Ontology for Structuring a Digital Databases for Decision Making in Grain Production

This paper presents an ontology for the structuring of digital databases with the objective of acting in a cloud environment and meeting big data sources in the agricultural context of grain production. Its conception is structured in three stages: the first stage presents an ontological architecture aimed at public and private cloud environments, the second stage deals with a semantic model at process level, and a pseudocode for ontological application is elaborated in the third stage, considering the technologies applied to the cloud. This work combines advanced features to support decision making from Data Lake storage solutions, semantic treatment of big data, as well as the presentation of strategies based on machine learning and data quality analysis to obtain data and metadata organized for application in a decision model. The configuration of the ontology presented meets the diversity of big data projects in the grain production context, the characteristics of which are based on interoperability in the use of heterogeneous data and its integration, elasticity of computational resources, and high availability of cloud access.