K-Dominance in Multidimensional Data: Theory and Applications
Thomas Schibler, Subhash Suri · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2017
We study the problem of k-dominance in a set of d-dimensional vectors, prove bounds on the number of maxima (skyline vectors), under both worst-case and average-case models, perform experimental evaluation using synthetic and real-world data, and explore an application of k-dominant skyline for extracting a small set of top-ranked vectors in high dimensions where the full skylines can be unmanageably large.