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MRSLICE: Efficient RkNN Query Processing in SpatialHadoop
dc.creator | García-García F., Corral A., Iribarne L., Vassilakopoulos M. | en |
dc.date.accessioned | 2023-01-31T07:39:43Z | |
dc.date.available | 2023-01-31T07:39:43Z | |
dc.date.issued | 2019 | |
dc.identifier | 10.1007/978-3-030-32065-2_17 | |
dc.identifier.isbn | 9783030320645 | |
dc.identifier.issn | 03029743 | |
dc.identifier.uri | http://hdl.handle.net/11615/71961 | |
dc.description.abstract | Nowadays, with the continuously increasing volume of spatial data, it is difficult to execute spatial queries efficiently in spatial data-intensive applications, because of the limited computational capability and storage resources of centralized environments. Due to that, shared-nothing spatial cloud infrastructures have received increasing attention in the last years. SpatialHadoop is a full-edged MapReduce framework with native support for spatial data. SpatialHadoop also supports spatial indexing on top of Hadoop to perform efficiently spatial queries (e.g., k-Nearest Neighbor search, spatial intersection join, etc.). 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 been recently studied very thoroughly. 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. In this paper, we present the design and implementation of an RkNN query MapReduce algorithm, so-called MRSLICE, in SpatialHadoop. We have evaluated the performance of the MRSLICE algorithm on SpatialHadoop with big real-world datasets. The experiments have demonstrated the efficiency and scalability of our proposal in comparison with other RkNNQ MapReduce algorithms in SpatialHadoop. © 2019, Springer Nature Switzerland AG. | en |
dc.language.iso | en | en |
dc.source | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075855611&doi=10.1007%2f978-3-030-32065-2_17&partnerID=40&md5=754c818611948a53b68c4b4334420f2d | |
dc.subject | Decision support systems | en |
dc.subject | Digital storage | en |
dc.subject | Location based services | en |
dc.subject | Motion compensation | en |
dc.subject | Nearest neighbor search | en |
dc.subject | Telecommunication services | en |
dc.subject | Text processing | en |
dc.subject | Cloud infrastructures | en |
dc.subject | Computational capability | en |
dc.subject | Design and implementations | en |
dc.subject | Map-reduce | en |
dc.subject | Reverse k-nearest neighbors | en |
dc.subject | RNNQ | en |
dc.subject | Spatial data processing | en |
dc.subject | SpatialHadoop | en |
dc.subject | Data handling | en |
dc.subject | Springer Science and Business Media Deutschland GmbH | en |
dc.title | MRSLICE: Efficient RkNN Query Processing in SpatialHadoop | en |
dc.type | conferenceItem | en |
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