# Efficient Top-\(k\) Spatial Dataset Search Processing
## 6 The Optimized Search Scheme
### The Optimized Search Algorithm
This means that choosing \(\epsilon\) is not just about improving search speed, it also impacts memory usage. If \(\epsilon\) is too small, the search efficiency may decrease, while a larger \(\epsilon\) can lead to unnecessary storage overhead. In real-world applications, it is important to find a good balance based on the dataset and performance needs. In this experiment, Figures 14 and 15 are combined to evaluate the parameter settings and the default parameter value is set to 4 as it strikes a good balance between search performance and memory overhead. This choice ensures efficient query processing while keeping storage consumption reasonable. ## 8 Conclusions

In this paper, we propose two spatial dataset search schemes, KSDS and KSDS+, for the problem of top-\(k\) spatial dataset search. The primary innovation of our scheme is to divide the spatial dataset into grids and measure the similarity between spatial datasets using the distribution of points in the dataset on the grids. Then, we introduced a GMBR-based optimization strategy and a pooling-based optimization strategy to accelerate spatial dataset filtering efficiency. The experimental results demonstrated that our schemes can efficiently perform top-\(k\) spatial dataset search, outperforming existing approaches in search efficiency. In the future, we plan to explore other faster similarity-calculation models, the application of our proposed schemes on various datasets and experiment with privacy-preserving techniques to ensure the security of dataset searches. These advances aim to improve the applicability and efficiency of spatial dataset search in real-world scenarios. **Author Contributions:** Software, H.Z. ; Formal analysis, G.Y. ; Investigation, P.L. ; Resources, M.Z. ; Data curation, L.C. ; Writing--original draft, J.S. ; Writing--review & editing, H.D. All authors have read and agreed to the published version of the manuscript. Figure 5:4 Pooling index cost versus \(\epsilon\). **Funding:** The National Natural Science Foundation of China under the grant Nos. 62372244, 62202338 and 62272238; the Jiangsu Province Postgraduate Scientific Research Innovation Program under Grand No. KYCX24_1221. **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The data presented in this study are openly available in OpenStreetMap at [[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org)). **Conflicts of Interest:** The authors declare no conflicts of interest. ## References

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