PolyJoin: Multi-key Semantic Joinable Table Search in Data Lakes
Authors: Xuming Hu, Chuan Lei, Xiao Qin, Asterios Katsifodimos, Christos Faloutsos, Huzefa Rangwala


Reviewer paper summaries: It introduces a method for finding multi-key joinable tables. To that end, it trains (fine-tunes) a BERT-based model using a hierarchical contrastive learning loss.
Summary of strengths: * Well-written paper on a practical problem 

* Good experimental analysis and solid results
Summary of weaknesses: * A data discovery model or system needs to be fast and scalable to be useful. What is the inference latency? How does that change with increasing table sizes (rows and columns)?  How does the prediction performance change with scale?  

* The paper misses several earlier works from the database community, e.g,
   * OmniMatch: Effective Self-Supervised Any-Join Discovery in Tabular Data Repositories https://arxiv.org/pdf/2403.07653
   * Semantics-aware dataset discovery from data lakes with contextualized column-based representation learning https://www.vldb.org/pvldb/vol16/p1726-fan.pdf
   * Discovery of Semantic Non-Syntactic Joins https://ceur-ws.org/Vol-3653/short3.pdf
   * WarpGate: A Semantic Join Discovery System for Cloud Data Warehouses https://arxiv.org/pdf/2212.14155
Comments, suggestions and typos: None.
Overall assessment scores: 2.5