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A hybrid spatio-temporal data indexing method for trajectory databases.

Shengnan Ke1, Jun Gong2, Songnian Li3

  • 1School of Software, Jiangxi Normal University, Nanchang 330022, China. consnan@126.com.

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Summary
This summary is machine-generated.

This study introduces HBSTR-tree, a novel hybrid indexing method for efficiently managing complex indoor and outdoor trajectory data. HBSTR-tree improves spatio-temporal data indexing, enhancing generation efficiency and query performance.

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Area of Science:

  • Geographic Information Science
  • Computer Science
  • Data Management

Background:

  • Indoor and outdoor positioning sensors generate vast amounts of trajectory data, posing challenges for efficient spatio-temporal indexing.
  • Existing methods struggle with the semantic complexity and sheer volume of modern trajectory datasets.

Purpose of the Study:

  • To propose a novel spatio-temporal data indexing method, HBSTR-tree, designed to handle large-scale and semantically complex trajectory data.
  • To enhance the efficiency of index generation and query performance for trajectory data management.

Main Methods:

  • Developed HBSTR-tree, a hybrid index structure combining spatio-temporal R-tree, B*-tree, and Hash table.
  • Grouped consecutive trajectory points into nodes based on spatio-temporal semantics for insertion into the R-tree.
  • Utilized a Hash table to manage leaf nodes and reduce insertion frequency.
  • Introduced a new spatio-temporal interval criterion and node-choosing sub-algorithm for R-tree optimization.
  • Implemented a B*-tree sub-index for efficient querying of specific object trajectories.
  • Proposed a NoSQL-based database storage scheme for cloud storage.

Main Results:

  • HBSTR-tree demonstrates improved index generation efficiency compared to existing methods.
  • Experimental results show superior query performance and broader query type support for HBSTR-tree.
  • The hybrid approach effectively manages semantically complex and large volumes of trajectory data.

Conclusions:

  • HBSTR-tree offers a robust and efficient solution for indexing and querying large-scale spatio-temporal trajectory data.
  • The proposed method significantly outperforms traditional approaches like TB*-tree in key performance metrics.
  • This indexing strategy is well-suited for location-based services and location intelligence applications.