Big Data is characterized by the volume and variety of data and the speed of management.
Geographic and Ecology, like many other subject areas, has been flooded by a huge amount of diverse data called Big Data. Though databases are increasingly accessible and affordable, data are becoming more voluminous because they are often georeferenced and include numerous samples (measurements) and in addition to attributes and associated variables (fields). Such data are obtained from increasingly large areas with a greater spatial, temporal and thematic detail. This avalanche of data offers great opportunities for research but also requires new approaches for managing it efficiently, rigorously and accurately, all depending on the particularities of associated thematic information.
Following its classic definition, Big Data is characterized by the volume and variety of data and the speed of management. However, we find it necessary to add an additional property: quality. The quality of the alphanumeric and spatial information of the available data must be analyzed. In the same regard, it is necessary to verify that access, maintenance and propagation of metadata is adequate and consistent. This is crucial when creating, editing and transforming the associated databases. Only after assuring the quality of the data used we can be confident that the corresponding related models are rigorous and accurate.
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