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

Louai Alarabi, Mohamed F. Mokbel, Mashaal Musleh

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

12 Scopus citations


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 (US)
Title of host publicationAdvances in Spatial and Temporal Databases - 15th International Symposium, SSTD 2017, Proceedings
EditorsWei-Shinn Ku, Agnes Voisard, Haiquan Chen, Chang-Tien Lu, Siva Ravada, Matthias Renz, Yan Huang, Michael Gertz, Liang Tang, Chengyang Zhang, Erik Hoel, Xiaofang Zhou
PublisherSpringer Verlag
Number of pages21
ISBN (Print)9783319643663
StatePublished - 2017
Event15th International Symposium on Spatial and Temporal Databases, SSTD 2017 - Arlington, United States
Duration: Aug 21 2017Aug 23 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


Other15th International Symposium on Spatial and Temporal Databases, SSTD 2017
Country/TerritoryUnited States

Bibliographical note

Funding Information:
This work is partially supported by the National Science Foundation, USA, under Grants IIS-1525953, CNS-1512877, IIS-1218168, and by a scholarship from the College of Computers & Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.

Publisher Copyright:
© Springer International Publishing AG 2017.


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