Efficient Top-k Spatial Dataset Search Processing

In this paper, we introduce two novel top-k spatial dataset search schemes, KSDS and KSDS+. The core innovation of these schemes lies in partitioning the spatial datasets into grids and assessing similarity based on the distribution of points within these grids. This approach provides a robust foundation for spatial dataset search. To optimize search performance, we have developed an optimized scheme that incorporates two key strategies: a GMBR-based optimization strategy and a pooling-based optimization strategy. These strategies are designed to filter datasets to significantly improve search efficiency. Our experimental results demonstrate that KSDS and KSDS+ can perform top-k spatial dataset searches with both high effectiveness and efficiency, outpacing existing methods in terms of search speed. In the future, our research will explore other similarity-calculation models to further accelerate processing times. Additionally, we aim to integrate privacy-preserving techniques to ensure secure dataset searches. These advancements are intended to enhance the practicality and efficiency of spatial dataset searches in real-world applications.