The spatial skyline queries

In this paper, for the first time, we introduce the concept of Spatial Skyline Queries (SSQ). Given a set of data points <i>P</i> and a set of query points <i>Q</i> each data point has a number of derived spatial attributes each of which is the point's distance to a query point. An SSQ retrieves those points of <i>P</i> which are not dominated by any other point in <i>P</i> considering their derived spatial attributes. The main difference with the regular skyline query is that this spatial domination depends on the location of the query points <i>Q</i> SSQ has application in several domains such as emergency response and online maps. The main intuition and novelty behind our approaches is that we exploit the geometric properties of the SSQ problem space to avoid the exhaustive examination of all the point pairs in <i>P</i> and <i>Q</i>. Consequently, we reduce the complexity of SSQ search from <i>O(|P|²|Q|)</i> to <i>O(|S|²|C|+√|P|)</i>, where |<i>S</i>| and |<i>C</i>| are the solution size and the number of vertices of the convex hull of <i>Q</i>, respectively.We propose two algorithms, B²S² and VS², for static query points and one algorithm, VCS², for streaming <i>Q</i> whose points change location over time (e.g., are mobile). VCS² exploits the pattern of change in <i>Q</i> to avoid unnecessary re-computation of the skyline and hence efficiently perform updates. Our extensive experiments using real-world datasets verify that both R-tree-based B²S² and Voronoi-based VS² out perform the best competitor approach in terms of processing time by a wide margin (4-6 times better in most cases).