Keyword-based k-nearest neighbor search in spatial databases

With the ever-increasing number of spatio-textual objects, many applications require to find objects close to a given query point in spatial databases. In this paper, we study the problem of keyword-based k-nearest neighbor search in spatial databases, which, given a query point and a set of keywords, finds k-nearest neighbors of the query point that contain all query keywords. To efficiently answer such queries, we propose a new indexing framework by integrating a spatial component and a textual component, which can efficiently prune search space in terms of both spatial information and textual descriptions. We develop effective index structures and pruning techniques to improve query performance. Experimental results show that our approach significantly outperforms state-of-the-art methods.