Processing Skyline Groups on Data Streams
Aziguli Wulamu, Hailing Li, Xi Wang Guo, Yonghong Xie, Yajing Fu · 2015
Skyline is defined as a set of objects in a multidimensional dataset. It returns objects which are not dominated by any other objects in the set. An object p dominates object p' if and only if it is not worse than p' on all of the attributes (dimensions) and is better than p' on at least one attribute. Given the same kind of dataset, the skyline group query returns groups which are not dominated by any other groups in the set and each group has the same number of objects. Although the skyline group query has been investigated in recent years, most techniques are designed for static datasets. However, data are changing with time in many practical applications nowadays and query processing techniques for dynamic datasets are not available. Therefore, finding skyline groups on data streams are highly required. In this paper, we propose a new algorithm to find the skyline groups on data streams. We use two data structures CL and GSM to store the immediate results. CL stores the candidate objects that may become members of the skyline groups. GSM stores the immediate results of the dynamic programming. We do experiments on synthetic datasets. The experimental results show that the algorithms proposed can find skyline groups efficiently.