Open Access

Lena Rudenko, Markus Endres

• Skyline processing has received considerable attention in the last decade, in particular when filtering the most preferred objects from a multi-dimensional set on contradictory criteria. Most of the work on Skyline computation focus on conventional databases, but stream data analysis becomes a high relevant topic in various academic and business fields. Nowadays, an enormous number of applications require the analysis of time evolving data and therefore the study of continuous query processing has recently attracted the interest of researchers all over the world. In this paper, we propose a novel algorithm called SLS for evaluating Skyline queries over continuous settings, and empirically demonstrate the advantage of this algorithm on artificial and real data. Our algorithm continuously monitors the incoming data and therefore is able to maintain the Skyline incrementally. For this, SLS utilizes the lattice structure a Skyline query constructs and analyzes the Skyline in linear time.