Skyline Computation with Noisy Comparisons

Given a set of n points in a d-dimensional space, we seek to compute the skyline, i.e., those points that are not strictly dominated by any other point, using few comparisons between elements. We study the crowdsourcing-inspired setting ([FRPU94]) where comparisons fail with constant probability. In this model, Groz & Milo [GM15] show three bounds on the query complexity for the skyline problem. We provide two output-sensitive algorithms computing the skyline with query complexity O(nd log(dk)) and O(ndk log(k)), where k is the size of the skyline. These results improve significantly on the state-of-the-art and are tight for low dimensions.