CG_Hadoop: computational geometry in MapReduce

@inproceedings{Eldawy2013CG_HadoopCG,
  title={CG_Hadoop: computational geometry in MapReduce},
  author={Ahmed Eldawy and Yuan Li and Mohamed F. Mokbel and Ravi Janardan},
  booktitle={SIGSPATIAL/GIS},
  year={2013}
}
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… CONTINUE READING
Highly Cited
This paper has 64 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 44 extracted citations

Simba: Efficient In-Memory Spatial Analytics

SIGMOD Conference • 2016
View 11 Excerpts
Highly Influenced

On Spatial Joins in MapReduce

SIGSPATIAL/GIS • 2017
View 2 Excerpts

Optimizing Spatial Queries in MapReduce

SIGMOD SRC '17 • 2017
View 1 Excerpt

64 Citations

01020'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 64 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…