用于地理空间大数据分析的SpatialHadoop和GeoSpark的对比分析

2016 2nd International Conference on Contemporary Computing and Informatics (IC3I) Pub Date : 2016-12-01 DOI:10.1109/IC3I.2016.7918013

R. K. Lenka, Rabindra Kumar Barik, Noopur Gupta, Syed Mohd Ali, A. Rath, Harishchandra Dubey

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引用次数: 48

摘要

在这个所有信息都被存储的数字化世界里，数据呈指数级增长。据估计，数据每两年翻一番。地理空间数据是大数据场景的主要贡献者之一。有许多大数据分析工具。但并不是所有的大数据分析工具都具备处理地理空间大数据的能力。本文讨论了最近流行的两种开源地理空间大数据分析工具，即SpatialHadoop和GeoSpark，它们可以有效地分析和处理地理空间大数据。比较了SpatialHadoop和GeoSpark的架构视图。通过体系结构的比较，根据所使用的数据量和执行时间，总结了这些工具的优缺点。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Comparative analysis of SpatialHadoop and GeoSpark for geospatial big data analytics

In this digitalised world where every information is stored, the data a are growing exponentially. It is estimated that data are doubles itself every two years. Geospatial data are one of the prime contributors to the big data scenario. There are numerous tools of the big data analytics. But not all the big data analytics tools are capabilities to handle geospatial big data. In the present paper, it has been discussed about the recent two popular open source geospatial big data analytical tools i.e. SpatialHadoop and GeoSpark which can be used for analysis and process the geospatial big data in efficient manner. It has compared the architectural view of SpatialHadoop and GeoSpark. Through the architectural comparison, it has also summarised the merits and demerits of these tools according the execution times and volume of the data which has been used.

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2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)

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