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Modelling Provenance of Sensor Data for Food Safety Compliance Checking

  • Milan Markovic
  • Peter Edwards
  • Martin Kollingbaum
  • Alan Rowe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9672)

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.

Keywords

Sensor Data Execution Trace Meat Probe Experimental Deployment Provenance Record 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

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.

References

  1. [CBB+12]
    Compton, M., Barnaghi, P., Bermudez, L., García-Castro, R., Corcho, O., Cox, S., Graybeal, J., Hauswirth, M., Henson, C., Herzog, A., Huang, V., Janowicz, K., Kelsey, W.D., Le Phuoc, D., Lefort, L., Leggieri, M., Neuhaus, H., Nikolov, A., Page, K., Passant, A., Sheth, A., Taylor, K.: The SSN ontology of the W3C semantic sensor network incubator group. Web Semant. Sci. Serv. Agents World Wide Web 17, 25–32 (2012)CrossRefGoogle Scholar
  2. [CCT14]
    Compton, M., Corsar, D., Taylor, K.: Sensor data provenance: SSNO and PROV-O together at last. In: Terra Cognita and Semantic Sensor, Networks, pp. 67–82 (2014)Google Scholar
  3. [CVLM+14]
    Cuevas-Vicenttín, V., Ludäscher, B., Missier, P., Belhajjame, K., Chirigati, F., Wei, Y., Dey, S., Kianmajd, P., Koop, D., Bowers, S., Altintas, I.: Provone: a PROV extension data model for scientific workflow provenance (2014). http://vcvcomputing.com/provone/provone.html
  4. [GG12]
    Garijo, D., Gil, Y.: Augmenting PROV with plans in P-PLAN: scientific processes as linked data. In: Proceedings of the Second International Workshop on Linked Science 2012 - Tackling Big Data. CEUR (2012)Google Scholar
  5. [Mar16]
    Markovic, M.: Utilising provenance to enhance social computation. Ph.D. thesis, University of Aberdeen (2016)Google Scholar
  6. [MDB+13]
    Missier, P., Dey, S., Belhajjame, K., Cuevas-Vicenttin, V., Ludaescher, B.: D-PROV: extending the prov provenance model with workflow structure. Technical report, School of Computing Science, Newcastle University (2013)Google Scholar
  7. [MEC13]
    Markovic, M., Edwards, P., Corsar, D.: Utilising provenance to enhance social computation. In: Alani, H., Kagal, L., Fokoue, A., Groth, P., Biemann, C., Parreira, J.X., Aroyo, L., Noy, N., Welty, C., Janowicz, K. (eds.) ISWC 2013, Part II. LNCS, vol. 8219, pp. 440–447. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. [MM12]
    Moreau, L., Missier, P.: PROV-DM: The PROV data model. W3C Recommendation (2012). http://www.w3.org/TR/prov-dm/

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Milan Markovic
    • 1
  • Peter Edwards
    • 1
  • Martin Kollingbaum
    • 1
  • Alan Rowe
    • 2
  1. 1.Computing ScienceUniversity of AberdeenAberdeenUK
  2. 2.Rowett Institute of Nutrition and HealthUniversity of AberdeenAberdeenUK

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