A demonstration of hadoopviz: An extensible mapreduce system for visualizing big spatial data

Ahmed Eldawy, Mohamed F. Mokbel, Christopher Jonathan

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

Abstract

This demonstration presents HadoopViz; an extensible MapReduce-based system for visualizing Big Spatial Data. HadoopViz has two main unique features that distinguish it from other techniques. (1) It provides an extensible interface that allows users to visualize various types of data by defining five abstract functions, without delving into the details of the MapReduce algorithms. We show how it is used to create four types of visualizations, namely, scatter plot, road network, frequency heat map, and temperature heat map. (2) HadoopViz is capable of generating big images with giga-pixel resolution by employing a three-phase approach of partitioning, rasterize, and merging. HadoopViz generates single and multi-level images, where the latter allows users to zoom in/out to get more/less details. Both types of images are generated with a very high resolution using the extensible and scalable framework of HadoopViz.

Original languageEnglish (US)
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Pages1896-1899
Number of pages4
Edition12
DOIs
StatePublished - Jan 1 2015
Event3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006 - Seoul, Korea, Republic of
Duration: Sep 11 2006Sep 11 2006

Publication series

NameProceedings of the VLDB Endowment
Number12
Volume8
ISSN (Electronic)2150-8097

Other

Other3rd Workshop on Spatio-Temporal Database Management, STDBM 2006, Co-located with the 32nd International Conference on Very Large Data Bases, VLDB 2006
CountryKorea, Republic of
CitySeoul
Period9/11/069/11/06

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Eldawy, A., Mokbel, M. F., & Jonathan, C. (2015). A demonstration of hadoopviz: An extensible mapreduce system for visualizing big spatial data. In Proceedings of the VLDB Endowment (12 ed., pp. 1896-1899). (Proceedings of the VLDB Endowment; Vol. 8, No. 12). Association for Computing Machinery. https://doi.org/10.14778/2824032.2824095