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dc.contributor.authorGarcía García, Francisco
dc.contributor.authorCorral Liria, Antonio Leopoldo
dc.contributor.authorIribarne Martínez, Luis Fernando
dc.contributor.authorVassilakopoulos, Michael
dc.contributor.authorManolopoulos, Yannis
dc.date.accessioned2017-11-06T11:32:53Z
dc.date.available2017-11-06T11:32:53Z
dc.date.issued2017
dc.identifier.issn1384-6175
dc.identifier.urihttp://hdl.handle.net/10835/5264
dc.description.abstractEfficient processing of Distance-Based Join Queries (DBJQs) in spatial databases is of paramount importance in many application domains. The most representative and known DBJQs are the K Closest Pairs Query (KCPQ) and the ε Distance Join Query (εDJQ). These types of join queries are characterized by a number of desired pairs (K) or a distance threshold (ε) between the components of the pairs in the final result, over two spatial datasets. Both are expensive operations, since two spatial datasets are combined with additional constraints. Given the increasing volume of spatial data originating from multiple sources and stored in distributed servers, it is not always efficient to perform DBJQs on a centralized server. For this reason, this paper addresses the problem of computing DBJQs on big spatial datasets in SpatialHadoop, an extension of Hadoop that supports efficient processing of spatial queries in a cloud-based setting. We propose novel algorithms, based on plane-sweep, to perform efficient parallel DBJQs on large-scale spatial datasets in Spatial Hadoop. We evaluate the performance of the proposed algorithms in several situations with large real-world as well as synthetic datasets. The experiments demonstrate the efficiency and scalability of our proposed methodologies.es_ES
dc.language.isoeses_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.sourceGeoinformatica (in press)es_ES
dc.titleEfficient Large-scale Distance-Based Join Queries in SpatialHadoopes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doihttps://doi.org/10.1007/s10707-017-0309-y
dc.relation.projectIDTIN2013-41576-Res_ES


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