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

Abstract

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.

GLPS: A Geohash-Based Location Privacy Protection Scheme

“…The time complexity of this process is a linear equation of the depth n of the GL-Tree tree, denoted as O(n) and independent of the total amount of data stored on the GL-Tree. For a detailed analysis, see [52].…”

“…Each Geohash code corresponds to a rectangular geographic location interval, and the area of the interval is determined by the length of the code. The specific principle and pseudo-code of Geohash encoding can be found in [35,52]. According to the Geohash encoding, we can obtain the following basic properties:…”

“…GL-Tree is a hierarchical tree data structure that we designed for the storage, management, and efficient retrieval of large areas in our previous research [52]. It reflects the correspondence between Geohash codes and the grid division of rectangular geographic regions.…”

“…Efficient storage and retrieval of locations can be achieved by following the GL-Tree in a top-down manner. For a description of the structure, characteristics, physical meaning, retrieval and maintenance process, and experimental comparison of GL-Tree, please read [52].…”

Anomalous vehicle recognition in smart cities using persistent bloom filter: Memory efficient and intelligent monitoring

“…The new hash index structure minimizes the number of write operations required by traditional hash index structures, making it well‐suited for flash‐based storage systems 21 . GL‐Tree 22 is a popular data structure used for range search in high‐dimensional spaces, especially in GIS applications. Its efficient retrieval of location data makes it useful for real‐time traffic monitoring systems, where up‐to‐date traffic information is essential for providing accurate navigation assistance to drivers.…”