RT Journal Article
T1 Enabling Efficient Distributed Spatial Join on Large Scale Vector-Raster Data Lakes
A1 Ríos Viqueira, José Ramón
A1 Cotos Yáñez, José Manuel
A1 Taboada González, José Ángel
A1 Villarroya Fernández, Sebastián
AB 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.
PB IEEE
YR 2022
FD 2022-03-08
LK https://hdl.handle.net/10347/39471
UL https://hdl.handle.net/10347/39471
LA eng
NO S. Villarroya, J. R. R. Viqueira, J. M. Cotos and J. A. Taboada, "Enabling Efficient Distributed Spatial Join on Large Scale Vector-Raster Data Lakes," in IEEE Access, vol. 10, pp. 29406-29418, 2022, doi: 10.1109/ACCESS.2022.3157405.
DS Minerva
RD 27 abr 2026