用智能方法自动补全关系数据中的特征，提升模型效果且更省资源。

ML models critically depend on feature quality, yet in real-world settings, useful features are often distributed across multiple relational tables rather than a single dataset. Feature augmentation addresses this problem by automatically discovering and joining additional tables to enrich a base table with predictive features. However, scaling feature augmentation to complex schemas with many tables and multi-hop relationships is challenging. It requires exploring a large space of join paths, executing costly joins, and selecting useful features from noisy results. Existing approaches suffer from either limited effectiveness or efficiency. Restricting exploration to simple joins limits predictive performance, while more expressive methods rely on expensive training data, lack scalability, or fail to fully exploit schema-level semantics. We present Hippasus, a cost-aware, LLM-augmented feature discovery framework over relational schemas that addresses these challenges. Hippasus combines lightweight statistical signals with adaptive semantic reasoning, invoking stronger (LLM-based) analysis only when necessary. It further introduces efficient multi-way join execution with cross-path feature consolidation, and a hybrid feature selection strategy that integrates statistical relevance with semantic refinement. Experiments on real-world datasets show that Hippasus improves feature augmentation accuracy by up to 26.8% over state-of-the-art methods, while achieving a favorable effectiveness-cost tradeoff.