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Clustering-based compression for raster time series
dc.contributor.author | Muñoz, Martita | |
dc.contributor.author | Fuentes Sepúlveda, José | |
dc.contributor.author | Hernández, Cecilia | |
dc.contributor.author | Navarro, Gonzalo | |
dc.contributor.author | Seco, Diego | |
dc.contributor.author | Silva-Coira, Fernando | |
dc.date.accessioned | 2024-10-03T09:38:52Z | |
dc.date.issued | 2024-09-29 | |
dc.identifier.citation | Martita Muñoz, José Fuentes-Sepúlveda, Cecilia Hernández, Gonzalo Navarro, Diego Seco, Fernando Silva-Coira, Clustering-based compression for raster time series, The Computer Journal, 2024;, bxae090, https://doi.org/10.1093/comjnl/bxae090 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/39386 | |
dc.description | Real world datasets, and scripts to generate the synthetic and semi-synthetic datasets are available at https://figshare.com/s/5ad53959f8eed8a83f83. | es_ES |
dc.description.abstract | [Abstract]: A raster time series is a sequence of independent rasters arranged chronologically covering the same geographical area. These are commonly used to depict the temporal evolution of represented variables. The T-k2-raster is a compact data structure that performs very well in practice for compact representations for raster time series. This structure classifies each raster as a snapshot or a log and encodes logs concerning their reference snapshots, which are the immediately preceding selected snapshots. An enhanced version of the T-k2-raster, called Heuristic T-k2-raster, incorporates a heuristic for automating the selection of snapshots. In this study, we investigate the optimality of the heuristic employed in Heuristic T-k2- raster by comparing it with a dynamic programming approach. Our experimental evaluation demonstrates that Heuristic T-k2-raster is a near-optimal solution, achieving compression performance almost identical to the dynamic programming method. These results indicate that variations of the structure that maintain the temporal order of the rasters are unlikely to significantly improve compression. Consequently, we explore an alternative approach based on clustering, where rasters are grouped according to their similarity, regardless of their temporal order. Our experimental evaluation reveals that this clustering-based strategy can enhance compression in scenarios characterized by cyclic behavior. | es_ES |
dc.description.sponsorship | This work was supported by the Agencia Nacional de Investigación y Desarrollo [21200810 to M.M. and FONDECYT grants 11220545 to J.F. and 1-230755 to G.N.]; the Centre for Biotechnology and Engineering [FB0001 to M.M., C.H., and G.N.]; the Agencia Nacional de Investigación y Desarrollo – Millennium Science Initiative Program [ICN17_002 to M.M., J.F., and G.N.]; and PID2022-141027NB-C21 (EarthDL), TED2021-129245B-C21 (PLAGEMIS), PID2020-114635RB-I00 (EXTRACompact), PDC2021-121239-C31 (FLATCITY-POC), and PDC2021-120917-C21 (SIGTRANS): partially funded by MCIN/AEI/10.13039/501100011033 and ’NextGenerationEU’/PRTR, GRC: ED431C 2021/53, partially funded by GAIN/Xunta de Galicia [D.S. and F.S.]. CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Department of Culture, Education, Vocational Training and Universities, and the Galician universities for the reinforcement of the research centers of the Galician University System (CIGUS). | es_ES |
dc.description.sponsorship | Chile. Agencia National de Investigación y Desarrollo; 21200810 | es_ES |
dc.description.sponsorship | Chile. Fondo Nacional de Desarrollo Científico y Tecnológico; 11220545 | es_ES |
dc.description.sponsorship | Chile. Fondo Nacional de Desarrollo Científico y Tecnológico; 1-230755 | es_ES |
dc.description.sponsorship | Chile. Centre for Biotechnology and Engineering; FB0001 | es_ES |
dc.description.sponsorship | Chile. Agencia National de Investigación y Desarrollo; ICN17_002 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C2021/53 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Oxford University Press | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-141027NB-C21/ES/MODELADO, DESCUBRIMIENTO, EXPLORACION Y ANALISIS DE DATA LAKES MEDIOAMBIENTALES [UDC] | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-129245B-C21/ES/PLAGEMIS | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114635RB-I00/ES/EXPLOTACIÓN ENRIQUECIDA DE TRAYECTORIAS CON ESTRUCTURAS DE DATOS COMPACTAS Y GIS | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2021-121239-C31/ES/FLATCITY-POC | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PDC2021-120917-C21/ES/SIGTRANS | es_ES |
dc.relation.uri | https://doi.org/10.1093/comjnl/bxae090 | es_ES |
dc.rights | This is a pre-copyedited, author-produced version of an article accepted for publication in The Computer Journal, following peer review. The version of record [Martita Muñoz, José Fuentes-Sepúlveda, Cecilia Hernández, Gonzalo Navarro, Diego Seco, Fernando Silva-Coira, Clustering-based compression for raster time series, The Computer Journal, 2024;, bxae090,] is available online at: https://doi.org/10.1093/comjnl/bxae090 © 2024, OUP © The British Computer Society 2024. | es_ES |
dc.subject | Raster dataset | es_ES |
dc.subject | Temporal Raster | es_ES |
dc.subject | Data compression | es_ES |
dc.subject | Compact Data Structure | es_ES |
dc.subject | Clustering | es_ES |
dc.subject | Dynamic Programming | es_ES |
dc.title | Clustering-based compression for raster time series | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/embargoedAccess | es_ES |
dc.date.embargoEndDate | 2025-09-29 | es_ES |
dc.date.embargoLift | 2025-09-29 | |
UDC.journalTitle | The Computer Journal | es_ES |
dc.identifier.doi | 10.1093/comjnl/bxae090 |
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