CG-Hadoop

Computational geometry in MapReduce

Ahmed Eldawy, Yuan Li, Mohamed Mokbel, Ravi Janardan

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

50 Citations (Scopus)

Abstract

Hadoop, employing the MapReduce programming paradigm, has been widely accepted as the standard framework for analyzing big data in distributed environments. Unfortunately, this rich framework was not truly exploited towards processing large-scale computational geometry operations. This paper introduces CG-Hadoop; a suite of scalable and efficient MapReduce algorithms for various fundamental computational geometry problems, namely, polygon union, skyline, convex hull, farthest pair, and closest pair, which present a set of key components for other geometric algorithms. For each computational geometry operation, CG-Hadoop has two versions, one for the Apache Hadoop system and one for the SpatialHadoop system; a Hadoop-based system that is more suited for spatial operations. These proposed algorithms form a nucleus of a comprehensive MapReduce library of computational geometry operations. Extensive experimental results on a cluster of 25 machines of datasets up to 128GB show that CG-Hadoop achieves up to 29x and 260x better performance than traditional algorithms when using Hadoop and SpatialHadoop systems, respectively.

Original languageEnglish
Title of host publication21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013
Pages284-293
Number of pages10
DOIs
Publication statusPublished - 1 Dec 2013
Externally publishedYes
Event21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013 - Orlando, FL, United States
Duration: 5 Nov 20138 Nov 2013

Other

Other21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013
CountryUnited States
CityOrlando, FL
Period5/11/138/11/13

Fingerprint

Computational geometry
MapReduce
Computational Geometry
geometry
Skyline
Geometric Algorithms
Distributed Environment
polygon
hull
Convex Hull
Polygon
Nucleus
Union
Programming
Paradigm
Experimental Results
Processing
Framework

Keywords

  • geometric algorithms
  • Hadoop
  • MapReduce

ASJC Scopus subject areas

  • Earth-Surface Processes
  • Computer Science Applications
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

Cite this

Eldawy, A., Li, Y., Mokbel, M., & Janardan, R. (2013). CG-Hadoop: Computational geometry in MapReduce. In 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013 (pp. 284-293) https://doi.org/10.1145/2525314.2525349

CG-Hadoop : Computational geometry in MapReduce. / Eldawy, Ahmed; Li, Yuan; Mokbel, Mohamed; Janardan, Ravi.

21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013. 2013. p. 284-293.

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

Eldawy, A, Li, Y, Mokbel, M & Janardan, R 2013, CG-Hadoop: Computational geometry in MapReduce. in 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013. pp. 284-293, 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013, Orlando, FL, United States, 5/11/13. https://doi.org/10.1145/2525314.2525349
Eldawy A, Li Y, Mokbel M, Janardan R. CG-Hadoop: Computational geometry in MapReduce. In 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013. 2013. p. 284-293 https://doi.org/10.1145/2525314.2525349
Eldawy, Ahmed ; Li, Yuan ; Mokbel, Mohamed ; Janardan, Ravi. / CG-Hadoop : Computational geometry in MapReduce. 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2013. 2013. pp. 284-293
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