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TBSI: a Transformer-based spatial learned index for efficient construction and query.

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Title: TBSI: a Transformer-based spatial learned index for efficient construction and query.
Authors: Hu, Yusen1,2 (AUTHOR), Tang, Peng1,2 (AUTHOR), Meng, Yuhang1,2 (AUTHOR), Hu, Linshu1,2,3 (AUTHOR), Zhang, Feng1,2,3 (AUTHOR) zfcarnation@zju.edu.cn, Liu, Renyi1,2,3 (AUTHOR)
Source: International Journal of Geographical Information Science. Jul2026, Vol. 40 Issue 7, p1943-1971. 29p.
Subjects: Quadtrees, Indexing, Data management, Machine learning, Geospatial data, Transformer models, Search algorithms
Abstract: The exponential growth of geographic data reveals limitations in traditional spatial indices. Spatial learned indices that incorporate machine learning models have been proposed to enhance index performance. However, due to the considerable overhead of fine-grained data partitioning and the complexity of hierarchical model structures, existing spatial learned indices still exhibit bottlenecks in index construction and query processing. To address the aforementioned issues, we propose TBSI, an in-memory Transformer-based spatial learned index with an end-to-end structure. TBSI employs an enhanced quadtree to optimize data partitioning and utilizes a Transformer-based position prediction model to manage each data partition, preserving a simple yet effective index structure. TBSI exhibits superior performance in both index construction and query processing. We also design spatial query algorithms based on a filtering-refinement mechanism and data update algorithms based on buffers and flag arrays to support efficient query processing and index maintenance. Extensive experiments on real-world and synthetic datasets demonstrated that, compared to baselines, TBSI achieved up to 23.4 times speedup in build time, up to 24.3 times reduction in index size, up to 5.9 times improvement in range queries, and up to 4.5 times improvement in kNN queries. Also, TBSI exhibited robust adaptability to dynamic data updates. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:The exponential growth of geographic data reveals limitations in traditional spatial indices. Spatial learned indices that incorporate machine learning models have been proposed to enhance index performance. However, due to the considerable overhead of fine-grained data partitioning and the complexity of hierarchical model structures, existing spatial learned indices still exhibit bottlenecks in index construction and query processing. To address the aforementioned issues, we propose TBSI, an in-memory Transformer-based spatial learned index with an end-to-end structure. TBSI employs an enhanced quadtree to optimize data partitioning and utilizes a Transformer-based position prediction model to manage each data partition, preserving a simple yet effective index structure. TBSI exhibits superior performance in both index construction and query processing. We also design spatial query algorithms based on a filtering-refinement mechanism and data update algorithms based on buffers and flag arrays to support efficient query processing and index maintenance. Extensive experiments on real-world and synthetic datasets demonstrated that, compared to baselines, TBSI achieved up to 23.4 times speedup in build time, up to 24.3 times reduction in index size, up to 5.9 times improvement in range queries, and up to 4.5 times improvement in kNN queries. Also, TBSI exhibited robust adaptability to dynamic data updates. [ABSTRACT FROM AUTHOR]
ISSN:13658816
DOI:10.1080/13658816.2025.2581211