Relational data augmentation is a powerful technique for enhancing data analytics and improving machine learning models by incorporating columns from external datasets. However, it is challenging to efficiently discover relevant external tables to join with a given input table. Existing approaches rely on data discovery systems to identify joinable tables from external sources, typically based on overlap or containment. However, the sheer number of tables obtained from these systems results in irrelevant joins that need to be performed; this can be computationally expensive or even infeasible in practice. We address this limitation by proposing the use of efficient mutual information (MI) estimation for finding relevant joinable tables. We introduce a new sketching method that enables efficient evaluation of relationship discovery queries by estimating MI without materializing the joins and returning a smaller set of tables that are more likely to be relevant. We also demonstrate the effectiveness of our approach at approximating MI in extensive experiments using synthetic and real-world datasets.

翻译：关系数据增强是一种强大的技术，通过整合外部数据集中的列来提升数据分析效果并改进机器学习模型。然而，高效发现与给定输入表相关联的外部表进行连接仍面临挑战。现有方法依赖数据发现系统从外部来源识别可连接表，通常基于重叠或包含关系。但这些系统获取的表数量庞大，导致需要执行不相关的连接操作，在实际应用中可能带来高昂的计算成本甚至不可行。为突破这一局限，我们提出利用高效互信息估计方法发现相关可连接表。我们引入一种新型草图化方法，无需物化连接即可通过估计互信息高效评估关系发现查询，并返回更可能相关的精简表集合。通过使用合成数据集和真实数据集的广泛实验，我们验证了该方法在近似互信息方面的有效性。