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Assessing NoSql Approaches for Spatial Big Data Management

  • El Hassane NassifEmail author
  • Hajji Hicham
  • Reda Yaagoubi
  • Hassan Badir
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 92)

Abstract

Since the advent of social networks, Iot and Smartphones, spatial data are taken by storm by the Big Data phenomenon. Their management and analysis is a real challenge for traditional geographic information systems. Indeed, these solutions don’t respond effectively to big data constraints as they still rely on relational databases to manage and process spatial data.

In this paper, we compare, from a qualitative and quantitative point of view, three families of NoSql databases with geospatial features: Key-value, column and document oriented. We explore the ways offered by these three NoSql paradigms for efficient management and analysis of massive spatial vector data and then we analyze the performance of two of them. The empirical evaluation is performed on two clusters based on an open data datasets and gives some advantages and limitations of these approaches.

Keywords

Spatial Big Data Distributed spatial computing Accumulo Elasticsearch Redis Spatial Vector 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • El Hassane Nassif
    • 1
    Email author
  • Hajji Hicham
    • 1
  • Reda Yaagoubi
    • 1
  • Hassan Badir
    • 2
  1. 1.Ecole ESGIT, IAV H2RabatMorocco
  2. 2.Ecole ENSAT TangerTangierMorocco

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