An optimizing spatial learned index for balanced update and query performance

Spatial databases are the main means to manage geo-big data, and learned spatial indices are a novel approach to improve the spatial retrieval performance of spatial databases by modeling the data distribution. However, the complex hierarchical structures in current learning models pose significant limitations, including prolonged construction times, slow data updates, and suboptimal dynamic query performance. Consequently, improving the efficiency of both index construction and updates is essential. We addressed these challenges by introducing a new method, the Spatial Uniform Partition Learned Index (SUPLI). SUPLI utilizes an iterative uniform partitioning algorithm that simplifies data distribution by uniformly segmenting space and applies a linear regression function—instead of a neural network model—to enable efficient index construction. Additionally, SUPLI incorporates query load optimization and historical query learning strategies, which dynamically adjust the spatial query algorithm to enhance query efficiency. Furthermore, a buffer structure is employed to store change information, facilitating efficient updates. Comparative evaluations conducted on three synthetic datasets and two real-world datasets show that SUPLI outperforms the classic R-tree by an order of magnitude in construction, query, and update performance, and demonstrates additional advantages over similar spatial learned indices, such as SPRIG and LISA.