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Related Experiment Video

Updated: Feb 5, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Quadrant-Based Minimum Bounding Rectangle-Tree Indexing Method for Similarity Queries over Big Spatial Data in HBase.

Bumjoon Jo1, Sungwon Jung2

  • 1Department of Computer Science and Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, Korea. bumjoonjo@sogang.ac.kr.

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|September 12, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new hierarchical indexing structure, the quadrant-based minimum bounding rectangle (QbMBR) tree, for efficient spatial query processing in HBase. The QbMBR tree improves data proximity and reduces storage, outperforming existing methods.

Keywords:
HBaseNoSQLQbMBR treebig spatial datakNN queryrange queryspatial data indexing

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Area of Science:

  • Computer Science
  • Data Management
  • Geographic Information Systems

Background:

  • Effective searching of big spatial data is crucial due to the proliferation of mobile devices and sensors.
  • NoSQL databases like HBase are used for storing large spatial datasets, but their one-dimensional indexing limits spatial data retrieval.
  • Linearization techniques for HBase indexing do not guarantee spatial proximity, leading to inefficiencies.

Purpose of the Study:

  • To propose a novel hierarchical indexing structure, the quadrant-based minimum bounding rectangle (QbMBR) tree, for enhanced spatial query processing in HBase.
  • To develop query-processing algorithms that leverage the QbMBR tree for range and kNN queries.
  • To improve the efficiency and accuracy of spatial data retrieval in NoSQL databases.

Main Methods:

  • Developed a hierarchical indexing structure, the quadrant-based minimum bounding rectangle (QbMBR) tree.
  • Grouped and indexed spatial objects using QbMBR for more precise data organization.
  • Proposed two query-processing algorithms for range and kNN queries, incorporating index node prefetching.

Main Results:

  • The QbMBR tree provides more selective spatial query processing.
  • The proposed indexing method reduces the storage space required for indexing spatial data.
  • Experimental analysis shows significant improvements in query execution times compared to existing methods.

Conclusions:

  • The QbMBR tree is an effective hierarchical indexing structure for spatial query processing in HBase.
  • The proposed algorithms significantly enhance the performance of range and kNN queries.
  • This approach offers a superior solution for managing and querying big spatial data in NoSQL environments.