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 › Conference contribution

9 Citations (Scopus)

6 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	United States
City	Virginia
Period	7/06/16 → 8/06/16

Fingerprint

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; 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

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). https://doi.org/10.1007/978-3-319-40593-3_11

Markovic, Milan ; Edwards, Peter ; Kollingbaum, Martin ; Rowe, Alan. / 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).

@inproceedings{c40a984845c44956b633a5f6eec5cc11,

title = "Modelling Provenance of Sensor Data for Food Safety Compliance Checking",

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.",

author = "Milan Markovic and Peter Edwards and Martin Kollingbaum and Alan Rowe",

note = "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.",

year = "2016",

month = "6",

day = "4",

doi = "10.1007/978-3-319-40593-3_11",

language = "English",

isbn = "9783319405926",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

pages = "134--145",

editor = "Mattoso, {Marta } and Boris Glavic",

booktitle = "Provenance and Annotation of Data and Processes",

}

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 -

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

Datasets

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

Dataset

Modelling Provenance of Sensor Data for Food Safety Compliance Checking

Abstract

Publication series

Conference

Fingerprint

Cite this

Access to Document

Related content

Datasets

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