Big data storage technologies: a case study for web-based LiDAR visualization

UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleBig Dataes_ES
UDC.departamentoEnxeñaría de Computadoreses_ES
UDC.endPage3840es_ES
UDC.grupoInvGrupo de Arquitectura de Computadores (GAC)es_ES
UDC.startPage3831es_ES
dc.contributor.authorDeibe, David
dc.contributor.authorAmor, Margarita
dc.contributor.authorDoallo, Ramón
dc.date.accessioned2025-01-13T10:18:03Z
dc.date.available2025-01-13T10:18:03Z
dc.date.issued2018
dc.descriptionPresented at: 2018 IEEE International Conference on Big Data (Big Data), Seattle, 10 December 2018 through 13 Decemberes_ES
dc.descriptionThis version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/BigData.2018.8622589es_ES
dc.description.abstract[Abstract]: Big data technologies have been growing up quickly during past years. New storage and computing solutions appear while those already established in the market are improved with new features and better performance. Along with this growth also rises the number of applications and fields where the inclusion of big data technologies provides a large number of benefits, from the reduction in computational costs and economic resources to the improvement in the quality of the services provided which has a direct impact on the customers satisfaction. LiDAR (Light Detection and Ranging) data processing is one of the topics that could benefit from the adoption of these kind of technologies due to the massive datasets that are being gathered nowadays, with applications in archaeology, geography, geology or forestry, among many others. An efficient management of this volume of data becomes a key point especially in visualization, computing and analytic processes. In this paper, we analyse how web applications for the visualization of LiDAR data can benefit from the adoption of big data storage technologies, as well as the advantages and disadvantages that may determine the choice of one of them.es_ES
dc.description.sponsorshipThis research has been supported by the Government of Galicia (Xunta de Galicia) under the Consolidation Programme of Competitive Reference Groups, co-founded by ERDF funds from the EU [Ref. ED431C 2017/04]; under the Consolidation Programme of Competitive Research Units, co-founded by ERDF funds from the EU [Ref. R2016/037]; by Xunta de Galicia (Centro Singular de Investigación de Galicia accreditation 2016/2019) and the European Union (European Regional Development Fund, ERDF) under Grant Ref. ED431G/01; and by the Ministry of Economy and Competitiveness of Spain and ERDF funds from the EU [TIN2016-75845-P]. The LiDAR datasets used in this article belong to: LiDAR-PNOA data repository. Provided by c Instituto Geográfico Nacional de España [33]. PG&E Diablo Canyon Power Plant (DCPP): San Simeon, CA Central Coast and PG&E Diablo Canyon Power Plant (DCPP): Los Osos, CA Central Coast. This material is based on LiDAR Point Cloud Data Distribution and Processing services provided by the OpenTopography Facility with support from the National Science Foundation under NSF Award Numbers 1226353 & 1225810 [34].es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2017/04es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.description.sponsorshipXunta de Galicia; R2016/037es_ES
dc.description.sponsorshipUnited States. National Science Foundation; 1226353es_ES
dc.description.sponsorshipUnited States. National Science Foundation; 1225810es_ES
dc.identifier.citationD. Deibe, M. Amor and R. Doallo, "Big data storage technologies: a case study for web-based LiDAR visualization," 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 3831-3840, doi: 10.1109/BigData.2018.8622589.es_ES
dc.identifier.doi10.1109/BigData.2018.8622589
dc.identifier.isbn978153865035-6
dc.identifier.urihttp://hdl.handle.net/2183/40675
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-75845-P/ES/NUEVOS DESAFIOS EN COMPUTACION DE ALTAS PRESTACIONES: DESDE ARQUITECTURAS HASTA APLICACIONES (II)es_ES
dc.relation.urihttps://doi.org/10.1109/BigData.2018.8622589es_ES
dc.rights© 2018 IEEE.es_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectBig Dataes_ES
dc.subjectData visualizationes_ES
dc.subjectLaser radares_ES
dc.subjectLiDARes_ES
dc.subjectstorage technologieses_ES
dc.subjectweb applicationses_ES
dc.subjectThree-dimensional displayses_ES
dc.subjectMetadataes_ES
dc.titleBig data storage technologies: a case study for web-based LiDAR visualizationes_ES
dc.typeconference outputes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationc98c1fe1-2016-44c1-9225-43fe1c6b8088
relation.isAuthorOfPublicationb3302f65-05d3-4b2c-b8b3-8503e58bba5e
relation.isAuthorOfPublication.latestForDiscoveryc98c1fe1-2016-44c1-9225-43fe1c6b8088

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