License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.24
URN: urn:nbn:de:0030-drops-93521
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9352/
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Choi, Changlock ; Kim, Yelin ; Lee, Youngho ; Hong, Seong-Yun

Evaluating Efficiency of Spatial Analysis in Cloud Computing Platforms (Short Paper)

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LIPIcs-GISCIENCE-2018-24.pdf (0.3 MB)

Abstract

The increase of high-resolution spatial data and methodological developments in recent years has enabled a detailed analysis of individuals' experience in space and over time. However, despite the increasing availability of data and technological advances, such individual-level analysis is not always possible in practice because of its computing requirements. To overcome this limitation, there has been a considerable amount of research on the use of high-performance, public cloud computing platforms for spatial analysis and simulation. In this paper, we aim to evaluate the efficiency of spatial analysis in cloud computing platforms. We compared the computing speed for calculating the Moran's I index between a local machine and spot instances on clouds, and our results demonstrated that there could be significant improvements in terms of computing time when the analysis was performed parallel on clouds.

BibTeX - Entry

@InProceedings{choi_et_al:LIPIcs:2018:9352,
  author =	{Changlock Choi and Yelin Kim and Youngho Lee and Seong-Yun Hong},
  title =	{{Evaluating Efficiency of Spatial Analysis in Cloud Computing Platforms (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{24:1--24:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9352},
  URN =		{urn:nbn:de:0030-drops-93521},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.24},
  annote =	{Keywords: spatial analysis, parallel computing, cloud services}
}

Keywords: spatial analysis, parallel computing, cloud services
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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