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.

翻译：近年来，研究者提出了若干用于衡量数据集元组（特别是所谓的天际线元组，即不被其他元组支配的元组）各类特征的指标。数值指标具有重要意义，例如它们可提供额外的排序标准，用于对天际线元组进行排序并聚焦于其子集。我们专注于一种可针对任意天际线元组 $t$ 进行度量的鲁棒性指标：网格抗扰度，即 $t$ 在保持非支配状态（从而保留在天际线中）时所能容忍的数值扰动幅度。该指标的计算通常涉及一轮或多轮天际线本身的计算，或至少涉及支配关系的计算。基于近期允许并行计算天际线的分区策略进展，我们探讨了如何将这些策略适配于该指标的计算。