# Efficient Top-\(k\) Spatial Dataset Search Processing
## 2 Related Work  
Various spatial dataset search schemes have been proposed to address different real-world business needs, such as keyword-based spatial dataset search, range search-based spatial dataset search and top-\(k\) spatial dataset search. * Keyword-based spatial dataset search. Keyword-based dataset search is used to retrieve data points or objects associated with specific terms or keywords, much like traditional text-based search engines. A typical example would be searching a dataset with geographic information such as places, landmarks or geographic features, where users enter keywords like \"park\" or \"restaurant\" to find corresponding locations. To provide a comprehensive understanding of spatial keyword search, [PERSON] et al. [12] conduct a comprehensive survey of existing studies on spatial keyword search. [PERSON] et al. [13; 14] propose a new query type termed instant error-tolerant spatial keyword queries on road networks and then present a real-time query system called TASKS for instant error-tolerant spatial keyword queries on road networks. [PERSON] et al. [15] introduce a new type of query called the moving collective spatial keyword query (MCSKQ). Although keyword search is intuitive and efficient for retrieving spatial data based on textual descriptions, it lacks spatial awareness and relies heavily on exact keyword matching. * Range-based spatial dataset search. Range-based dataset search is a type of query operation in spatial data processing that involves retrieving all data points or objects within a specified geographic range. [PERSON] et al. [16] propose a set of five analysis methods to estimate the selectivity and the number of index nodes accessed in serving a range query. [PERSON] et al. [17] propose a novel indexing structure STITCH that efficiently executes spatial range queries on multiple datasets. [PERSON] et al. [18] propose a dictionary partition-based multi-keyword ranked search scheme that compresses vector dimensions, employs a double-tier index for efficient searches and safeguards user privacy over encrypted cloud data. Range search effectively retrieves all spatial objects within a specified geographic boundary, making it useful for location-based services. * Top-\(k\) spatial dataset search. The top-\(k\) spatial dataset search introduces a novel query paradigm that considers a user query as an example of the data in which the user is interested [19; 20; 21]. [PERSON] et al. [22] provide an approximate solution that prunes the search space and achieves considerably better time performance with minimal or no impact on effectiveness. [PERSON] et al. [10] tackle spatial dataset search using Earth Mover's Distance (EMD) to measure the similarity between datasets and propose a Dual-Bound Filtering (DBF) framework to accelerate EMD-based spatial dataset search. [PERSON] et al. [23] design the order-preserving encrypted similarity to achieve secure similarity calculation and propose the baseline search scheme PriDAS and the optimized search scheme PriDAS+. [PERSON] et al. [24] demonstrate the functionality of a query engine XQ, which enables top-\(k\) spatial dataset search and shows its applicability in various situations. However, existing research on top-\(k\) spatial dataset search is insufficient, and it struggles to provide efficient and effective search results when dealing with large-scale spatial datasets. For example, the Hausdorff distance-based spatial dataset search is sensitive to outliers and also faces privacy restrictions in certain scenarios [25]. Additionally, the EMD-based spatial dataset search is computationally complex and incurs a significant time cost when dealing with large spatial datasets. Therefore, developing more efficient top-\(k\) spatial dataset search schemes has become crucial for improving system performance and meeting practical demands. In this paper, we focus on top-\(k\) spatial dataset search.