Multidimensional Skylines over Streaming Data

Abstract

We consider a stream where each record is described by a set of dimensions D. The records have a validity time interval of size $\omega$. The queries we consider consist in retrieving the valid skyline records with respect to subsets $D'$ (subspace) of D. Answering multidimensional skyline queries over streaming data is a hard task because of the data velocity and even index structures that optimize these queries need to be continuously updated. To overcome this difficulty, we propose a framework that handles the streaming data in a micro-batch mode together with an incrementally maintainable index structure.