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High-Level Languages for Geospatial Analysis of Big Data: Strengths and Weaknesses
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Author(s): Symphorien Monsia (LTSIRS Laboratory, National Engineering School, Tunis, Tunisia)and Sami Faiz (LTSIRS Laboratory, National Engineering School, Tunis, Tunisia)
Copyright: 2021
Pages: 20
Source title:
Interdisciplinary Approaches to Spatial Optimization Issues
Source Author(s)/Editor(s): Sami Faiz (University of Tunis El Manar, Tunis, Tunisia)and Soumaya Elhosni (University of Tunis El Manar, Tunis, Tunisia)
DOI: 10.4018/978-1-7998-1954-7.ch004
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Abstract
In recent years, big data has become a major concern for many organizations. An essential component of big data is the spatio-temporal data dimension known as geospatial big data, which designates the application of big data issues to geographic data. One of the major aspects of the (geospatial) big data systems is the data query language (i.e., high-level language) that allows non-technical users to easily interact with these systems. In this chapter, the researchers explore high-level languages focusing in particular on the spatial extensions of Hadoop for geospatial big data queries. Their main objective is to examine three open source and popular implementations of SQL on Hadoop intended for the interrogation of geospatial big data: (1) Pigeon of SpatialHadoop, (2) QLSP of Hadoop-GIS, and (3) ESRI Hive of GIS Tools for Hadoop. Along the same line, the authors present their current research work toward the analysis of geospatial big data.
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