Summary of Tabsketchfm: Sketch-based Tabular Representation Learning For Data Discovery Over Data Lakes, by Aamod Khatiwada et al.
TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
by Aamod Khatiwada, Harsha Kokel, Ibrahim Abdelaziz, Subhajit Chaudhury, Julian Dolby, Oktie Hassanzadeh, Zhenhan Huang, Tejaswini Pedapati, Horst Samulowitz, Kavitha Srinivas
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TabSketchFM neural tabular model is designed for data discovery tasks over data lakes, specifically identifying unionable, joinable, or subset table pairs. The model utilizes a novel pre-training approach based on sketches to enhance its effectiveness. Finetuning the model yields significant improvements over previous state-of-the-art tabular neural models. An ablation study highlights the importance of specific sketches for various tasks. The model is further used for table search, where given a query table, it finds other tables in the corpus that satisfy certain conditions. Our results show substantial improvements in F1 scores compared to existing techniques and demonstrate significant transfer across datasets and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Enterprises are increasingly searching for relevant tables in their data lakes. A new model called TabSketchFM can help with this task. The model is a type of neural tabular model that can identify unionable, joinable, or subset table pairs. To make the model better, researchers proposed a new way to pre-train it using sketches. They then fine-tuned the model and found that it performed much better than other models in this area. The model was also tested on different datasets and showed great results, even when used for tasks it hadn’t been trained for. |
