Efficient Computation of Reverse Skyline Queries

In this paper, for the first time, we introduce the concept of Reverse Skyline Queries. At first, we consider for a multidimensional data set P the problem of dynamic skyline queries according to a query point q. This kind of dynamic skyline corresponds to the skyline of a transformed data space where point q becomes the origin and all points of P are represented by their distance vector to q. The reverse skyline query returns the objects whose dynamic skyline contains the query object q. In order to compute the reverse skyline of an arbitrary query point, we first propose a Branch and Bound algorithm (called BBRS), which is an improved customization of the original BBS algorithm. Furthermore, we identify a super set of the reverse skyline that is used to bound the search space while computing the reverse skyline. To further reduce the computational cost of determining if a point belongs to the reverse skyline, we propose an enhanced algorithm (called RSSA) that is based on accurate pre-computed approximations of the skylines. These approximations are used to identify whether a point belongs to the reverse skyline or not. Through extensive experiments with both real-world and synthetic datasets, we show that our algorithms can efficiently support reverse skyline queries. Our enhanced approach improves reversed skyline processing by up to an order of magnitude compared to the algorithm without the usage of pre-computed approximations.