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

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



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Author Details

Changlock Choi
  • Department of Geography, Kyung Hee University, Seoul, South Korea
Yelin Kim
  • Department of Geography, Kyung Hee University, Seoul, South Korea
Youngho Lee
  • Department of Geography, Kyung Hee University, Seoul, South Korea
Seong-Yun Hong
  • Department of Geography, Kyung Hee University, Seoul, South Korea

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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)
https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.24

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.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • spatial analysis
  • parallel computing
  • cloud services

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References

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