Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Spatial Queries in the Cloud

  • Ablimit AjiEmail author
  • Hoang Vo
  • Fusheng Wang
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80713


Spatial queries in the cloud refer to processing of spatial queries on a distributed and interconnected network of computers that provide computation, storage, and resource management capabilities elastically in large scale. Resources in the cloud can be allocated on demand, and customers only pay for what they use. Cloud offers a number of query processing infrastructure and services ranging from parallel spatial database systems to MapReduce-based systems. Common spatial queries of interest include range queries, joins, and k-nearest neighbor queries.

Historical Background

Support of high-performance queries on large volumes of spatial data becomes increasingly important in many application domains, including geo-spatial problems in numerous fields, location-based services, and emerging scientific applications that are increasingly data and compute intensive. Past research efforts fall into three major directions toward improving spatial query performance: (i) algorithmic...

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Recommended Reading

  1. 1.
    Aji, A, Wang, F, Saltz, JH. Towards building a high performance spatial query system for large scale medical imaging data. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems; 2012. p. 309–18.Google Scholar
  2. 2.
    Aji, A, Wang F, Vo H, Lee R, Liu Q, Zhang X, Saltz J. Hadoop GIS: a high performance spatial data warehousing system over mapreduce. Proc VLDB Endowment. 2013;6(11):1009–20.CrossRefGoogle Scholar
  3. 3.
    Akdogan A, Demiryurek U, Banaei-Kashani F, Shahabi C. Voronoi-based geospatial query processing with mapreduce. In: Proceedings of the 2010 IEEE 2nd International Conference on Cloud Computing Technology and Science; 2010. p. 9–16.Google Scholar
  4. 4.
    Eldawy A, Mokbel MF. SpatialHadoop: a MapReduce framework for spatial data. In: Proceedings of the 31st International Conference on Data Engineering; 2015.Google Scholar
  5. 5.
    Lu J, Guting RH. Parallel secondo: boosting database engines with hadoop. In: Proceedings of the 18th IEEE International Conference on Parallel and Distributed Systems; 2012. p. 738–43.Google Scholar
  6. 6.
    Lu W, Shen Y, Chen S, Ooi BC. Efficient processing of k nearest neighbor joins using mapreduce. Proc VLDB Endowment. 2012;5(10):1016–27.CrossRefGoogle Scholar
  7. 7.
    Nishimura S, Das S, Agrawal D, Abbadi AE. MD-HBase: a scalable multi-dimensional data infrastructure for location aware services. Proceedings of the 12th IEEE International Conference on Mobile Data Management; 2011. p. 7–16.Google Scholar
  8. 8.
    Ray S, Simion B, Brown AD, Johnson R. A parallel spatial data analysis infrastructure for the cloud. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems; 2013. p. 284–93.Google Scholar
  9. 9.
    Zhang C, Li F, Jestes J. Efficient parallel kNN joins for large data in MapReduce. In: Proceedings of the 15th International Conference on Extending Database Technology; 2012. p. 38–49.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Analytics LabHewlett PackardPalo AltoUSA
  2. 2.Computer ScienceStony Brook UniversityStony BrookUSA
  3. 3.Stony Brook UniversityStony BrookUSA

Section editors and affiliations

  • Dimitris Papadias
    • 1
  1. 1.Dept. of Computer Science and Eng.Hong Kong Univ. of Science and TechnologyKowloonHong Kong SAR