A Method to build a Geolocalized Food Price Time Series Knowledge Base analyzable by Everyone

Johyn Papin , Frédéric Andrès (1) , Laurent d'Orazio (2, 3)

1 NII - National Institute of Informatics
2 IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires
3 SHAMAN - Symbolic and Human-centric view of dAta MANagement

Time-series analysis is a very challenging concept in Data Science for companies and industries. Harvesting prices of agricultural production (e.g. vegetable, fruit, milk...) as time series is key to operating reliable dish cost prediction at scale to ensure for example that the market price is valid. In this paper, we describe initial stakeholder needs, the service and engineering contexts in which the challenge of time-serie harvesting and management arose, and theoretical and architectural choices we made to implement a solution of historical food prices to demonstrate the feasibility. For this, we use scrappers through the TOR network. We also propose a knowledge map approach to make the data accessible to any type of users.

Domaines: Base de données [cs.DB]

Johyn Papin, Frédéric Andrès, Laurent d'Orazio. A Method to build a Geolocalized Food Price Time Series Knowledge Base analyzable by Everyone. Latin America Data Science Workshop (LADaS@VLDB), 2018, Rio de Janeiro, Brazil. ⟨hal-01857388⟩