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.
CITATION STYLE
Alami, K., & Maabout, S. (2019). Multidimensional Skylines over Streaming Data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11448 LNCS, pp. 338–342). Springer Verlag. https://doi.org/10.1007/978-3-030-18590-9_41
Mendeley helps you to discover research relevant for your work.