2021/08/11 by Mihail Stoian, Stoian, Mihail, Andreas Kipf +5
Computer Science · #Data Management and Algorithms #Data Stream Mining Techniques #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2108.05117
openalex publication_date 2021/08/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Latest research proposes to replace existing index structures with learned models. However, current learned indexes tend to have many hyperparameters, often do not provide any error guarantees, and are expensive to build. We introduce Practical Learned Index (PLEX). PLEX only has a single hyperparameter ε (maximum prediction error) and offers a better trade-off between build and lookup time than state-of-the-art approaches. Similar to RadixSpline, PLEX consists of a spline and a (multi-level) radix layer. It first builds a spline satisfying the given ε and then performs an ad-hoc analysis of the distribution of spline points to quickly tune the radix layer.