LAUSR

With the advent of a large number of spatial-textual data, collective spatial keyword queries have been widely studied in recent years. However, the collective spatial keyword query studied so far usually looks for only a set of objects. In addition, the existing collective spatial keyword query algorithms are all based on index structure, which requires excessive additional memory overhead. In this paper, we study the Top- $k$ collective spatial keyword queries( $\text{T}k$ CoSKQ), which aims at retrieving a set $G$ including $k$ sets of objects. Each group of object set can cover all the query keywords, and the objects in the set are close to the query position and have the minimum inter-object distance. We prove that the $\text{T}k$ CoSKQ problem is NP-hard, and then propose two index-independent algorithms based on the spatial-textual similarity constraint, containing an exact algorithm and a heuristic algorithm. In addition, a variety of effective pruning strategies are presented to minimize the search scope. A large number of experiments on real datasets demonstrate the effectiveness and scalability of the proposed algorithms.