Efficient k-dominant Skyline Computation for High Dimensional Space 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 kdominant skyline computation. Our algorithms reduce the pairwise comparison between the k-dominant skyline objects and the dataset. Through extensive experiments with real and synthetic datasets, we confirm that our algorithms can efficiently compute k-dominant skyline queries.