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

Ahmed Eldawy, Mohamed Mokbel, Christopher Jonathan

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

27 Citations (Scopus)

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
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
Publication statusPublished - 22 Jun 2016
Externally publishedYes
Event32nd IEEE International Conference on Data Engineering, ICDE 2016 - Helsinki, Finland
Duration: 16 May 201620 May 2016

Other

Other32nd IEEE International Conference on Data Engineering, ICDE 2016
CountryFinland
CityHelsinki
Period16/5/1620/5/16

Fingerprint

Scalability
Visualization
Electric fuses
Pixels
MapReduce
Big data
Hot Temperature
Open source
Smoothing
Functionality
Road network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management

Cite this

Eldawy, A., Mokbel, M., & Jonathan, C. (2016). HadoopViz: A MapReduce framework for extensible visualization of big spatial data. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016 (pp. 601-612). [7498274] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDE.2016.7498274

HadoopViz : A MapReduce framework for extensible visualization of big spatial data. / Eldawy, Ahmed; Mokbel, Mohamed; Jonathan, Christopher.

2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 601-612 7498274.

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

Eldawy, A, Mokbel, M & Jonathan, C 2016, HadoopViz: A MapReduce framework for extensible visualization of big spatial data. in 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016., 7498274, Institute of Electrical and Electronics Engineers Inc., pp. 601-612, 32nd IEEE International Conference on Data Engineering, ICDE 2016, Helsinki, Finland, 16/5/16. https://doi.org/10.1109/ICDE.2016.7498274
Eldawy A, Mokbel M, Jonathan C. HadoopViz: A MapReduce framework for extensible visualization of big spatial data. In 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 601-612. 7498274 https://doi.org/10.1109/ICDE.2016.7498274
Eldawy, Ahmed ; Mokbel, Mohamed ; Jonathan, Christopher. / HadoopViz : A MapReduce framework for extensible visualization of big spatial data. 2016 IEEE 32nd International Conference on Data Engineering, ICDE 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 601-612
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