Big Data Architectures for the Climate Change Analysis: A Systematic Mapping Study



Big Data, Climate Change, Architectures, systematic mapping


Despite the volume of data generated, scientists cannot accurately predict how climate change will manifest itself locally and what measures should be applied to mitigate it effectively. On the other hand, Big Data is a new technology that faces the challenge of collecting, characterizing and analyzing a large amount of data, taking into account data from multiple sources, multiple variables and multiple scales with different spatial and temporal attributes. To do this, we review and synthesize the current state of research of Big Data architectures that help solve the problems caused by climate change in health (16%), agriculture(8%), biodiversity(16%), energy(8%), water resources(4%) and clima(48%). To achieve the objective, we have carried out a systematic mapping study, which includes four research questions, including 25 studies, published from 2013 to 2019. The architectures found have been classified according to their use, which can be for statistical analysis, monitoring and simulations; helping researchers to integrate knowledge into the practical use of Big Data in the context of climate change.


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Author Biographies

ANIA Lorena CRAVERO, Universidad de La Frontera

Licenciada en Ciencias de la Ingeniería (1996) e Ingeniera Civil Industrial m. Informática (1997), por la Universidad de La Frontera, Temuco, Chile. Obtuvo su máster en Tecnologías de la Información, por la Universidad Politécnica de Madrid, España (2006). Ha obtenido en 2010 su Doctorado en Cs. de la Computación y Sistemas Informáticos por la Atlantic International University, EE.UU.
Se desempeña como Académico en el Departamento de Ciencias de la Computación e Informática, e investigadora en el Centro de Estudios en Ingeniería de Software, Universidad de La Frontera. Participa en el comité de gestión del Centro de Excelencia de Computación Científica. Sus intereses de investigación están en el área de Modelado Bases de Datos, Ingeniería de Requisitos para Almacenes de Datos y Big Data.

Samuel Sepulveda, Universidad de La Frontera, Temuco, Chile

Licenciado en Cs. de la Ingeniería (1998) e Ing. Civil Industrial m. Informática (1999), por la Universidad de La Frontera, Temuco, Chile. Obtuvo su máster en Dirección y Gestión de Sistemas de Información y TIC, por la Universitat Oberta de Catalunya, España (2006). Actualmente postula al grado de Doctor en Aplicaciones de la Informática por la Universidad de Alicante, España.
Se desempeña como Académico en el Dpto. de Ciencias de la Computación e Informática, e investigador en el Centro de Estudios en Ingeniería de Software, Universidad de La Frontera. Sus intereses de investigación están en el área de Ingeniería de Requerimientos, Modelado de Líneas de Productos de Software y estudios secundarios aplicados en Ingeniería de Software.

Lilia Muñoz, Universidad Tecnológica de Panamá, Chiriquí, Panamá

Ingeniera de Sistemas Computacionales por la Universidad Tecnológica de Panamá. Obtuvo la Maestría en Computación con énfasis en Sistemas de Información en el Instituto Tecnológico de Costa Rica. Ha obtenido el Doctorado en Aplicaciones de la Informática por la Universidad de Alicante en el seno del grupo de Investigación Lucentia (2010).
Docente tiempo completo en la Facultad de Ingeniería de Sistemas Computacionales de la Universidad Tecnológica de Panamá. Sus áreas de interés son la calidad de software, almacenes de datos, auditoría de sistemas, bases de datos, informática aplicada a la educación, internet de las cosas. Actualmente coordina el Grupo en Tecnologías Computacionales Emergentes en la Universidad Tecnológica de Panamá, Centro Regional de Chiriquí.


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How to Cite

CRAVERO, A. L., Sepulveda, S., & Muñoz, L. (2021). Big Data Architectures for the Climate Change Analysis: A Systematic Mapping Study. IEEE Latin America Transactions, 18(10), 1793–1806. Retrieved from