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Enabling Efficient Distributed Spatial Join on Large Scale Vector-Raster Data Lakes
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DOI：10.1109/ACCESS.2022.3157405.png)
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Abstract
En 中文
Both the increasing number of GPS-enabled mobile devices and the geographic crowd-sourcing initiatives, such as Open Street Map, are determinants for the large amount of vector spatial data that is currently being produced. On the other hand, the automatic generation of raster data by remote sensing devices and environmental modeling processes was always leading to very large datasets. Currently, huge data generation rates are reached by improved sensor observation systems and data processing infrastructures. As an example, the Sentinel Data Access System of the Copernicus Program of the European Space Agency (ESA) was publishing 38.71 TB of data per day during 2020. This paper shows how the assumption of a new spatial data model that includes multi-resolution parametric spatial data types, enables achieving an efficient implementation of a large scale distributed spatial analysis system for integrated vector-raster data lakes. In particular, the proposed implementation outperforms the state-of-the-art Spark-based spatial analysis systems by more than one order of magnitude during vector-raster spatial join evaluation.
Keywords:
Spatial databases
Data models
Distributed databases
Big Data applications
Data analysis
Cluster computing
Arrays
Large-scale data analysis
spatial analytics
spatial data management
vector-raster data analysis
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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Cited Papers
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