We present an in-depth analysis of data discovery in data lakes, focusing on
table augmentation for given machine learning tasks. We analyze alternative
methods used in the three main steps: retrieving joinable tables, merging
information, and predicting with the resultant table. As data lakes, the paper
uses YADL (Yet Another Data Lake) -- a novel dataset we developed as a tool for
benchmarking this data discovery task -- and Open Data US, a well-referenced
real data lake. Through systematic exploration on both lakes, our study
outlines the importance of accurately retrieving join candidates and the
efficiency of simple merging methods. We report new insights on the benefits of
existing solutions and on their limitations, aiming at guiding future research
in this space.

数据湖中的数据发现在表增强中的应用进行了深入分析，研究了检索可连接表、信息合并和使用生成表进行预测的替代方法，并通过对 YADL 和 Open Data US 的系统性探索，总结了准确检索连接候选项的重要性和简单合并方法的效率。本研究报道了现有解决方案的优势和局限性，旨在指导未来该领域的研究。