# Retrieve, Merge, Predict: Augmenting Tables with Data Lakes

arXiv: https://arxiv.org/abs/2402.06282 · HTML: https://mlsanity.com/paper/2402.06282/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 86 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 30 of 36)

- [Revisiting Deep Learning Models for Tabular Data](https://mlsanity.com/paper/2106.11959/) (2021)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [When Do Neural Nets Outperform Boosted Trees on Tabular Data?](https://mlsanity.com/paper/2305.02997/) (2023)
- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023](https://mlsanity.com/paper/2307.14338/) (2023)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [Relational Deep Learning: Graph Representation Learning on Relational Databases](https://mlsanity.com/paper/2312.04615/) (2023)
- [On the consistency of supervised learning with missing values](https://mlsanity.com/paper/1902.06931/) (2019)
- [GitTables: A Large-Scale Corpus of Relational Tables](https://mlsanity.com/paper/2106.07258/) (2021)
- [Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning](https://mlsanity.com/paper/2210.01922/) (2022)
- [SANTOS: Relationship-based Semantic Table Union Search](https://mlsanity.com/paper/2209.13589/) (2022)
- [LSH Ensemble: Internet-Scale Domain Search](https://mlsanity.com/paper/1603.07410/) (2016)
- [Auctus: A Dataset Search Engine for Data Augmentation](https://mlsanity.com/paper/2102.05716/) (2021)
- [DeepJoin: Joinable Table Discovery with Pre-trained Language Models](https://mlsanity.com/paper/2212.07588/) (2022)
- [WarpGate: A Semantic Join Discovery System for Cloud Data Warehouses](https://mlsanity.com/paper/2212.14155/) (2022)
- [LakeBench: Benchmarks for Data Discovery over Data Lakes](https://mlsanity.com/paper/2307.04217/) (2023)
- [Correlation Sketches for Approximate Join-Correlation Queries](https://mlsanity.com/paper/2104.03353/) (2021)
- [SilkMoth: An Efficient Method for Finding Related Sets with Maximum Matching Constraints](https://mlsanity.com/paper/1704.04738/) (2017)
- [TabSim: A Siamese Neural Network for Accurate Estimation of Table Similarity](https://mlsanity.com/paper/2008.10856/) (2020)
- [Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?](https://mlsanity.com/paper/1704.00485/) (2017)
- [Ver: View Discovery in the Wild](https://mlsanity.com/paper/2106.01543/) (2021)
- [Table Enrichment System for Machine Learning](https://mlsanity.com/paper/2204.08235/) (2022)
- [DIALITE: Discover, Align and Integrate Open Data Tables](https://mlsanity.com/paper/2304.08285/) (2023)

## Built on by (most cited first, 5 of 5)

- [Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolution](https://mlsanity.com/paper/2412.00324/) (2024)
- [Something's Fishy In The Data Lake: A Critical Re-evaluation of Table Union Search Benchmarks](https://mlsanity.com/paper/2505.21329/) (2025)
- [RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Models](https://mlsanity.com/paper/2505.21409/) (2025)
- [MosaicJoin: Compact Semantic Sketches for Value-Level Join Discovery](https://mlsanity.com/paper/2607.21781/) (2026)
- [ML Engineering Agents - A Data Management Perspective](https://mlsanity.com/paper/2610.16919/) (2026)

## Abstract (arXiv)

Machine-learning from a disparate set of tables, a data lake, requires assembling features by merging and aggregating tables. Data discovery can extend autoML to data tables by automating these steps. We present an in-depth analysis of such automated table augmentation for machine learning tasks, analyzing different methods for the three main steps: retrieving joinable tables, merging information, and predicting with the resultant table. We use two data lakes: Open Data US, a well-referenced real data lake, and a novel semi-synthetic dataset, YADL (Yet Another Data Lake), which we developed as a tool for benchmarking this data discovery task. Systematic exploration on both lakes outlines 1) the importance of accurately retrieving candidate tables to join, 2) the efficiency of simple merging methods, and 3) the resilience of tree-based learners to noisy conditions. Our experimental environment is easily reproducible and based on open data, to foster more research on feature engineering, autoML, and learning in data lakes.