Mostrar el registro sencillo del ítem

dc.contributor.authorGarcía-García, Francisco
dc.contributor.authorCorral, Antonio
dc.contributor.authorIribarne, Luis
dc.contributor.authorVassilakopoulos, Michael
dc.date.accessioned2023-02-24T11:13:48Z
dc.date.available2023-02-24T11:13:48Z
dc.date.issued2020
dc.identifier.issn0167-739X
dc.identifier.urihttp://hdl.handle.net/10835/14373
dc.description.abstractSpatialHadoop is an extended MapReduce framework supporting global indexing techniques that partition spatial datasets across several machines and improve spatial query processing performance compared to traditional Hadoop systems. SpatialHadoop supports several spatial operations (e.g., Nearest Neighbor search, range query, spatial intersection join, etc.) and seven spatial partitioning techniques (Grid, Quadtree, STR, STR+, -d tree, Z-curve and Hilbert-curve). Distance-Join Queries (DJQs), like the Nearest Neighbors Join Query (NNJQ) and Closest Pairs Query (CPQ), are common operations used in numerous spatial applications. DJQs are costly operations, since they combine spatial joins with distance-based search. Data partitioning improves the management of large datasets and speeds up query performance. Therefore, performing DJQs efficiently with new partitioning methods in SpatialHadoop is a challenging task. In this paper, a new data partitioning technique based on Voronoi-Diagrams is designed and implemented in SpatialHadoop. Moreover, improved NNJQ and CPQ MapReduce algorithms, using the new partitioning mechanism, are also designed and developed for SpatialHadoop. Finally, the results of an extensive set of experiments with real-world datasets are presented, demonstrating that the new partitioning technique and the improved DJQ MapReduce algorithms are efficient, scalable and robust in SpatialHadoop.es_ES
dc.language.isoenes_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/ES/MINECO/TIN2013-41576-R/ES/Evolución de sistemas dinámicos en la nube: Un escenario marco hacia las interfaces de usuario inteligentes/ESDNEMIUIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceFuture Generation Computer Systems, Volume 111, October 2020, Pages 723-740es_ES
dc.titleImproving Distance-Join Query Processing with Voronoi-Diagram based Partitioning in SpatialHadoopes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.future.2019.10.037es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


Ficheros en el ítem

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 Internacional