Abstract
Spatial textual indexing techniques allow one to efficiently access and process large volume of geospatial data, and recent research efforts have demonstrated that learned indexes can lead to better performance in comparison to conventional indexes. In this paper, we present a learned spatial textual index designed to process spatial textual data efficiently. Specifically, our proposed index is constructed based on the idea of radix table, spline points, and inverted lists. Besides, Morton encoding was used to convert high-dimensional coordinates into one dimension. In order to handle data insertion, deletion, and update in real-time, a gap array is used to store the underlying data, and a space reallocation strategy in units of spline points is designed. Based on the index, we propose query processing algorithms to handle different spatial keyword queries efficiently. An optimizer using random forest regression model was also designed to obtain appropriate index parameters for minimizing query latency. We evaluate our proposed index with IR-tree, and the findings show that our index outperforms IR-tree in terms of construction time, index size, and query efficiency.
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Notes
The Z-order curve is a space-filling curve which can project multi-dimensional data into one-dimensional space. It has nice locality properties, such that coordinates close to each other in multi-dimensional space also have close Z-coordinates.
All range queries are square-shaped with their sides ranging from 2km to 10km.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 62172179.
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X. Ding and Y. Zheng wrote the main manuscript text and figures, Raymond and H. Jin improved the paper. Z. Wang and Y. Zheng wrote the revision manuscript and report. All authors reviewed the manuscript.
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Ding, X., Zheng, Y., Wang, Z. et al. A learned spatial textual index for efficient keyword queries. J Intell Inf Syst 60, 803–827 (2023). https://doi.org/10.1007/s10844-022-00752-2
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DOI: https://doi.org/10.1007/s10844-022-00752-2