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- W2507029678 abstract "The Internet of Things IoT is resulting in ever greater volumes of low level sensor data. However, such data is meaningless without higher level context that describes why such data is needed and what useful information can be derived from it. Provenance records should play a pivotal role in supporting a range of automated processes acting on the data streams emerging from an IoT-enabled infrastructure. In this paper we discuss how such provenance can be modelled by extending an existing suite of provenance ontologies. Furthermore, we demonstrate how provenance abstractions can be inferred from sensor data annotated using the SSN ontology. A real-world application from food-safety compliance monitoring will be used throughout to illustrate our achievements to date, and the challenges that remain." @default.
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- W2507029678 date "2016-01-01" @default.
- W2507029678 modified "2023-09-23" @default.
- W2507029678 title "Modelling Provenance of Sensor Data for Food Safety Compliance Checking" @default.
- W2507029678 cites W2102802363 @default.
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- W2507029678 doi "https://doi.org/10.1007/978-3-319-40593-3_11" @default.
- W2507029678 hasPublicationYear "2016" @default.
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