Efficient computation for k-dominant skyline queries with domination power index

Skyline queries have recently attracted a lot of attention for its intuitive query formulation. It can act as a filter to discard sub-optimal objects. However, a major drawback of skyline is that, in datasets with many dimensions, the number of skyline objects becomes large and no longer offer any interesting insights. To solve the problem, recently k-dominant skyline queries have been introduced, which can reduce the number of skyline objects by relaxing the definition of the dominance. This paper addresses the problem of k-dominant skyline objects for high dimensional dataset. We propose algorithms for k-dominant skyline computation. Through extensive experiments with real and synthetic datasets, we show that our algorithms can efficiently compute k-dominant skyline queries.