# Efficient Top-\(k\) Spatial Dataset Search Processing
## 1 Introduction  
With the rapid growth of structured and semi-structured data in repositories such as data lakes, open government platforms and web forms, dataset search has become an essential task for data scientists and engineers and a variety of search engines have emerged [1, 2, 3, 4, 5, 6]. Efficient dataset search enables faster data discovery, enhances decision-making processes and supports more effective data integration and analysis, making it a crucial component in modern data-management workflows. Spatial datasets, as a crucial component of real-world data, encompass vast geographic information related to various entities such as people, vehicles and other dynamic objects. These spatial datasets have garnered increasing global interest and are being applied in various industries, such as autonomous transportation, healthcare mapping, urban mobility and energy management. Searching and retrieving relevant spatial datasets is critical to optimizing decision-making, improving operational efficiency and enabling real-time applications in these domains. This paper focuses on the top-\(k\) spatial dataset search, which allows users to submit an exemplar dataset \(S_{e}\) as a search query and retrieves the \(k\) spatial datasets most similar to the exemplar dataset from a spatial data repository \(\mathcal{S}=\{S_{1},S_{2},\cdots,S_{n}\}\), as illustrated in Figure 1. For example, in the Auctus search system [7], users can upload a one-month bicycle-usage dataset as an exemplar dataset to retrieve related datasets for other months, enabling the establishment of robust models for prediction tasks. In RONIN [8], users can utilize an example dataset concerning smart cities to find additional datasets that are joinable, based on the set-containment relationships between datasets. [PERSON] et al. [9]introduce the Hausdorff distance into the dataset search, providing a metric to quantify the maximum deviation between two sets of location points. [PERSON] et al. [10] employ Earth Mover's Distance, a widely recognized metric for comparing distributions, to quantify the similarity between datasets for top-\(k\) searches. Exemplar search has emerged as a promising trend in spatial dataset search. Moreover, methods employing the Hausdorff Distance face the impact of outliers, which can affect the accuracy of the search results. On the other hand, the Earth Mover's Distance provides more fine-grained similarity measurements, but it incurs substantial computational costs [11]. Striking a balance between the effectiveness and efficiency of similarity calculations continues to be a significant challenge. In this paper, we propose an efficient top-\(k\) spatial dataset search processing, which can retrieve the \(k\) most similar spatial datasets to a given exemplar dataset. First, a density distribution-based similarity model (DDSM) is designed to measure the similarity between spatial datasets, which applies grid partition to compress spatial datasets into density and quantity distributions for similarity calculation. Based on the DDSM model, a baseline spatial dataset search scheme (KSDS) is proposed. It traverses each dataset in the spatial data repository and calculates its similarity to the exemplar dataset to obtain the top-\(k\) datasets with the highest similarity. To improve search efficiency, an optimized spatial dataset search scheme (KSDS+) is designed, which contains two optimization strategies to filter out datasets that are unlikely to appear in the search result. The optimized scheme also supports dynamic update operations on the spatial data repository. Experimental results on two real-world data repositories show that the proposed search-processing performs well in search effectiveness and efficiency. The contributions of this paper are as follows. * We propose a density distribution-based similarity model (DDSM), which uses grid partition to compress spatial datasets into density and quantity distributions to measure similarity between datasets (see Section 4).