BigDataGrapes aims to support all European companies active in two key industries powered by grapevines: the wine industry and the natural cosmetics one but also provide innovative tools to the food safety market. It will help them respond to the significant opportunity that big data is creating in their relevant markets, by pursuing two ambitious goals:
The BigDataGrapes Data Marketplace is officially launched. A data marketplace is a platform where users buy or sell different types of data sets and data streams from several sources. Data marketplaces are mostly cloud services where individuals or businesses upload data to the cloud. Those platforms enable self-service data access while ensuring security, consistency and high quality of data for both parties.
The main services of the BigDataGrapes data marketplace are:
Big_Data_Grapes_Hellberg.pdfJohn Stoitsis and Michalis Papakonstantinou from Agroknow (Coordinator partner of the BigDataGrapes project), exploit the results of the BigDataGrapes project and contribute at the book "Food Fraud: A Global Threat With Public Health and Economic Consequences", providing advanced information for food fraud detection in the meat and poultry products.
Access to food safety insights enables businesses and professionals alike, to identify, monitor and prevent any increasing risks or incidents that need global attention across the complete supply chain. Technology alone is not enough. In order to truly enrich data to create actionable intelligence – for example reactive and predictive insights on the global supply chain – we must integrate human skills and expertise as well. Using scientific or hands-on human expertise can help interpret (and even sense-check) the results thrown out by sophisticated AI algorithms.
Continuing the “Food Safety insights and lessons learned” through the mini video series of the BigDataGrapes project, we are moving into the dairy industry report in this video. Via the accumulation of data for the case of dairy products over the past decade, we are able to form a well-rounded opinion towards the origin of arising safety incidents and the sectors where they are perceived to recur over the years. As already mentioned in our previous video, we need to frequently update the prediction models by training them with new data.
On the 22nd of January, 2020, a dedicated webinar was held via Zoom, from the 2 partners of the BigDataGrapes project. KU Leuven and Agroknow organized a webinar on:
"Predictive Analytics for Food Risk Prevention: Towards visually appealing & highly interactive predictions"
On the 8th of December, 2020, a dedicated online workshop was carried out with the participation of 13 members of the Conagra company. Conagra Brands, Inc. (founded in 1919) is an American packaged foods company headquartered in Chicago, Illinois. It is an approximately $8 billion company that combines a rich heritage of making great food with a sharpened focus and entrepreneurial spirit. Conagra makes and sells products under various brand names that are available in supermarkets, restaurants, and food service establishments.
During the previous videos of the BigDataGrapes series we explained all the theoretical background on how to use Predictions in the food sector. Now, it is time to focus on how we can implement all this gained-knowledge and methodology that combines big data processing technologies with artificial intelligence technologies, in the chocolate products and their ingredients.
Predictive analytics, over time, they gain more recognition as at the same time their widespread use, by companies is done systematically. An Industry Innovation Group on Predictive Analytics for Food Integrity from over 26 companies of different types and sizes is mentioning the tremendous value in enhancing the predictive capabilities of food risk assessment and prevention.
In this episode, Giannis Stoitsis, CTO and partner at Agroknow (Coordinator partner of the BigDataGrapes project), presents the right prediction metrics that we are calling to choose when we want to check if our prediction algorithm is working properly.
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