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Efficient Processing of Raster and Vector Data
dc.contributor.author | Silva-Coira, Fernando | |
dc.contributor.author | Paramá, José R. | |
dc.contributor.author | Ladra, Susana | |
dc.contributor.author | López, Juan R. | |
dc.contributor.author | Gutiérrez, Gilberto | |
dc.date.accessioned | 2020-01-29T15:32:11Z | |
dc.date.available | 2020-01-29T15:32:11Z | |
dc.date.issued | 2020-01-10 | |
dc.identifier.citation | Silva-Coira F, Paramá JR, Ladra S, López JR, Gutiérrez G (2020) Efficient processing of raster and vector data. PLoS ONE 15(1): e0226943. https://doi.org/10.1371/journal.pone.0226943 | es_ES |
dc.identifier.issn | 1932-6203 | |
dc.identifier.uri | http://hdl.handle.net/2183/24792 | |
dc.description.abstract | [Abstract] 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. | es_ES |
dc.description.sponsorship | This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 690941; from the Ministerio de Ciencia, Innovación y Universidades (PGE and ERDF) grant numbers TIN2016-78011-C4-1-R; TIN2016-77158 C4-3-R; RTC-2017-5908-7; from Xunta de Galicia (co-founded with ERDF) grant numbers ED431C 2017/58; ED431G/01; IN852A 2018/14; and University of Bío-Bío grant numbers 192119 2/R; 195119 GI/VC | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2017/58 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; IN852A 2018/14 | es_ES |
dc.description.sponsorship | Universidad del Bío-Bío (Chile); 192119 2/R | es_ES |
dc.description.sponsorship | Universidad del Bío-Bío (Chile); 195119 GI/VC | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Public Library of Science | es_ES |
dc.relation | info:eu-repo/grantAgreement/EC/H2020/690941 | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-78011-C4-1-R/ES/DATOS 4.0: RETOS Y SOLUCIONES-UDC | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-77158-C4-3-R/ES/VELOCITY: PROCESADO EFICIENTE DE BIG DATA ESPACIO-TEMPORAL PARA FLATCITY | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTC-2017-5908-7/ES/STEPS. Soluciones Tecnológicas para la Evolución en la Prestación de Servicios en campo/ | |
dc.relation.uri | https://doi.org/10.1371/journal.pone.0226943 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Raster data | es_ES |
dc.subject | Vector data | es_ES |
dc.subject | Algorithms | es_ES |
dc.subject | Spatial data | es_ES |
dc.title | Efficient Processing of Raster and Vector Data | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | PLoS One | es_ES |
UDC.volume | 15 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | e0226943 | es_ES |
dc.identifier.doi | 10.1371/journal.pone.0226943 |
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