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

Louai Alarabi, Mohamed F. Mokbel

Research output: Contribution to journalConference article

6 Citations (Scopus)

Abstract

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
Pages (from-to)1961-1964
Number of pages4
JournalProceedings of the VLDB Endowment
Volume10
Issue number12
DOIs
Publication statusPublished - 1 Aug 2017
Event43rd International Conference on Very Large Data Bases, VLDB 2017 - Munich, Germany
Duration: 28 Aug 20171 Sep 2017

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ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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