标题： Sig2Model：一种可更新学习索引的提升驱动模型 显示英文标题

标题： Sig2Model: A Boosting-Driven Model for Updatable Learned Indexes

摘要： 学习索引（LIs）通过使用机器学习模型来近似排序数据的累积分布函数（CDF），实现了从传统索引结构到新范式的转变。 虽然LIs在静态数据集上表现出色，但在动态更新下性能会下降：保持CDF不变（F(k)之和等于1）需要全局模型重新训练，这会阻塞查询并限制每秒查询数（QPS）指标。 当前方法未能有效解决这些重新训练成本，因此不适合具有频繁更新的真实工作负载。 在本文中，我们提出了Sig2Model，这是一种高效且自适应的学习索引，通过三种关键技术最小化重新训练成本：（1）一种逻辑回归提升近似技术，通过局部逻辑函数近似数据分布的变化来动态调整索引模型，同时保持有界误差保证并推迟完整重新训练；（2）通过高斯混合模型（GMMs）进行主动更新训练，识别高更新概率区域以进行战略占位符分配，加快更新速度；以及（3）一种神经联合优化框架，通过基于梯度的学习持续优化逻辑函数集合和GMM参数。 我们在真实世界和合成工作负载上对最先进的可更新学习索引进行了评估，并表明 Sig2Model将重新训练成本降低了最多20倍，QPS提高了最多3倍，并且内存使用量减少了最多1000倍。 显示英文摘要

摘要： Learned Indexes (LIs) represent a paradigm shift from traditional index structures by employing machine learning models to approximate the cumulative distribution function (CDF) of sorted data. While LIs achieve remarkable efficiency for static datasets, their performance degrades under dynamic updates: maintaining the CDF invariant (sum of F(k) equals 1) requires global model retraining, which blocks queries and limits the queries-per-second (QPS) metric. Current approaches fail to address these retraining costs effectively, rendering them unsuitable for real-world workloads with frequent updates. In this paper, we present Sig2Model, an efficient and adaptive learned index that minimizes retraining cost through three key techniques: (1) a sigmoid boosting approximation technique that dynamically adjusts the index model by approximating update-induced shifts in data distribution with localized sigmoid functions while preserving bounded error guarantees and deferring full retraining; (2) proactive update training via Gaussian mixture models (GMMs) that identifies high-update-probability regions for strategic placeholder allocation to speed up updates; and (3) a neural joint optimization framework that continuously refines both the sigmoid ensemble and GMM parameters via gradient-based learning. We evaluate Sig2Model against state-of-the-art updatable learned indexes on real-world and synthetic workloads, and show that Sig2Model reduces retraining cost by up to 20x, achieves up to 3x higher QPS, and uses up to 1000x less memory.