Modelling Provenance of Sensor Data for Food Safety Compliance Checking

Milan Markovic; Peter Edwards; Martin Kollingbaum; Alan Rowe

doi:10.1007/978-3-319-40593-3_11

Modelling Provenance of Sensor Data for Food Safety Compliance Checking

Milan Markovic, Peter Edwards, Martin Kollingbaum, Alan Rowe

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

12 Citations (Scopus)

14 Downloads (Pure)

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.

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	https://doi.org/10.1007/978-3-319-40593-3_11
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/Territory	United States
City	Virginia
Period	7/06/16 → 8/06/16

Access to Document

10.1007/978-3-319-40593-3_11Licence: Unspecified

Accepted Manuscript
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-40593-3_11
Accepted author manuscript, 2.04 MBLicence: Unspecified

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

Pete Edwards
- Agents at Aberdeen
- Trusted Things and Communities
Person: Academic

Cite this

Markovic, M., Edwards, P., Kollingbaum, M., & Rowe, A. (2016). Modelling Provenance of Sensor Data for Food Safety Compliance Checking. In M. Mattoso, & B. Glavic (Eds.), Provenance and Annotation of Data and Processes: 6th International Provenance and Annotation Workshop, IPAW 2016, McLean, VA, USA, June 7-8, 2016, Proceedings (pp. 134-145). (Lecture Notes in Computer Science; Vol. 9672). Springer . https://doi.org/10.1007/978-3-319-40593-3_11

Modelling Provenance of Sensor Data for Food Safety Compliance Checking. / Markovic, Milan ; Edwards, Peter; Kollingbaum, Martin et al.

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 › Published conference contribution

Markovic, M , Edwards, P, Kollingbaum, M & Rowe, A 2016, Modelling Provenance of Sensor Data for Food Safety Compliance Checking. in M Mattoso & B Glavic (eds), Provenance and Annotation of Data and Processes: 6th International Provenance and Annotation Workshop, IPAW 2016, McLean, VA, USA, June 7-8, 2016, Proceedings. Lecture Notes in Computer Science, vol. 9672, Springer , pp. 134-145, 6th International Provenance and Annotation Workshop, Virginia, United States, 7/06/16. https://doi.org/10.1007/978-3-319-40593-3_11

Markovic M , Edwards P, Kollingbaum M, Rowe A. Modelling Provenance of Sensor Data for Food Safety Compliance Checking. In Mattoso M, Glavic B, editors, Provenance and Annotation of Data and Processes: 6th International Provenance and Annotation Workshop, IPAW 2016, McLean, VA, USA, June 7-8, 2016, Proceedings. Springer . 2016. p. 134-145. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-40593-3_11

Markovic, Milan ; Edwards, Peter ; Kollingbaum, Martin et al. / Modelling Provenance of Sensor Data for Food Safety Compliance Checking. Provenance and Annotation of Data and Processes: 6th International Provenance and Annotation Workshop, IPAW 2016, McLean, VA, USA, June 7-8, 2016, Proceedings. editor / Marta Mattoso ; Boris Glavic. Springer , 2016. pp. 134-145 (Lecture Notes in Computer Science).

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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 achievementsto date, and the challenges that remain.",

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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.

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Modelling Provenance of Sensor Data for Food Safety Compliance Checking

Abstract

Publication series

Conference

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Datasets

Sensor data - Food Safety Assurance : combining provnace and the internet of things.

Profiles

Pete Edwards

Cite this