A demonstration of ST-Hadoop: A MapReduce framework for big spatio-temporal data

Louai Alarabi, Mohamed F Mokbel

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations


This demo presents ST-Hadoop; the first full-fledged open-source MapReduce framework with a native support for spatio-temporal data. ST-Hadoop injects spatio-temporal awareness in the Hadoop base code, which results in achieving order(s) of magnitude better performance than Hadoop and SpatialHadoop when dealing with spatio-temporal data and queries. The key idea behind ST-Hadoop is its ability in indexing spatio-temporal data within Hadoop Distributed File System (HDFS). A real system prototype of STHadoop, running on a local cluster of 24 machines, is demonstrated with two big-spatio-temporal datasets of Twitter and NYC Taxi data, each of around one billion records.

Original languageEnglish (US)
Pages (from-to)1961-1964
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number12
StatePublished - Aug 1 2017
Event43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany
Duration: Aug 28 2017Sep 1 2017

Bibliographical note

Publisher Copyright:
© 2017 VLDB.


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