A Skylining Approach to Optimize Influence and Cost in Location Selection

摘要: Location-selection problem underlines many spatial decision-making applications. In this paper, we study an interesting location-selection which can find applications such as banking outlet and hotel locations selections. particular, given a number of objects set location candidates, select some maximize the influence but minimize cost. The is defined by within distance; cost indicated minimum payment for location, measured quality vectors. We show that straightforward extension skyline approach inefficient, it needs to compute all candidates relying on expensive range queries. To overcome weakness, extend Branch Bound Skyline (BBS) method with novel join algorithm. derive bounds prune irrelevant R-tree entries early confirm part final answers. Theoretical analysis extensive experiments demonstrate efficiency scalability our proposed algorithms.