Spatial partitioning techniques in spatialhadoop

Ahmed Eldawy, Louai Alarabi, Mohamed F Mokbel

Research output: Chapter in Book/Report/Conference proceedingChapter

102 Scopus citations

Abstract

SpatialHadoop is an extended MapReduce framework that supports global indexing that spatial partitions the data across machines providing orders of magnitude speedup, compared to traditional Hadoop. In this paper, we describe seven alternative partitioning techniques and experimentally study their effect on the quality of the generated index and the performance of range and spatial join queries. We found that using a 1% sample is enough to produce high quality partitions. Also, we found that the total area of partitions is a reasonable measure of the quality of indexes when running spatial join. This study will assist researchers in choosing a good spatial partitioning technique in distributed environments.

Original languageEnglish (US)
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Pages1602-1605
Number of pages4
Edition12
DOIs
StatePublished - 2015
Event3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of
Duration: Sep 11 2006Sep 11 2006

Publication series

NameProceedings of the VLDB Endowment
Number12
Volume8
ISSN (Electronic)2150-8097

Other

Other3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
Country/TerritoryKorea, Republic of
CitySeoul
Period9/11/069/11/06

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