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DOI: 10.4230/LIPIcs.GISCIENCE.2018.24

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

Authors: Changlock Choi, Yelin Kim, Youngho Lee, and Seong-Yun Hong

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

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.

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Changlock Choi, Yelin Kim, Youngho Lee, and Seong-Yun Hong. Evaluating Efficiency of Spatial Analysis in Cloud Computing Platforms (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 24:1-24:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{choi_et_al:LIPIcs.GISCIENCE.2018.24,
  author =	{Choi, Changlock and Kim, Yelin and Lee, Youngho and Hong, Seong-Yun},
  title =	{{Evaluating Efficiency of Spatial Analysis in Cloud Computing Platforms}},
  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 =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.24},
  URN =		{urn:nbn:de:0030-drops-93521},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.24},
  annote =	{Keywords: spatial analysis, parallel computing, cloud services}
}

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

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