Abstract
Provenance of food describes food, the processes in food transformation, and the food operators from the source to consumption; modelling the history food. In processing food, the risk of contamination increases if food is treated inappropriately. Therefore, identifying critical processes and applying suitable prevention actions are necessary to measure the risk; known as due diligence. To achieve due diligence, food provenance can be used to analyse the risk of contamination in order to find the best place to sample food. Indeed, it supports building rationale over food-related activities because it describes the details about food during its lifetime. However, many food risk models only rely on simulation with little notion of provenance of food. Incorporating the risk model with food provenance through our framework, prFrame, is our first contribution. prFrame uses Belief Propagation (BP) over the provenance graph for automatically measuring the risk of contamination. As BP works efficiently in a factor graph, our next contribution is the conversion of the provenance graph into the factor graph. Finally, an evaluation of the accuracy of the inference by BP is our last contribution.
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Batlajery, B.V., Weal, M., Chapman, A., Moreau, L. prFood: ontology principles for provenance and risk in the food domain. IEEE, December 2017
Markovic, M., Edwards, P., Kollingbaum, M., Rowe, A.: Modelling provenance of sensor data for food safety compliance checking. In: Mattoso, M., Glavic, B. (eds.) IPAW 2016. LNCS, vol. 9672, pp. 134–145. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-40593-3_11
Thakur, M., Hurburgh, C.R.: Framework for implementing traceability system in the bulk grain supply chain. J. Food Eng. 95(4), 617–626 (2009)
Food Standards Agency: Food Law Code of Practice (England)-April 2015. Report, Food Standards Agency, April 2015
Eves, A., Dervisi, P.: Experiences of the implementation and operation of hazard analysis critical control points in the food service sector. Int. J. Hosp. Manag. 24(1), 319 (2005)
Nauta, M.J.: A modular process risk model structure for quantitative microbiological risk assessment and its application in an exposure assessment of Bacillus cereus in a REPFED. RIVM Rapport 149106007 (2001)
Duarte, A.S.R.: The interpretation of quantitative microbial data: meeting the demands of quantitative microbiological risk assessment. Ph.D. thesis, National Food Institute, Technical University of Denmark (2013)
Pearl, J.: Reverend Bayes on inference engines: a distributed hierarchical approach. In: AAAI 1982. AAAI Press (1982)
Moreau, L., Groth, P., Cheney, J., Lebo, T., Miles, S.: The rationale of PROV. Web Semant.: Sci. Serv. Agents World Wide Web 35(4), 235–257 (2015)
Moreau, L., Missier, P.: PROV-DM: the PROV Data Model, W3C Recommendation REC-prov-dm-20130430, World Wide Web Consortium, April 2013
Moreau, L., Ali, M.: A provenance-based policy control framework for cloud services, May 2014
Packer, H.S., Drăgan, L., Moreau, L.: An auditable reputation service for collective adaptive systems. In: Miorandi, D., Maltese, V., Rovatsos, M., Nijholt, A., Stewart, J. (eds.) Social Collective Intelligence. CSS, pp. 159–184. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08681-1_8
Markovic, M., Edwards, P., Corsar, D.: SC-PROV: a provenance vocabulary for social computation. In: Ludäscher, B., Plale, B. (eds.) IPAW 2014. LNCS, vol. 8628, pp. 285–287. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16462-5_35
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
Cohen, M.H.: The unknown and the unknowable-managing sustained uncertainty. West. J. Nurs. Res. 15(1), 77–96 (1993)
Pearl, J.: Causality: Models, Reasoning and Inference, 2nd edn. Cambridge University Press, New York (2009)
Frey, B.J., Kschischang, F.R., Loeliger, H.A., Wiberg, N.: Factor graphs and algorithms. In: Proceedings of the Annual Allerton Conference on Communication Control and Computing, vol. 35, pp. 666–680. University of Illinois (1997)
Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, Secaucus (2006)
Kschischang, F.R., Frey, B.J., Loeliger, H.A.: Factor graphs and the sum-product algorithm. IEEE Trans. Inf. Theor. 47(2), 498–519 (2006)
Holleran, E., Bredahl, M.E., Zaibet, L.: Private incentives for adopting food safety and quality assurance. Food Policy 24, 669–683 (1999)
World Health Organization: Risk Assessments of Salmonella in Eggs and Broiler Chickens, vol. 2. Food & Agriculture Organization, Geneva (2002)
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Batlajery, B.V., Weal, M., Chapman, A., Moreau, L. (2018). Belief Propagation Through Provenance Graphs. In: Belhajjame, K., Gehani, A., Alper, P. (eds) Provenance and Annotation of Data and Processes. IPAW 2018. Lecture Notes in Computer Science(), vol 11017. Springer, Cham. https://doi.org/10.1007/978-3-319-98379-0_11
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DOI: https://doi.org/10.1007/978-3-319-98379-0_11
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