We revisit the online Unit Clustering and Unit Covering problems in higher dimensions: Given a set of $n$ points in a metric space, that arrive one by one, Unit Clustering asks to partition the points into the minimum number of clusters (subsets) of diameter at most one; while Unit Covering asks to cover all points by the minimum number of balls of unit radius. In this paper, we work in $\mathbb{R}^d$ using the $L_\infty$ norm. We show that the competitive ratio of any online algorithm (deterministic or randomized) for Unit Clustering must depend on the dimension $d$. We also give a randomized online algorithm with competitive ratio $O(d^2)$ for Unit Clustering of integer points (i.e., points in $\mathbb{Z}^d$, $d\in \mathbb{N}$, under $L_{\infty}$ norm). We show that the competitive ratio of any deterministic online algorithm for Unit Covering is at least $2^d$. This ratio is the best possible, as it can be attained by a simple deterministic algorithm that assigns points to a predefined set of unit cubes. We complement these results with some additional lower bounds for related problems in higher dimensions.

翻译：我们重新审视在线单位集群和单位覆盖更高层面的问题:如果在一个公制空间里有一套美元点数,一个一个一个到达,单位集群要求将这些点分成直径最小组数(子集)中,一个最多一个;而Unit封面要求以单位半径最小球数覆盖所有点数。在本文中,我们使用美元标准,用美元标准,用美元标准工作。我们表明,任何单位集群在线算法(确定性或随机化)的竞争性比率必须取决于一个维度,美元。我们给出一个随机化的在线算法,对整点的组合(即单位半分数的点数,美元/月/月/月/月/日/月/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日/日