An Efficient Method of Computing the k-Dominant Skyline Efficiently by Partition Value

Skyline queries are useful in many applications such as multicriteria decision-making, data mining, and user-preference queries. However, the probability that a point dominates another one reduces significantly as the number of dimensions increases, which results in the number of skyline points becoming too numerous to offer any interesting insights. The concept of the k-dominant skyline was previously proposed to solve this problem. A point p is said to k-dominate another point q if there are k (<= d) dimensions in which p is better than or equal to q and is better in at least one of these k dimensions. A point that is not k-dominated by any other points is in the k-dominant skyline. This paper addresses the problem of computing the k-dominant skyline. By analyzing the properties of the k-dominant skyline, four lemmas are derived to reduce the effort required to select candidates and prune false positives during the computation. A set of experiments showed that our method can efficiently compute the k-dominant skyline for independent, correlated and anticorrelated datasets.