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dc.contributor.authorPhuoc, Danh Leen
dc.contributor.authorHauswirth, Manfreden
dc.identifier.citationDanh 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.en
dc.description.abstractSensors 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.en
dc.subjectSemantic sensor dataen
dc.subjectSensor weben
dc.titleLinked Open Data in Sensor Data Mashupsen
dc.typeWorkshop paperen
dc.contributor.funderLíon 2en

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