Towards an Ontology Proposal Model in Data Lake for Real-time COVID-19 Cases Prevention - Kachaoui, Jabrane; Larioui, Jihane; Belangour, Abdessamad (International Journal of Online and Biomedical Engineering (iJOE), 2020)
Globally, the coronavirus epidemic has now hit lives of millions and thousands of people around the world. The growing threat of this virus continues rising as new cases appear every day. Yet, affected countries by coronavirus are currently taking important measures to remedy it by using artificial intelligence (AI) and Big Data technologies. According to the World Health Organization (WHO), AI and Big Data have performed an important role in China's response to COVID-19, the genetic mutation name for coronavirus. Predicting an epidemic emergence, from the corona virus appearance to a person's predisposition to develop it, is fundamental to combating it. In this battle, Big Data is on the front line. However, Big Data cannot provide all of the expected insights and derive value from manipulated data. This is why we propose a semantic approach to facilitate the use of these data. In this paper, we present a novel approach that combines between the Semantic Web Services (SWS) and the Big Data characteristics in order to extract a significant information from multiple Data sources that can be exploitable for generating real-time statistics and reports.