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RkNN Query Processing in Distributed Spatial Infrastructures: A Performance Study
dc.contributor.author | García García, Francisco | |
dc.contributor.author | Corral Liria, Antonio Leopoldo | |
dc.contributor.author | Iribarne Martínez, Luis Fernando | |
dc.contributor.author | Vassilakopoulos, Michael | |
dc.date.accessioned | 2017-11-08T08:35:29Z | |
dc.date.available | 2017-11-08T08:35:29Z | |
dc.date.issued | 2017 | |
dc.identifier.uri | http://hdl.handle.net/10835/5279 | |
dc.description.abstract | The Reverse k-Nearest Neighbor (RkNN) problem, i.e. finding all objects in a dataset that have a given query point among their corresponding k-nearest neighbors, has received increasing attention in the past years. RkNN queries are of particular interest in a wide range of applications such as decision support systems, resource allocation, profile-based marketing, location-based services, etc. With the current increasing volume of spatial data, it is difficult to perform RkNN queries efficiently in spatial data-intensive applications, because of the limited computational capability and storage resources. In this paper, we investigate how to design and implement distributed RkNN query algorithms using shared-nothing spatial cloud infrastructures as SpatialHadoop and LocationSpark. SpatialHadoop is a framework that inherently supports spatial indexing on top of Hadoop to perform efficiently spatial queries. LocationSpark is a recent spatial data processing system built on top of Spark. We have evaluated the performance of the distributed RkNN query algorithms on both SpatialHadoop and LocationSpark with big real-world datasets. The experiments have demonstrated the efficiency and scalability of our proposal in both distributed spatial data management systems, showing the performance advantages of LocationSpark. | es_ES |
dc.language.iso | es | es_ES |
dc.relation | info: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/ESDNEMIUI | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | 7th International Conference, MEDI 2017, Barcelona, Spain, October 4–6, 2017. LNCS 10563, pp. 200-207, Springer.. ISBN: 978-3-319-66853-6 | es_ES |
dc.title | RkNN Query Processing in Distributed Spatial Infrastructures: A Performance Study | es_ES |
dc.type | info:eu-repo/semantics/book | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.doi | https://doi.org/10.1007/978-3-319-66854-3_15 | es_ES |
dc.relation.projectID | TIN2013-41576-R | es_ES |