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R*-Grove: Balanced Spatial Partitioning for Large-Scale Datasets.

Tin Vu1, Ahmed Eldawy1

  • 1Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, United States.

Frontiers in Big Data
|March 11, 2021
PubMed
Summary

R*-Grove efficiently partitions big spatial data, creating high-quality, load-balanced datasets. This novel method improves spatial query processing and integrates with big data platforms like Apache Spark.

Keywords:
R*-Grovebig spatial dataindex optimizationpartitioningquery processing

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Area of Science:

  • Computer Science
  • Data Science
  • Geographic Information Systems

Background:

  • Big spatial data systems require efficient data partitioning across machines.
  • Existing methods using R-trees or Quad-trees struggle with high-quality, load-balanced partitions.
  • Challenges include maintaining spatial quality and leveraging distributed processing.

Purpose of the Study:

  • To propose a novel spatial partitioning method, R*-Grove.
  • To address the limitations of existing big spatial partitioning techniques.
  • To achieve high-quality partitions with excellent load balance and block utilization.

Main Methods:

  • Developed a new partitioning algorithm, R*-Grove.
  • Designed R*-Grove for seamless integration with big data platforms (e.g., Apache Spark, Hadoop).
  • Evaluated R*-Grove's performance against existing methods.

Main Results:

  • R*-Grove partitions very large spatial datasets effectively.
  • Achieved high spatial quality and excellent load balance.
  • Demonstrated superior performance in spatial query processing compared to existing techniques.

Conclusions:

  • R*-Grove offers a superior solution for big spatial data partitioning.
  • The method enhances spatial query performance and integrates easily with big data platforms.
  • R*-Grove is available as open-source, promoting community adoption.