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Reseach Article

Parallel and Distributed GIS for Processing Geo-data: An Overview

by Jhummarwala Abdul, M.b. Potdar, Prashant Chauhan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 16
Year of Publication: 2014
Authors: Jhummarwala Abdul, M.b. Potdar, Prashant Chauhan
10.5120/18602-9881

Jhummarwala Abdul, M.b. Potdar, Prashant Chauhan . Parallel and Distributed GIS for Processing Geo-data: An Overview. International Journal of Computer Applications. 106, 16 ( November 2014), 9-16. DOI=10.5120/18602-9881

@article{ 10.5120/18602-9881,
author = { Jhummarwala Abdul, M.b. Potdar, Prashant Chauhan },
title = { Parallel and Distributed GIS for Processing Geo-data: An Overview },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 16 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 9-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number16/18602-9881/ },
doi = { 10.5120/18602-9881 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:39:32.705672+05:30
%A Jhummarwala Abdul
%A M.b. Potdar
%A Prashant Chauhan
%T Parallel and Distributed GIS for Processing Geo-data: An Overview
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 16
%P 9-16
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Geographic Information System (GIS) is a collection of applications whose tasks include (collaborating with other systems and) gathering geographic data, store and process spatio–temporal data (geo-data) and share the derived geographic knowledge with the users and other applications. Some of the most important routine applications of GIS are spatial analysis, digital elevation model (DEM) analysis such as line of sight and slope computations, watershed and viewshed analysis, etc. GIS has became quite an important tool for geospatial sciences and has gone beyond typical tasks of mapping to performing complex spatio-temporal analysis and operations. The number of users relying upon Decision and Support Systems (DSS) built upon GIS has increased as a result of the availability of very high resolution satellite imagery and integration of spatial data and analyses with GIS packages which now satisfies the needs of many and is not just used for specialized operations. Moreover, Global Positioning Systems (GPS) in a range of mobile devices and sensors which includes updates in very short intervals has led to geo-information explosion. Parallel and Distributed Computing systems are now essential for computing over such huge amount of data and deliver faster results. The focus on development should thus be shifted from traditional GIS to Parallel and Distributed GIS as the traditional GIS systems have become quite mature and saturated while technologies such as MPI (Message Passing Interface) and GPGPUs (General Purpose Graphics Programming Units) can be readily utilized for faster geo-data processing. The performance improved using recently developed technologies such as CUDA (Nvidia GPUs), OpenCL (ATI GPUs) and Intel's Xeon Phi co-processors could be as much as ten times if not more compared to traditional Geographic Information Systems.

References
  1. The World-Wide Earthquake Locator. http://tsunami. geo. ed. ac. uk/local-bin/quakes/mapscript/home. pl
  2. Simple Features for OLE/COM. http://www. opengeospatial. org/standards/sfo
  3. PostGIS - Spatial and Geographic Objects for PostgreSQL. http://postgis. net/
  4. Li Yingcheng, Li Ling. 2012 Research On Spatial Database Design and Tuning Based on Oracle and ARCSDE, International Society for Photogrammetry and Remote Sensing, Volume XXXV Part B4, 2004. XXth ISPRS Congress Technical Commission IV.
  5. The Open Data project. http://data. nasa. gov/about/
  6. A. Aji, F. Wang, and J. H. Saltz. 2012 Towards Building A High Performance Spatial Query System for Large Scale Medical Imaging Data. In SIGSPATIAL/GIS pages 309–318.
  7. A. Aji, F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, and J. Saltz. 2013 Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce, Proceedings of the VLDB Endowment Vol 6.
  8. Jeffrey Dean, Sanjay Ghemawat 2008 MapReduce: Simplified Data Processing on Large Clusters, Communications of the ACM, Volume 51, Issue 1.
  9. Hadoop at Yahoo!. https://developer. yahoo. com/hadoop/
  10. The Apache Software Foundation. http://apache. org/
  11. A. Aji, F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, and J. Saltz. 2013 Demonstration of Hadoop-GIS: A Spatial Data Warehousing System Over MapReduce, Proceedings VLDB Endowment.
  12. Jingren Zhou, Nicolas Bruno, Ming-Chuan Wu, Per-Ake Larson 2012 SCOPE: Parallel databases meet MapReduce, The VLDB Journal, Vol 21, Issue 5.
  13. CLUP GIS Guidebook: A Guide to Comprehensive Land Use Data Management http://www. cookbook. hlurb. gov. ph/book/
  14. Esri Grid format http://resources. arcgis. com/en/help/main/10. 1/index. html
  15. Yonggang Wang, Sheng Wang, Daliang Zhou. 2009 Retrieving and Indexing Spatial Data in the Cloud Computing Environment, Lecture Notes in Computer Science Volume 5931, 322-331.
  16. Michael Paul Deskevich, Method and system for processing raster scan images. http://www. google. com/patents/US8731309
  17. Ahmed Eldawy, Mohamed F. Mokbel. 2013 A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data, Proceedings of the VLDB Endowment, Vol 6 Issue 12.
  18. Ahmed Eldawy and Mohamed Mokbel. 2014 Pigeon: A Spatial MapReduce Language, In Proceedings of the IEEE International Conference on Data Engineering.
  19. Ahmed Eldawy, Yuan Li, Mohamed F. Mokbel. 2013 CG_Hadoop: Computational Geometry in MapReduce, Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
  20. Hadoop 2 On Windows https://wiki. apache. org/hadoop/Hadoop2OnWindows
  21. Vavilapalli V. K. , Murthy A. C. , Douglas C. , Agarwal S. , Konar M. 2013 Apache Hadoop YARN: Yet Another Resource Negotiator, Proceedings of the 4th annual Symposium on Cloud Computing.
  22. The Hadoop Distributed File System: Architecture and Design https://svn. eu. apache. org/repos/asf/hadoop/common/tags/release-0. 16. 3/docs/hdfs_design. pdf
  23. Fay Chang, Jeffrey Dean, Sanjay Ghemawat et al. 2008 Bigtable: A Distributed Storage System for Structured Data, ACM Transactions on Computer Systems (TOCS), Volume 26 Issue 2
Index Terms

Computer Science
Information Sciences

Keywords

GIS GPS DSS spatio–temporal data spatio–temporal analysis Digital Elevation Model (DEM) geospatial data geodata geoprocessing OGC Message Passing Interface (MPI) GPU CUDA.