Skyline Query Processing Research Articles

An Algebra for Skyline Query Processing Data Cube

An Algebra for Skyline Query Processing Data Cube

Continuous Skyline Queries for Moving Objects

The literature on skyline algorithms has so far dealt mainly with queries of static query points over static data sets. With the increasing number of mobile service applications and users, however, the need for continuous skyline query processing has become more pressing. A continuous skyline query involves not only static dimensions, but also the dynamic one. In this paper, we examine the spatiotemporal coherence of the problem and propose a continuous skyline query processing strategy for moving query points. First, we distinguish the data points that are permanently in the skyline and use them to derive a search bound. Second, we investigate the connection between the spatial positions of data points and their dominance relationship, which provides an indication of where to find changes in the skyline and how to maintain the skyline continuously. Based on the analysis, we propose a kinetic-based data structure and an efficient skyline query processing algorithm. We concisely analyze the space and time costs of the proposed method and conduct an extensive experiment to evaluate the method. To the best of our knowledge, this is the first work on continuous skyline query processing

Progressive skylining over Web-accessible databases

Skyline queries return a set of interesting data points that are not dominated on all dimensions by any other point. Most of the existing algorithms focus on skyline computation in centralized databases, and some of them can progressively return skyline points upon identification rather than all in a batch. Processing skyline queries over the Web is a more challenging task because in many Web applications, the target attributes are stored at different sites and can only be accessed through restricted external interfaces. In this paper, we develop PDS (progressive distributed skylining), a progressive algorithm that evaluates skyline queries efficiently in this setting. The algorithm is also able to estimate the percentage of skyline objects already retrieved, which is useful for users to monitor the progress of long running skyline queries. Our performance study shows that PDS is efficient and robust to different data distributions and achieves its progressive goal with a minimal overhead.