Finding the minimum spatial keyword cover

The existing works on spatial keyword search focus on finding a group of spatial objects covering all the query keywords and minimizing the diameter of the group. However, we observe that such a formulation may not address what users need in some application scenarios. In this paper, we introduce a novel spatial keyword cover problem (SK-COVER for short), which aims to identify the group of spatio-textual objects covering all keywords in a query and minimizing a distance cost function that leads to fewer proximate objects in the answer set. We prove that SK-COVER is not only NP-hard but also does not allow an approximation better than O(log m) in polynomial time, where m is the number of query keywords. We establish an O(log m)-approximation algorithm, which is asymptotically optimal in terms of the approximability of SK-COVER. Furthermore, we devise effective accessing strategies and pruning rules to improve the overall efficiency and scalability. In addition to our algorithmic results, we empirically show that our approximation algorithm always achieves the best accuracy, and the efficiency of our algorithm is comparable to a state-of-the-art algorithm that is intended for mCK, a problem similar to yet theoretically easier than SK-COVER.