Table learning, which lies at the intersection of machine learning and modern database systems, has recently attracted growing attention. However, existing frameworks typically require explicit data export and extensive feature engineering, creating a high barrier for database practitioners. We present TLSQL (Table Learning Structured Query Language), a system that enables table learning directly over relational databases via SQL-like declarative specifications. TLSQL is implemented as a lightweight Python library that translates these specifications into standard SQL queries and structured learning task descriptions. The generated SQL queries are executed natively by the database engine, while the task descriptions are consumed by downstream table learning frameworks. This design allows users to focus on modeling and analysis rather than low-level data preparation and pipeline orchestration. Experiments on real-world datasets demonstrate that TLSQL effectively lowers the barrier to integrating machine learning into databasecentric workflows. Our code is available at https://github.com/rllm-project/tlsql/.

翻译：表学习作为机器学习与现代数据库系统的交叉领域，近年来受到日益增长的关注。然而，现有框架通常需要显式的数据导出和大量的特征工程，为数据库从业者设置了较高的使用门槛。本文提出TLSQL（面向表学习的结构化查询语言），该系统支持通过类SQL的声明式规范直接在关系型数据库上执行表学习任务。TLSQL实现为一个轻量级Python库，能够将此类规范转换为标准SQL查询及结构化学习任务描述。生成的SQL查询由数据库引擎原生执行，而任务描述则由下游表学习框架解析使用。该设计使用户能够专注于建模与分析，而非底层数据准备与流程编排。在真实数据集上的实验表明，TLSQL有效降低了将机器学习集成到以数据库为中心的工作流中的门槛。我们的代码发布于https://github.com/rllm-project/tlsql/。