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A Cost-Efficient Method for Big Geospatial Data on Public Cloud Providers

Authors:
Joao Bachiega Junior
Marco Antonio Sousa Reis
Aletéia Patrícia Favacho de Araújo
Maristela Holanda

Keywords: Big geospatial data; Hadoop; SpatialHadoop; Spatial Cloud Computing

Abstract:
The rise of big geospatial data creates the need for an environment with powerful computational resources to process this large amount of geographical information. Spatial Cloud Computing is a solution to this problem as it offers facilities to overcome the challenges of a big data environment, providing significant computer power and vast storage. However, the software to process this data requires great performance capacity. These requirements are met by SpatialHadoop, a fully-fledged MapReduce framework with native support for spatial data. This paper presents a cost-efficient method for processing geospatial data on public cloud providers, optimizing the number of data nodes in a Hadoop cluster according to dataset size. Tests have proven that it can optimize the use of computational resources for available SpatialHadoop datasets.

Pages: 25 to 31

Copyright: Copyright (c) IARIA, 2017

Publication date: March 19, 2017

Published in: conference

ISSN: 2308-393X

ISBN: 978-1-61208-539-5

Location: Nice, France

Dates: from March 19, 2017 to March 23, 2017