Efficient Algorithms for Top-k Keyword Queries on Spatial Databases

With the ever-increasing number of spatio-textual objects on the Internet, many applications require to find objects in a given range that have the best scores to a keyword query. In this paper, we study the problem of top-k keyword search on spatial databases, which given a range, a keyword query, and a ranking function, finds k objects in the range that has the maximal scores to the keyword query. We study research challenges to address this problem. We extend the well-known R-tree to store both the textual and spatial information and propose a new index structure to index the objects. We devise an efficient threshold-based algorithm and develop effective pruning techniques to efficiently find the best answers. The experiments show that our algorithm achieves high performance and outperforms state-of-the-art methods.