A novel transversal processing model to build environmental big data services in the cloud

• DOI: 10.1016/j.envsoft.2021.105173
• Publication Date: 2021-08-24T21:58:08Z
• Authors: J. Armando Barron...; Jose Luis Gonzale...; Jesus Carretero; Ivan Lopez-Arevalo; Raffaele Montella
• Keywords: Informática; Big data; Climate data; Cloud computing; Data analytic; Environmental data; Machine learning; Big data; Climate data; Environmental data; Data analytic; 13. Climate action; Machine learning; 0207 environmental engineering; Cloud computing; 02 engineering and technology

This work has been partially supported by the project 41756 "Plataforma tecnológica para la gestión, aseguramiento, intercambio y preservación de grandes volúmenes de datos en salud y construcción de un repositorio nacional de servicios de análisis de datos de salud" by the FORDECYT-PRONACES.<br/>This paper presents a novel transversal, agnostic-infrastructure, and generic processing model to build environmental big data services in the cloud. Transversality is used for building processing structures (PS) by reusing/coupling multiple existent software for processing environmental monitoring, climate, and earth observation data, even in execution time, with datasets available in cloud-based repositories. Infrastructure-agnosticism is used for deploying/executing PSs on/in edge, fog, and/or cloud. Genericity is used to embed analytic, merging information, machine learning, and statistic micro-services into PSs for automatically and transparently converting PSs into big data services to support decision-making procedures. A prototype was developed for conducting case studies based on the data climate classification, earth observation products, and making predictions of air data pollution by merging different monitoring climate data sources. The experimental evaluation revealed the efficacy and flexibility of this model to create complex environmental big data services.<br/>