Abstract: Skyline query set includes the objects that are not ``dominated'' by other objects in the dataset. In recent years, skyline query has been becoming a hot research topic due to its potential applications in online services, decision-making and real-time monitoring fields. Usually, people care about obtaining the skyline set quickly and progressively in real applications, however, because of the continuity, large-volume, and high-dimension of stream data, mining the sparse skyline set over data stream under control of losing quality is a more meaningful and challenging problem. In this paper, firstly, we propose a novel concept, called sparse-skyline, which uses a skyline object that represents its nearby skyline neighbors within ε-distance (acceptable difference). Then, two algorithms are developed which adopt correlation coefficient to adjust adaptively the quality of the sparse skyline query. Furthermore, theoretical analysis and experimental results show that the proposed methods are more efficient and effective compared with the existing skyline computing algorithm, and are suitable for data stream applications.