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 adopt the noisy comparison model [FRPU94] where comparisons fail with constant probability and confidence can be increased through independent repetitions of a comparison. In this model motivated by Crowdsourcing applications, Groz & Milo [GM15] show three bounds on the query complexity for the skyline problem. We improve significantly on that state of the art and provide two output-sensitive algorithms computing the skyline with respective query complexity $O(nd\log (dk/δ))$ and $O(ndk\log (k/δ))$ where $k$ is the size of the skyline and $δ$ the expected probability that our algorithm fails to return the correct answer. These results are tight for low dimensions.