Partitioning Strategies for Parallel Computation of Flexible Skylines

Abstract

While classical skyline queries identify interesting data within large datasets, flexible skylines introduce preferences through constraints on attribute weights, and further reduce the data returned. However, computing these queries can be time-consuming for large datasets. We propose and implement a parallel computation scheme consisting of a parallel phase followed by a sequential phase, and apply it to flexible skylines. We assess the additional effect of an initial filtering phase to reduce dataset size before parallel processing, and the elimination of the sequential part (the most time-consuming) altogether. All our experiments are executed in the PySpark framework for a number of different datasets of varying sizes and dimensions.