mlsanityData to 7 Oct 2026

Retrieve, Merge, Predict: Augmenting Tables with Data Lakes

arXiv 2402.06282 ↗First posted 9 Feb 2024cs.DB cs.LGcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 36 of its 86 references that are in the map. First posted on arXiv 9 Feb 2024.

Builds on

36 of 86 references.

References · 36

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24 more
  1. 2020AutoML to Date and Beyond: Challenges and Opportunitiescited by 63
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  4. 2021GitTables: A Large-Scale Corpus of Relational Tablescited by 44
  5. 2022Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learningcited by 38
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  11. 2023LakeBench: Benchmarks for Data Discovery over Data Lakescited by 9
  12. 2021Correlation Sketches for Approximate Join-Correlation Queriescited by 8
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Table Integration in Data Lakes Unleashed: Pairwise Integrability Judgment, Integrable Set Discovery, and Multi-Tuple Conflict Resolutioncited by 3
  2. 2025Something's Fishy In The Data Lake: A Critical Re-evaluation of Table Union Search Benchmarkscited by 1
  3. 2025RelationalFactQA: A Benchmark for Evaluating Tabular Fact Retrieval from Large Language Modelscited by 0
  4. 2026MosaicJoin: Compact Semantic Sketches for Value-Level Join Discoverycited by 0