Model for Semantic Base Structuring of Digital Data to Support Agricultural Management

This article presents a semantic model for structuring digital databases to function in a cloud environment and connect to data sources originating from Big Data. The work examines the process of receiving structured, semi-structured and unstructured data for use in agricultural risk management. It is conceived as an architecture that combines Data Mart, Data Warehouse (NoSQL), and Data Lake resources to support decision making, through knowledge discovery and applies algorithms for data mining by machine learning resources. The configuration presented addresses scenarios involving agricultural data, obtained from sensors operating in multiple modes.