HadoopViz: A MapReduce framework for extensible visualization of big spatial data

Ahmed Eldawy, Mohamed F Mokbel, Christopher Jonathan

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

61 Scopus citations

Abstract

This paper introduces HadoopViz; a MapReduce-based framework for visualizing big spatial data. HadoopViz has three unique features that distinguish it from other techniques. (1) It exposes an extensible interface which allows users to define a new visualization types, e.g., scatter plot, road network, or heat map, by defining five abstract functions, without delving into the implementation details of the MapReduce algorithms. As it is open source, HadoopViz allows algorithm designers to focus on how the data should be visualized rather than performance or scalability issues. (2) HadoopViz is capable of generating big images with giga-pixel resolution by employing a three-phase technique, partition-plot-merge. (3) HadoopViz provides a smoothing functionality which can fuse nearby records together as the image is plotted. This makes it capable of generating more types of images with high quality as compared to existing work. Experimental results on real datasets of up to 14 Billion points show the extensibility, scalability, and efficiency of HadoopViz to handle different visualization types of spatial big data.

Original languageEnglish (US)
Title of host publication2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages601-612
Number of pages12
ISBN (Electronic)9781509020195
DOIs
StatePublished - Jun 22 2016
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: May 16 2016May 20 2016

Publication series

Name2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016

Other

Other32nd IEEE International Conference on Data Engineering, ICDE 2016
Country/TerritoryFinland
CityHelsinki
Period5/16/165/20/16

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