Title: Stress-testing big data platform to extract smart and interoperable food safety analytics

Authors: Ioanna Polychronou; Giannis Stoitsis; Mihalis Papakonstantinou; Nikos Manouselis

Addresses: Agroknow, 110 Pentelis, Maroussi, Greece ' Agroknow, 110 Pentelis, Maroussi, Greece ' Agroknow, 110 Pentelis, Maroussi, Greece ' Agroknow, 110 Pentelis, Maroussi, Greece

Abstract: One of the significant challenges for the future is to guarantee safe food for all inhabitants of the planet. During the last 15 years, very important fraud issues like the '2013 horse meat scandal' and the '2008 Chinese milk scandal' have greatly affected the food industry and public health. One of the alternatives for this issue consists of increasing production, but to accomplish this, it is necessary that innovative options be applied to enhance the safety of the food supply chain. For this reason, it is quite important to have the right infrastructure in order to manage data of the food safety sector and provide useful analytics to Food Safety Experts. In this paper, we describe Agroknow's Big Data Platform architecture and examine its scalability for data management and experimentation.

Keywords: food safety; big data; stress-testing data; data platform; data management; data experimentation; machine-learning; predictive analytics; infrastructure performance; evaluation metrics.

DOI: 10.1504/IJMSO.2020.115441

International Journal of Metadata, Semantics and Ontologies, 2020 Vol.14 No.4, pp.306 - 314

Received: 05 Aug 2020
Accepted: 06 Jan 2021
Published online: 02 Jun 2021 *

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