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.
to date, and the challenges that remain.
Original language | English |
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Title of host publication | Provenance and Annotation of Data and Processes |
Subtitle of host publication | 6th International Provenance and Annotation Workshop, IPAW 2016, McLean, VA, USA, June 7-8, 2016, Proceedings |
Editors | Marta Mattoso, Boris Glavic |
Publisher | Springer |
Pages | 134-145 |
Number of pages | 12 |
ISBN (Electronic) | 9783319405933 |
ISBN (Print) | 9783319405926 |
DOIs | |
Publication status | Published - 4 Jun 2016 |
Event | 6th International Provenance and Annotation Workshop: IPAW 2016 - Virginia, United States Duration: 7 Jun 2016 → 8 Jun 2016 |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 9672 |
ISSN (Print) | 0302-9743 |
Conference
Conference | 6th International Provenance and Annotation Workshop |
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Country/Territory | United States |
City | Virginia |
Period | 7/06/16 → 8/06/16 |
Fingerprint
Dive into the research topics of 'Modelling Provenance of Sensor Data for Food Safety Compliance Checking'. Together they form a unique fingerprint.Datasets
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Sensor data - Food Safety Assurance : combining provnace and the internet of things.
Markovic, M. (Creator), University of Aberdeen, 27 May 2016
DOI: 10.20392/2d277e63-6e4d-4d23-8751-b11406354355, https://github.com/m-markovic/FoodSafety-Data
Dataset