Continuous Adaptive Mining the Thin Skylines over Evolving Data Stream

Skyline queries, which return the objects that are better than or equal in all dimensions and better in at least one dimension, are useful in many decision making and real-time monitor applications. With the number of dimensions increasing and continuous large volume data arriving, mining the thin skylines over data stream under control of losing quality is a more meaningful problem. In this paper, firstly, we propose a novel concept, called thin skyline, which uses a skyline object that represents its nearby skyline neighbors within Ɛ-distance (acceptable difference). Then, two algorithms are developed which prunes the skyline objects within the acceptable difference and adopts correlation coefficient to adjust adaptively thin skyline query quality. Furthermore, our experimental performance study shows that the proposed methods are both efficient and effective.