ABSTRACT Spatial learned indexes construct index structures by learning the spatial distribution characteristics of data. Compared to traditional spatial indexes, they significantly reduce storage overhead and query costs. A widely adopted strategy in learned spatial indexes involves employing spatial pivots to model the global distribution of spatial data. Each data point is indexed by its distances to these pivots to enable efficient data partitioning and fast query processing. However, most existing methods rely on overly simplistic assumptions when selecting or modeling pivots. These assumptions limit their effectiveness when dealing with complex or heterogeneous spatial data. To address this, we propose a Distribution‐Aware Spatial Pivot Learned Index (DSPI). DSPI leverages a self‐attention mechanism to model the spatial distribution of data. It employs a Transformer neural network to jointly optimize pivot placement and pruning efficiency, enabling end‐to‐end query pruning. Within a given pivot search space, DSPI accurately locates query results using a lightweight function mapping model, optimized via second‐order derivatives and Akaike Information Criterion (AIC). Based on DSPI, we further design a range query algorithm with encoding‐based pruning and a k‐nearest neighbor (kNN) query algorithm with adaptive search radius. Experimental results on real‐world and synthetic datasets show that DSPI achieves state‐of‐the‐art efficiency: its index size is about 66.7% of LIMS and 71.4% of TBSI, while average query latency is about 2.5× faster than LIMS and 1.59× faster than TBSI. Under high‐dimensional data and large‐scale spatial queries, these advantages further widen. Moreover, DSPI maintains stable retrieval performance under dynamic updates.
Meng et al. (Thu,) studied this question.