Εμφάνιση απλής εγγραφής

dc.creatorGarcía-García F., Corral A., Iribarne L., Vassilakopoulos M., Manolopoulos Y.en
dc.date.accessioned2023-01-31T07:39:46Z
dc.date.available2023-01-31T07:39:46Z
dc.date.issued2018
dc.identifier10.1007/s10707-017-0309-y
dc.identifier.issn13846175
dc.identifier.urihttp://hdl.handle.net/11615/71968
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 SpatialHadoop. 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. © 2017, Springer Science+Business Media, LLC.en
dc.language.isoenen
dc.sourceGeoInformaticaen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85029578015&doi=10.1007%2fs10707-017-0309-y&partnerID=40&md5=fc9a68fb2077fa4cd555218040c05cc1
dc.subjectData handlingen
dc.subjectMultiprocessing systemsen
dc.subjectQuery languagesen
dc.subjectDistance-baseden
dc.subjectMap-reduceen
dc.subjectSpatial data processingen
dc.subjectSpatial queriesen
dc.subjectSpatialHadoopen
dc.subjectDatabase systemsen
dc.subjectalgorithmen
dc.subjectdata processingen
dc.subjectmapen
dc.subjectspatial dataen
dc.subjectthresholden
dc.subjectSpringer New York LLCen
dc.titleEfficient large-scale distance-based join queries in spatialhadoopen
dc.typejournalArticleen


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