A novel transversal processing model to build environmental big data services in the cloud. (October 2021)

• Record Type: Journal Article
• Authors: Armando Barron-Lugo, J.; Gonzalez-Compean, Jose Luis; Carretero, Jesus; Lopez-Arevalo, Ivan; Montella, Raffaele
• Is Part Of: Environmental modelling & software. Volume 144(2021)
• Publication Date: 2021-10
• Subjects: Big data -- Cloud computing -- Environmental data -- Climate data -- Machine learning -- Data analytic; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105173
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: 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. Highlights: A novel transversal processing model to build environmental big data services in clouds. TCP: coupling points interconnect multiple existent services with data sources. TEP: extracting points consume data to build bridges with existent data services. TPS: Processing services embed analytic and machine learning into existent software. Transversal big data services for climate, earth observation and environment studies.