Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach
This addresses the need for more robust data integration in applications like data augmentation and machine learning, though it is an incremental improvement over existing similarity-based approaches.
The paper tackles the problem of finding joinable tables in data lakes by proposing PEXESO, a framework that uses high-dimensional vector embeddings and similarity predicates to handle misspellings and semantic joins, resulting in substantially more tables identified than equi-joins and outperforming other similarity-based methods.
Finding joinable tables in data lakes is key procedure in many applications such as data integration, data augmentation, data analysis, and data market. Traditional approaches that find equi-joinable tables are unable to deal with misspellings and different formats, nor do they capture any semantic joins. In this paper, we propose PEXESO, a framework for joinable table discovery in data lakes. We embed textual values as high-dimensional vectors and join columns under similarity predicates on high-dimensional vectors, hence to address the limitations of equi-join approaches and identify more meaningful results. To efficiently find joinable tables with similarity, we propose a block-and-verify method that utilizes pivot-based filtering. A partitioning technique is developed to cope with the case when the data lake is large and the index cannot fit in main memory. An experimental evaluation on real datasets shows that our solution identifies substantially more tables than equi-joins and outperforms other similarity-based options, and the join results are useful in data enrichment for machine learning tasks. The experiments also demonstrate the efficiency of the proposed method.