Efficient skyline computation in metric space

Given a set of n query points in a general metric space, a metric-space skyline (MSS) query asks what are the closest points to all these query points in the database. Here, consider for any point p, if there are no other points in the database which have less or equal distance to all the query points, then p is denoted as one of the closest points to the query points. This problem is a direct generalization of the recently proposed spatial-skyline query problem, where all the points are located in two or three dimensional Euclidean space. It is also closely related with the nearest neighbor (NN) query, the range query and the common skyline query problem. In this paper, we have developed new algorithms to aggressively prune non-skyline points from the search space. We also contribute two new optimization techniques to reduce the number of distance computations and dominance tests. Our experimental evaluation has shown the effectiveness and efficiency of our approach.