Analysis of Big Data technologies for use in agro-environmental science. (October 2016)

• Record Type: Journal Article
• Authors: Lokers, Rob; Knapen, Rob; Janssen, Sander; van Randen, Yke; Jansen, Jacques
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 494
• Page End: 504
• Publication Date: 2016-10
• Subjects: Big Data -- Semantics -- Data integration -- Interdisciplinary research -- Agriculture -- Forestry; 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.2016.07.017
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Recent developments like the movements of open access and open data and the unprecedented growth of data, which has come forward as Big Data, have shifted focus to methods to effectively handle such data for use in agro-environmental research. Big Data technologies, together with the increased use of cloud based and high performance computing, create new opportunities for data intensive science in the multi-disciplinary agro-environmental domain. A theoretical framework is presented to structure and analyse data-intensive cases and is applied to three case studies, together covering a broad range of technologies and aspects related to Big Data usage. The case studies indicate that most persistent issues in the area of data-intensive research evolve around capturing the huge heterogeneity of interdisciplinary data and around creating trust between data providers and data users. It is therefore recommended that efforts from the agro-environmental domain concentrate on the issues of variety and veracity. Highlights: A theoretical framework is presented to frame and analyse Big Data use cases. Three case studies related to agro-environmental modelling, covering the range of Big Data characteristics are analysed. Most persistent issues in agro-environmental science concern variety and veracity. Approaches to deal with variety and veracity issues are presented.