为结构化数据分析提供数据库内动态模型切片能力
Powering In-Database Dynamic Model Slicing for Structured Data Analytics
AI总结：
LEADS提出SQL感知动态模型切片技术，利用专家混合与门控网络在数据库内定制模型，集成于PostgreSQL，提升结构化数据预测效率并降低推理延迟。
摘要：
关系数据库管理系统（RDBMS）广泛用于结构化数据的存储。为了获得超越统计聚合的洞察，我们通常必须使用传统数据库操作从数据库中提取特定的子数据集，然后在单独的分析系统中对这些子数据集应用深度神经网络（DNN）训练和推理。这一过程可能代价高昂，尤其是当针对不同分析目的提取各种子数据集时。这要求数据库内高效支持高级分析方法。在本文中，我们介绍了LEADS，一种新颖的SQL感知动态模型切片技术，用于为指定的SQL查询定制模型。LEADS通过专家混合（MoE）改进结构化数据的预测建模，并通过SQL感知门控网络保持效率。LEADS的核心是在数据库上训练多个专家子模型来构建通用模型。MoE扩展了建模能力，增强了有效性，并通过在推理期间通过SQL感知门控网络激活必要的专家来保持效率。为了支持数据库内分析，我们构建了一个推理扩展，将LEADS集成到PostgreSQL上。我们在真实世界数据集上的大量实验表明，LEADS始终优于基线模型，并且数据库内推理扩展相比传统解决方案显著降低了推理延迟。
英文摘要：
Relational database management systems (RDBMS) are widely used for the storage of structured data. To derive insights beyond statistical aggregation, we typically have to extract specific subdatasets from the database using conventional database operations, and then apply deep neural networks (DNN) training and inference on these subdatasets in a separate analytics system. The process can be prohibitively expensive, especially when there are various subdatasets extracted for different analytical purposes. This calls for efficient in-database support of advanced analytical methods. In this paper, we introduce LEADS, a novel SQL-aware dynamic model slicing technique to customize models for specified SQL queries. LEADS improves the predictive modeling of structured data via the mixture of experts (MoE) and maintains efficiency by a SQL-aware gating network. At the core of LEADS is the construction of a general model with multiple expert sub-models trained over the database. The MoE scales up the modeling capacity, enhances effectiveness, and preserves efficiency by activating necessary experts via the SQL-aware gating network during inference. To support in-database analytics, we build an inference extension that integrates LEADS onto PostgreSQL. Our extensive experiments on real-world datasets demonstrate that LEADS consistently outperforms the baseline models, and the in-database inference extension delivers a considerable reduction in inference latency compared to traditional solutions.
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。
