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Reseach Article

Commentary on Polygon Overlay using MapReduce and Random Forest

by Prerna Rai, Prativa Rai, Bijoy Chhetri
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 152 - Number 7
Year of Publication: 2016
Authors: Prerna Rai, Prativa Rai, Bijoy Chhetri
10.5120/ijca2016911890

Prerna Rai, Prativa Rai, Bijoy Chhetri . Commentary on Polygon Overlay using MapReduce and Random Forest. International Journal of Computer Applications. 152, 7 ( Oct 2016), 5-9. DOI=10.5120/ijca2016911890

@article{ 10.5120/ijca2016911890,
author = { Prerna Rai, Prativa Rai, Bijoy Chhetri },
title = { Commentary on Polygon Overlay using MapReduce and Random Forest },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2016 },
volume = { 152 },
number = { 7 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = { 5-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume152/number7/26329-2016911890/ },
doi = { 10.5120/ijca2016911890 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:57:31.328687+05:30
%A Prerna Rai
%A Prativa Rai
%A Bijoy Chhetri
%T Commentary on Polygon Overlay using MapReduce and Random Forest
%J International Journal of Computer Applications
%@ 0975-8887
%V 152
%N 7
%P 5-9
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Geographical Information System is known for its analytical capability. The spatial analysis function of GIS is the major factors that distinguish the GIS from other information system. This analysis provides with the functions such as spatial interpolation, buffering, and overlay operations [2]. Among all these functions, the focus on the study here is on overlay operation for polygon on polygon, using MapReduce and Random Forest. The capability to overlay multiple data layers in a vertical fashion is the most required and common technique in geographic data processing. In this study, we focus on the comparison between polygon overlay operation with and without spatial index in MapReduce and also proposed method for polygon overlay using Random Forest and its comparison with MapReduce algorithm.

References
  1. Yong Wang · Zhenling Liu · Hongyan Liao ·Chengjun Li, Improving the performance of GIS polygon overlay computation with MapReduce for spatial big data processing, Springer Science+Business , Accepted: 10 January 2015Media New York 2015, DOI 10.1007/s10586-015-0428-x
  2. Basudeb Bhatta,”Remote Sensing and GIS”, Second Edition in 2011, ISBN-10:0-19-807239, 554-558.
  3. Puri, Satish, "Efficient Parallel and Distributed Algorithms for GIS Polygon Overlay Processing." Dissertation, Georgia State University, 2015. http://scholarworks.gsu.edu/cs_diss/98 accepted for inclusion in Computer Science Dissertations by an authorized administrator of ScholarWorks @ Georgia State University.
  4. Puri, S., Agarwal, D., He, X., Prasad, S.K.: MapReduce algorithms for GIS polygon overlay processing. In: Proceedings of the 27th IEEE International Parallel and Distributed Processing Symposium Workshops and PhD Forum (IPDPSW), pp. 1009–1016 (2013)
  5. Guo , B. Palanisamy, H. A. Karimi ,A Distributed Polygon Retrieval Algorithm using MapReduce, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-4/W2, 2015 International Workshop on Spatiotemporal Computing, 13–15 July 2015, Fairfax, Virginia, USA
  6. Dinesh Agarwal, Satish Puri, Xi He, and Sushil K. Prasad1, A system for GIS polygonal overlay computationon Linux Cluster - An experience and performance Report, 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
  7. Fangju Wang, A Parallel Intersection Algorithm for Vector Polygon Overlay, 0272- 17.16.93/0300-0074 IEEE Computer Graphics & Applications
  8. Cheng, C.: Spatial Database Management System. Science Press, Beijing (2012)
  9. Processing of massive data, MapReduce New Trends In Distributed Systems MSc Software and Systems
  10. Random Forests Leo Breiman and Adele Cutler. www.stat. berkeley. edu/breiman/ RandomForests date: 29.04.16
  11. Random forests and big data Robin Genuer, Jean-Michel Poggi, Christine Tuleau-Malot. Random forests and big data. 47`eme Journ´ees de Statistique de la SFdS, Jun 2015, Lille, France. 2015. HAL Id: hal-01160643 https://hal.archives-ouvertes.fr/hal-01160643 Submitted on 8 Jun 2015
  12. A Scalable Random Forest Algorithm Based on MapReduce Jiawei Hanl,Yanheng Liul,College of Computer Science and Technology, Jilin University 2,Changchun University,Jilin Province, ChinaJason.hjw@gmail.com978-1-4673-5000-6/13.2013 IEEE
  13. On the use of MapReduce for imbalanced big data using Random Forest Sara del Río, Victoria López, José Manuel Benítez, Francisco Herrera Dept. of Computer Science and Artificial Intelligence, CITIC-UGR, University of Granada, Granada, Spain. Article history: Received 28 March 2013Received in revised form 21 February 2014 Accepted 11 March 2014 by Elsevier.
  14. Random forests, aka decision forests, and ensemble methods Published on Feb 21, 2013
Index Terms

Computer Science
Information Sciences

Keywords

MapReduce Polygon Overlay Random Forest Spatial index