Abstract
to date, and the challenges that remain.
| Original language | English |
|---|---|
| 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 |
|---|---|
| Publisher | Springer |
| Volume | 9672 |
| ISSN (Print) | 0302-9743 |
Conference
| Conference | 6th International Provenance and Annotation Workshop |
|---|---|
| Country | United States |
| City | Virginia |
| Period | 7/06/16 → 8/06/16 |
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Modelling Provenance of Sensor Data for Food Safety Compliance Checking. / Markovic, Milan; Edwards, Peter; Kollingbaum, Martin; Rowe, Alan.
Provenance and Annotation of Data and Processes: 6th International Provenance and Annotation Workshop, IPAW 2016, McLean, VA, USA, June 7-8, 2016, Proceedings. ed. / Marta Mattoso; Boris Glavic. Springer , 2016. p. 134-145 (Lecture Notes in Computer Science; Vol. 9672).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Modelling Provenance of Sensor Data for Food Safety Compliance Checking
AU - Markovic, Milan
AU - Edwards, Peter
AU - Kollingbaum, Martin
AU - Rowe, Alan
N1 - 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.
PY - 2016/6/4
Y1 - 2016/6/4
N2 - 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 achievementsto date, and the challenges that remain.
AB - 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 achievementsto date, and the challenges that remain.
U2 - 10.1007/978-3-319-40593-3_11
DO - 10.1007/978-3-319-40593-3_11
M3 - Conference contribution
SN - 9783319405926
T3 - Lecture Notes in Computer Science
SP - 134
EP - 145
BT - Provenance and Annotation of Data and Processes
A2 - Mattoso, Marta
A2 - Glavic, Boris
PB - Springer
ER -