turn be published as linked open data sources and be used by others.
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Publisher
CEUR
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Attribution-NonCommercial-NoDerivs 3.0 Ireland