Finding skyline on-the-fly

Speaker: Eric LO

Abstract

Skyline queries return a set of interesting data points that are not dominated by any other point on all dimensions. Most of the existing algorithms focus on skyline computation in centralized databases, and some of them can progressively return skyline points as they are identified rather than returning the answer in a batch. Processing skyline queries over the web is much challenging. One reason is that in many web applications, the target attributes are stored at different sites, and they might not be available other than through external web-accessible form interfaces. In this talk, we talk about PDS (progressive distributed skylining), a progressive algorithm that evaluates skyline queries efficiently in this setting. PDS could also be applied to different types of web skyline queries. Furthermore, it supports a user-friendly progress indicator that allows users to monitor the progress of long running skyline queries. Our performance study further shows that PDS is efficient and robust to different data distributions and achieves its progressive goal with a minimal overhead.

Referred Papers