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GL-Tree: A Hierarchical Tree Structure for Efficient Retrieval of Massive Geographic Locations

Sensors (Basel, Switzerland), 2023-02, Vol.23 (4), p.2245 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2023 by the authors. 2023 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s23042245 ;PMID: 36850842

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  • Title:
    GL-Tree: A Hierarchical Tree Structure for Efficient Retrieval of Massive Geographic Locations
  • Author: Liu, Bin ; Zhang, Chunyong ; Xin, Yang
  • Subjects: Access control ; Algorithms ; Communication ; Data encryption ; Datasets ; Geographical locations ; Geohash code ; Intervals ; Location based services ; location privacy protection ; location privacy space index ; Privacy ; Privacy, Right of ; Queries ; Semantics ; Servers ; User behavior
  • Is Part Of: Sensors (Basel, Switzerland), 2023-02, Vol.23 (4), p.2245
  • Description: Location-based application services and location privacy protection solutions are often required for the storage, management, and efficient retrieval of large amounts of geolocation data for specific locations or location intervals. We design a hierarchical tree-like organization structure, GL-Tree, which enables the storage, management, and retrieval of massive location data and satisfies the user's location-hiding requirements. We first use Geohash encoding to convert the two-dimensional geospatial coordinates of locations into one-dimensional strings and construct the GL-Tree based on the Geohash encoding principle. We gradually reduce the location intervals by extending the length of the Geohash code to achieve geospatial grid division and spatial approximation of user locations. The hierarchical tree structure of GL-Tree reflects the correspondence between Geohash codes and geographic intervals. Users and their location relationships are recorded in the leaf nodes at each level of the hierarchical GL-Tree. In top-down order, along the GL-Tree, efficient storage and retrieval of location sets for specified locations and specified intervals can be achieved. We conducted experimental tests on the Gowalla public dataset and compared the performance of the B+ tree, R tree, and GL-Tree in terms of time consumption in three aspects: tree construction, location insertion, and location retrieval, and the results show that GL-Tree has good performance in terms of time consumption.
  • Publisher: Switzerland: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s23042245
    PMID: 36850842
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    Directory of Open Access Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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