Layered List Labeling
Michael A. Bender, Alex Conway, Martin Farach-Colton, Hanna Komlos,, William Kuszmaul

TL;DR
This paper introduces a novel data-structural approach that combines different list-labeling algorithms to achieve optimal bounds across worst-case, adaptive, and expected scenarios, resolving a long-standing tension in the field.
Contribution
The authors develop techniques to merge existing list-labeling solutions, enabling simultaneous optimal bounds in worst-case, adaptive, and expected settings.
Findings
Achieves combined bounds for list-labeling problems.
Resolves the tension between different bounds in list-labeling.
Provides a unified framework for multiple list-labeling algorithms.
Abstract
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 . 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…
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Taxonomy
TopicsPharmacy and Medical Practices · Web Applications and Data Management
