Parallelizing the Computation of Robustness for Measuring the Strength of Tuples

TL;DR

This paper introduces a parallelized approach to efficiently compute the robustness indicator for skyline tuples, which measures how much perturbation they can withstand while remaining non-dominated, leveraging recent skyline partitioning strategies.

Contribution

It adapts recent skyline partitioning methods to parallelize the computation of the robustness indicator for skyline tuples, improving efficiency.

Findings

Parallelization reduces computation time.

Partitioning strategies enable scalable robustness computation.

Method maintains accuracy while improving performance.

Abstract

Several indicators have been recently proposed for measuring various characteristics of the tuples of a dataset -- particularly, the so-called skyline tuples, i.e., those that are not dominated by other tuples. Numeric indicators are very important as they may, e.g., provide an additional criterion to be used to rank skyline tuples and focus on a subset thereof. We concentrate on an indicator of robustness that may be measured for any skyline tuple $t$: grid resistance, i.e., how large value perturbations can be tolerated for $t$ to remain non-dominated (and thus in the skyline). The computation of this indicator typically involves one or more rounds of computation of the skyline itself or, at least, of dominance relationships. Building on recent advances in partitioning strategies allowing a parallel computation of skylines, we discuss how these strategies can be adapted to the computation of the indicator.