$\rho$ -Dominant Skyline Computation on Data Stream

Skyline query is widely used in multi-standard decision making process. As the extension to skyline query, $\rho$ -dominant skyline query controls the base of the result set by adjusting data proportion, which is suitable for the application on fast deciding data stream. However, skyline query on data stream cannot satisfy the need for skyline query of corresponding value ratio, and the traditional $\rho$ -dominant skyline query cannot provide skyline query with frequent data update. As a result, a data stream-based algorithm that can achieve the goal of $\rho$ -dominant skyline query is proposed. First, the definitions of full-dominant relationship and $\rho$ -dominance are given, after which the definitions of $\rho$ -dominant skyline and $\rho$ -dominant skyline based on data stream are proposed. Second, by analyzing the characters of data stream-based $\rho$ -dominant skyline, a new data filtering method is established. Based on the method, $\rho$ -Dominant Skyline Query on data stream over Sliding Window (DSSW) is proposed in order to improve the efficiency of $\rho$ -dominant skyline query. After that, we expend the $\rho$ -dominant skyline query of data stream into ${n}$ -of- ${N}~\rho$ -dominant skyline query and the $(n_{1},n_{2})$ -of- $N \rho$ -dominant skyline query in data stream. Finally, we prove that DSSW has obvious advantages in responding time and storage skyline space by comparing with the traditional $\rho$ -dominant skyline query algorithms through abundant experiments.