SpatialHadoop: A MapReduce framework for spatial data

Ahmed Eldawy, Mohamed F. Mokbel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

319 Scopus citations

Abstract

This paper describes SpatialHadoop; a full-fledged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and expressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. Other spatial operations are also implemented following a similar approach. Extensive experiments on real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.

Original languageEnglish (US)
Title of host publication2015 IEEE 31st International Conference on Data Engineering, ICDE 2015
PublisherIEEE Computer Society
Pages1352-1363
Number of pages12
ISBN (Electronic)9781479979639
DOIs
StatePublished - May 26 2015
Event2015 31st IEEE International Conference on Data Engineering, ICDE 2015 - Seoul, Korea, Republic of
Duration: Apr 13 2015Apr 17 2015

Publication series

NameProceedings - International Conference on Data Engineering
Volume2015-May
ISSN (Print)1084-4627

Other

Other2015 31st IEEE International Conference on Data Engineering, ICDE 2015
Country/TerritoryKorea, Republic of
CitySeoul
Period4/13/154/17/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

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