Relational database management systems (RDBMS) are widely used for the
storage and retrieval 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 respective subdatasets in a
separate machine learning system. The process can be prohibitively expensive,
especially when there are a combinatorial number of 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 subdatasets
specified by SQL queries. LEADS improves the predictive modeling of structured
data via the mixture of experts (MoE) technique and maintains inference
efficiency by a SQL-aware gating network. At the core of LEADS is the
construction of a general model with multiple expert sub-models via MoE trained
over the entire database. This SQL-aware MoE technique scales up the modeling
capacity, enhances effectiveness, and preserves efficiency by activating only
necessary experts via the gating network during inference. Additionally, we
introduce two regularization terms during the training process of LEADS to
strike a balance between effectiveness and efficiency. We also design and build
an in-database inference system, called INDICES, to support end-to-end advanced
structured data analytics by non-intrusively incorporating LEADS onto
PostgreSQL. Our extensive experiments on real-world datasets demonstrate that
LEADS consistently outperforms baseline models, and INDICES delivers effective
in-database analytics with a considerable reduction in inference latency
compared to traditional solutions.

使用 LEADS 技术，通过 SQL 查询定制模型，提高关系数据库上的预测建模能力和推断效率，并通过 INDICES 系统实现高效的结构化数据分析。

为结构化数据分析提供数据库内动态模型切片的能力

Powering In-Database Dynamic Model Slicing for Structured Data Analytics

The increasing use of statistical data analysis in enterprise applications
has created an arms race among database vendors to offer ever more
sophisticated in-database analytics. One challenge in this race is that each
new statistical technique must be implemented from scratch in the RDBMS, which
leads to a lengthy and complex development process. We argue that the root
cause for this overhead is the lack of a unified architecture for in-database
analytics. Our main contribution in this work is to take a step towards such a
unified architecture. A key benefit of our unified architecture is that
performance optimizations for analytics techniques can be studied generically
instead of an ad hoc, per-technique fashion. In particular, our technical
contributions are theoretical and empirical studies of two key factors that we
found impact performance: the order data is stored, and parallelization of
computations on a single-node multicore RDBMS. We demonstrate the feasibility
of our architecture by integrating several popular analytics techniques into
two commercial and one open-source RDBMS. Our architecture requires changes to
only a few dozen lines of code to integrate a new statistical technique. We
then compare our approach with the native analytics tools offered by the
commercial RDBMSes on various analytics tasks, and validate that our approach
achieves competitive or higher performance, while still achieving the same
quality.

本文针对数据库供应商在提供日益复杂的 in-database analytics 功能时面临的开发难题进行探讨，提出了一种统一的架构来优化表现，其中包括对数据存储顺序和计算并行化的研究，并成功地将多种流行的分析技术集成到商业和开源 RDBMS 中，证明其具有与商业 RDBMS 本地工具相当或更高的性能。