分层列表标记
Layered List Labeling
AI总结：
本文提出新的数据结构技术，可组合任意三个列表标记解决方案，从而分别挑选出最佳的最坏情况、自适应和期望界限，打破了三者之间长期存在的性能张力。
摘要：
列表标记问题是数据结构中最基本且被研究得最广泛的算法原语之一，拥有涵盖上界、下界及数据管理应用的广泛文献。该问题的经典算法可追溯至1981年，其摊还代价为$O(\log^2 n)$。随后的工作在三个方向上取得了进展：低延迟（最坏情况）界限；高吞吐量（期望）界限；以及针对重要工作负载的（自适应）界限。\n令人惊讶的是，这三个研究方向几乎完全互不交集——这是因为，到目前为止，允许在一个方向上取得进展的技术会迫使其他方向上的界限恶化。因此，在最坏情况、自适应和期望界限之间似乎存在一种张力。早在PODS'99就有人提出将列表标记用于数据库，但数据库需要良好的吞吐量、响应时间，并且需要适应常见工作负载（例如批量加载），而目前没有列表标记算法能在这三个方面都达到良好的界限。\n我们表明这种张力并非根本性的。事实上，借助新的数据结构技术，实际上可以组合任意三个列表标记解决方案，从而从每个方案中挑选出最佳的最坏情况、自适应和期望界限。
英文摘要：
The list-labeling problem is one of the most basic and well-studied algorithmic primitives in data structures, with an extensive literature spanning upper bounds, lower bounds, and data management applications. The classical algorithm for this problem, dating back to 1981, has amortized cost $O(\log^2 n)$. Subsequent work has led to improvements in three directions: \emph{low-latency} (worst-case) bounds; \emph{high-throughput} (expected) bounds; and (adaptive) bounds for \emph{important workloads}. Perhaps surprisingly, these three directions of research have remained almost entirely disjoint -- this is because, so far, the techniques that allow for progress in one direction have forced worsening bounds in the others. Thus there would appear to be a tension between worst-case, adaptive, and expected bounds. List labeling has been proposed for use in databases at least as early as PODS'99, but a database needs good throughput, response time, and needs to adapt to common workloads (e.g., bulk loads), and no current list-labeling algorithm achieve good bounds for all three. We show that this tension is not fundamental. In fact, with the help of new data-structural techniques, one can actually \emph{combine} any three list-labeling solutions in order to cherry-pick the best worst-case, adaptive, and expected bounds from each of them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
