DGGS/Processing
< DGGS
Indicar quais as principais operações DGGS de processamento e análise.
- criar cobertura (interna ou externa) de células mistas ou de mesmo nível hierárquico.
- conferir se célula pequena (ponto) está dentro de alguma célula maior (zona).
- conferir se célula está dentro de cobertura
- listar vizinhos imediatos de uma célula
- ...
Big Data Hadoop
Segundo artigo recente de 2023:
- Distributed computing frameworks, e.g., SpatialHadoop [18], HadoopGIS [19], and GeoSpark [20], are designed for geospatial data management to leverage the high performance advantage of Hadoop MapReduce and Spark, providing spatial operation and basic spatial models for processing. HadoopViz [21] and GeoSparkViz [22] were developed based on SpatialHadoop and GeoSpark, respectively, for geospatial data visualization. HadoopViz is compatible with SpatialHadoop, and the two are responsible for visualization and data processing, respectively [18]. GeoSparkViz is a scalable spatial data visualization framework with massive data rasterization, pixel aggregation, color rendering and other parallel processing capabilities, and its efficiency can be increased by up to five times [22]. GeoTrellis is also an open-source high-performance geographic data framework developed from Spark, which is capable of processing and visualizing massive amounts of geospatial data
- Eldawy, A.; Mokbel, M.F. SpatialHadoop: A MapReduce framework for spatial data. In Proceedings of the 2015 IEEE 31st International Conference on Data Engineering, Seoul, Republic of Korea, 13–17 April 2015; pp. 1352–1363.
- Aji, A.; Wang, F.; Vo, H.; Lee, R.; Liu, Q.; Zhang, X.; Saltz, J. Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce. Proc. VLDB Endow. 2013, 6, 1009–1020.
- Jia, Y.; Wu, J.; Sarwat, M. GeoSpark: A cluster computing framework for processing large-scale spatial data. In Proceedings of the 23rd SIGSPATIAL International Conference, Seattle, WA, USA, 3–6 November 2015; pp. 1–4.
- Eldawy, A.; Mokbel, M.F.; Jonathan, C. HadoopViz: A MapReduce framework for extensible visualization of big spatial data. In Proceedings of the 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, 16–20 May 2016; pp. 601–612.
- Yu, J.; Zhang, Z.; Sarwat, M. GeoSparkViz: A Scalable Geospatial Data Visualization Framework in the Apache Spark Ecosystem. In Proceedings of the 30th International Conference on Scientific and Statistical Database Management, Bozen-Bolzano, Italy, 9–11 July 2018; pp. 1–12.