This document discusses algorithms for finding the top-K dominating queries on incomplete datasets. It proposes using a skyline-based algorithm that incorporates priority values for each dimension. This allows the algorithm to determine dominance even when the values are missing for some dimensions. It works by bucketing the data based on bit representations, finding the local skylines within each bucket, and then calculating scores for objects based on their dominance over other objects while considering the priority of dimensions. The top-K objects with the highest scores are then returned as the results. This approach provides more accurate outputs for applications like movie recommendations by allowing users to specify dimension priorities.