Top-k Collective Spatial Keyword Approximate Query

The rapid expansion of spatial textual data, covering location and textual information, has spurred extensive research and application of spatial keyword query technology. Traditional methods focus on identifying groups of spatial objects that satisfy spatial keyword queries but often overlook the relationships between these objects, such as social correlations. To address this problem, this paper proposes a top-k collective spatial keyword approximate query approach. Firstly, an association rule-based social relationship evaluation method for spatial objects is proposed. Then, we design a scoring function that combines the location distances and social relationships of spatial objects within a group. Secondly, a Vantage Point Tree (VP-Tree) based pruning strategy is proposed for quickly searching the local neighborhood of spatial objects. Finally, the top-k spatial object groups are selected as the query result by leveraging the scoring function to calculate the score of candidate object groups. The experimental results demonstrate that the proposed social relationship evaluation method can achieve high accuracy, the proposed pruning strategy has high execution efficiency, and the obtained top-k groups of spatial objects can further meet users’ needs and preferences well.