Map algebra on raster datasets represented by compact data structures
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http://hdl.handle.net/2183/33371
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Map algebra on raster datasets represented by compact data structuresFecha
2023-06Cita bibliográfica
F. Silva-Coira, J.R. Paramá & S. Ladra, "Map algebra on raster datasets represented by compact data structures", Software - Practice and Experience, 53(6), pp. 1362-1390, 2023. doi:10.1002/spe.3191
Resumen
[Abstract]: The increase in the size of data repositories has forced the design of new computing paradigms to be able to process large volumes of data in a reasonable amount of time. One of them is in-memory computing, which advocates storing all the data in main memory to avoid the disk I/O bottleneck. Compression is one of the key technologies for this approach. For raster data, a compact data structure, called (Formula presented.) -raster, have been recently been proposed. It compresses raster maps while still supporting fast retrieval of a given datum or a portion of the data directly from the compressed data. (Formula presented.) -raster's original work introduced several queries in which it was superior to competitors. However, to be used as the basis of an in-memory system for raster data, it is mandatory to demonstrate its efficiency when performing more complex operations such as the map algebra operators. In this work, we present the algorithms to run a set of these operators directly on (Formula presented.) -raster without a decompression procedure.
Palabras clave
Compression
Map algebra
Raster maps
Map algebra
Raster maps
Descripción
Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG
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Atribución 4.0 Internacional (CC BY 4.0)