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.
The research described here was funded by an award made by the RCUK IT as a Utility Network+ (EP/K003569/1) and the UK Food Standards Agency. We thank the owner and staff of Rye & Soda restaurant, Aberdeen for their support throughout the project.
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Four burgers were cooked separately and two burgers were cooked at the same time.
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The meat probe sensor data had to be manually annotated with the feature of interest (i.e. the meat item for which the core temperature was measured) as the current design of the probe does not support automatic recognition of probed items.
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Acknowledgment
The research described here was funded by an award made by the RCUK IT as a Utility Network+ (EP/K003569/1) and the UK Food Standards Agency. We thank the owner and staff of Rye & Soda restaurant, Aberdeen for their support throughout the project.
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Markovic, M., Edwards, P., Kollingbaum, M., Rowe, A. (2016). Modelling Provenance of Sensor Data for Food Safety Compliance Checking. In: Mattoso, M., Glavic, B. (eds) Provenance and Annotation of Data and Processes. IPAW 2016. Lecture Notes in Computer Science(), vol 9672. Springer, Cham. https://doi.org/10.1007/978-3-319-40593-3_11
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DOI: https://doi.org/10.1007/978-3-319-40593-3_11
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