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Stronger compact representations of object trajectories
dc.contributor.author | Gómez-Brandón, Adrián | |
dc.contributor.author | Navarro, Gonzalo | |
dc.contributor.author | R. Paramá, José | |
dc.contributor.author | Brisaboa, Nieves R. | |
dc.contributor.author | Gagie, Travis | |
dc.date.accessioned | 2024-07-10T07:28:45Z | |
dc.date.available | 2024-07-10T07:28:45Z | |
dc.date.issued | 2024-02-13 | |
dc.identifier.citation | Gómez-Brandón, A., Navarro, G., Paramá, J. R., Brisaboa, N. R., & Gagie, T. (2024). Stronger compact representations of object trajectories. Geo-Spatial Information Science, 1–37. https://doi.org/10.1080/10095020.2024.2310590 | es_ES |
dc.identifier.issn | 1009-5020 | |
dc.identifier.issn | 1993-5153 | |
dc.identifier.uri | http://hdl.handle.net/2183/37856 | |
dc.description.abstract | [Absctract]: GraCT and ContaCT were the first compressed data structures to represent object trajectories, demonstrating that it was possible to use orders of magnitude less space than classical indexes while staying competitive in query times. In this paper we considerably enhance their space, query capabilities, and time performance with three contributions. (1) We design and evaluate algorithms for more sophisticated nearest neighbor queries, finding the trajectories closest to a given trajectory or to a given point during a time interval. (2) We modify the data structure used to sample the spatial positions of the objects along time. This improves the performance on the classic spatio-temporal and the nearest neighbor queries, by orders of magnitude in some cases. (3) We introduce RelaCT, a tradeoff between the faster and larger ContaCT and the smaller and slower GraCT, offering a new relevant space-time tradeoff for large repetitive datasets of trajectories. | es_ES |
dc.description.sponsorship | For the A Coruña team: This work was supported by GAIN/Xunta de Galicia: GRC: grants ED431C 2021/53, and CIGUS 2023-2026; Ministerio de Ciencia e Innovación and EU/ERDF A way of making Europe under grant [PID2022-141027NB-C21];Ministerio de Ciencia e Innovación under grant [PID2020-114635RB-I00]; Ministerio de Ciencia e Innovación and Next-GenerationEU/PRTR under grants [TED2021-129245B-C21; PDC2021-120917-C21]; Gonzalo Navarro was funded by ANID – Millennium Science Initiative Program – Code ICN17_002 and by Fondecyt under grants [1-200038; 1-230755]. Travis Gagie was funded by Fondecyt under grant [1171058] and by NSERC Discovery under grant [RGPIN-07185-2020]. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2021/53 | es_ES |
dc.description.sponsorship | Xunta de Galicia; CIGUS 2023-2026 | es_ES |
dc.description.sponsorship | Chile. National Agency of Research and Development (ANID); ICN17_002 | es_ES |
dc.description.sponsorship | Chile. Fondo Nacional de Desarrollo Científico y Tecnológico (Fondecyt); 1-200038 | es_ES |
dc.description.sponsorship | Chile. Fondo Nacional de Desarrollo Científico y Tecnológico (Fondecyt); 1-230755 | es_ES |
dc.description.sponsorship | Chile. Fondo Nacional de Desarrollo Científico y Tecnológico (Fondecyt); 1171058 | es_ES |
dc.description.sponsorship | Canada. Natural Science and Engineering Research Council (NSERC); RGPIN-07185-2020 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Taylor & Francis | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/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 | 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/TED2021-129245B-C21/ES/PLAGEMIS | 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/10.1080/10095020.2024.2310590 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Moving objects | es_ES |
dc.subject | Mobile computing | es_ES |
dc.subject | Trajectories representation | es_ES |
dc.subject | Compact data structures | es_ES |
dc.subject | Nearest neighbor algorithms | es_ES |
dc.title | Stronger compact representations of object trajectories | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Geo-spatial Information Science | es_ES |
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