Big Data Storage Techniques for Spatial Databases: Implications of Big Data Architecture on Spatial Query Processing

  • Roger Frye
  • Mark McKenneyEmail author
Part of the Studies in Big Data book series (SBD, volume 8)


Today, a large amount of data is being collected and stored. Data that has grown beyond traditional data management solutions has come to be known as big data. Solutions such as Hadoop have emerged to address the big data problem. However, spatial data presents its own challenges to storage and processing. Researchers have taken various approaches with Hadoop to handle spatial data efficiently. The approaches includes multi-stage map/reduce algorithms, generating on-demand indexes, and maintaining persistent indexes. This paper reviews the various approaches, categorizes the spatial queries reported in the testing, summarizes results, and identifies strengths and weaknesses with each approach.


Spatial data Big data MapReduce Query processing 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Southern Illinois UniversityEdwardsvilleUSA

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