Efficient processing of raster and vector data


Autoři: Fernando Silva-Coira aff001;  José R. Paramá aff001;  Susana Ladra aff001;  Juan R. López aff001;  Gilberto Gutiérrez aff002
Působiště autorů: Universidade da Coruña, Centro de investigación CITIC, Facultade de Informática, Campus de Elviña, s/n, A Coruña, Spain aff001;  Universidad del Bío-Bío, DCCTI, Chillán, Chile aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226943

Souhrn

In this work, we propose a framework to store and manage spatial data, which includes new efficient algorithms to perform operations accepting as input a raster dataset and a vector dataset. More concretely, we present algorithms for solving a spatial join between a raster and a vector dataset imposing a restriction on the values of the cells of the raster; and an algorithm for retrieving K objects of a vector dataset that overlap cells of a raster dataset, such that the K objects are those overlapping the highest (or lowest) cell values among all objects. The raster data is stored using a compact data structure, which can directly manipulate compressed data without the need for prior decompression. This leads to better running times and lower memory consumption. In our experimental evaluation comparing our solution to other baselines, we obtain the best space/time trade-offs.

Klíčová slova:

Algorithms – Cost models – Data compression – Data management – Decision making – Language – Leaves – Memory


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