ST-Hadoop: A mapreduce framework for spatio-temporal data

Louai Alarabi, Mohamed Mokbel, Mashaal Musleh

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

8 Citations (Scopus)

Abstract

This paper presents ST-Hadoop; the first full-fledged open-source MapReduce framework with a native support for spatio-temporal data. ST-Hadoop is a comprehensive extension to Hadoop and SpatialHadoop that injects spatio-temporal data awareness inside each of their layers, mainly, language, indexing, and operations layers. In the language layer, ST-Hadoop provides built in spatio-temporal data types and operations. In the indexing layer, ST-Hadoop spatiotemporally loads and divides data across computation nodes in Hadoop Distributed File System in a way that mimics spatio-temporal index structures, which result in achieving orders of magnitude better performance than Hadoop and SpatialHadoop when dealing with spatio-temporal data and queries. In the operations layer, ST-Hadoop shipped with support for two fundamental spatio-temporal queries, namely, spatio-temporal range and join queries. Extensibility of ST-Hadoop allows others to expand features and operations easily using similar approach described in the paper. Extensive experiments conducted on large-scale dataset of size 10 TB that contains over 1 Billion spatio-temporal records, to show that ST-Hadoop achieves orders of magnitude better performance than Hadoop and SpaitalHadoop when dealing with spatio-temporal data and operations. The key idea behind the performance gained in ST-Hadoop is its ability in indexing spatio-temporal data within Hadoop Distributed File System.

Original languageEnglish
Title of host publicationAdvances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings
PublisherSpringer Verlag
Pages84-104
Number of pages21
ISBN (Print)9783319643663
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event15th International Symposium on Spatial and Temporal Databases, SSTD 2017 - Arlington, United States
Duration: 21 Aug 201723 Aug 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10411 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th International Symposium on Spatial and Temporal Databases, SSTD 2017
CountryUnited States
CityArlington
Period21/8/1723/8/17

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Alarabi, L., Mokbel, M., & Musleh, M. (2017). ST-Hadoop: A mapreduce framework for spatio-temporal data. In Advances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings (pp. 84-104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10411 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-64367-0_5