Geoprocessing with NoSQL databases and MapReduce
Geospatial information has been relevant for several applications. In the context of monitoring environmental degradation, geospatial data normally collected by sensors present in satellites, map fires, deforested areas, and others. These data are collected continuously for long periods of time and...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Brasil |
| Institución: | Universidade Tecnológica Federal do Paraná (UTFPR) |
| Repositorio: | Revista Brasileira de Geomática |
| Idioma: | portugués |
| OAI Identifier: | oai:periodicos.utfpr:article/15927 |
| Acceso en línea: | https://periodicos.utfpr.edu.br/rbgeo/article/view/15927 |
| Access Level: | acceso abierto |
| Palabra clave: | Ciências Exatas e da Terra /Ciência da Computação /Metodologia e Técnicas da Computação /Banco de Dados Dados Geoespaciais; Dados Abertos; Bancos de Dados NoSQL; MapReduce Geospatial Data; Open Data; NoSQL Databases; MapReduce |
| Sumario: | Geospatial information has been relevant for several applications. In the context of monitoring environmental degradation, geospatial data normally collected by sensors present in satellites, map fires, deforested areas, and others. These data are collected continuously for long periods of time and in great detail, thus generating large volumes. Over the past few decades, this data has been stored mainly in relational databases. To attend the current requirements for large-scale data from different sources, processing in real time and with high concurrency, NoSQL databases are proving to be a better alternative. These database systems are normally distributed, do not require structured data, and are designed for horizontal scalability. However, there is still a deficiency of NoSQL databases in terms of spatial functions. Therefore, the purpose in this paper is to review the NoSQL databases in order to verify its support for geospatial data. The NoSQL databases that have been highlighted in the literature and have been shown to be more suitable for the management of large geographic data are those based on documents, due the wide support for geometric data formats, indexes, geospatial functions, and also due the high computational performance. In addition to these, the MapReduce distributed processing model also highlights for the possibility of creating mapping and reduction functions for geospatial data and taking advantage of the high computational performance platforms that follow this model. |
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