A Big Data Analytics Architecture Framework for Oilseeds and Textile Industry Production and International Trade for Sub-Saharan Africa (SSA)

Among the most revolutionary technologies are Big Data Analytics, Artificial Intelligence (AI) and robotics, Machine Learning (ML), cybersecurity, blockchain technology, and cloud computing. The research was focused on how to create a Big Data Analytics Architecture Framework to increase production capability and global trade for Sub-Saharan Africa's oilseeds and textile industries (SSA). Legumes, shea butter, groundnuts, and soybeans are significant crops in Sub-Saharan Africa (SSA) because they offer a range of advantages in terms of the economy, society, and the environment. The infrastructure, e-commerce, and disruptive technologies in the oilseeds and textile industries, as well as global e-commerce, all demand large investments. The pragmatic worldview served as the foundation for the Mixed Methods Research technique. This study employed a review of the literature, document analysis, and focus groups. For the oilseeds and textile sectors in SSA, a Big Data analytics architectural framework was created. It supports E-commerce and is based on the Hadoop platform, which offers the analytical tools and computing power needed to handle such massive data volumes. The low rate of return on investments made in breeding, seed production, processing, and marketing limits the competitiveness of the oil crop or legume seed markets.

Keywords: AI, Big Data Analytics, Cybersecurity, E-Commerce, Hadoop, Machine Learning, Oilseeds, Textile industry.