# METAM: Goal-Oriented Data Discovery

arXiv: https://arxiv.org/abs/2304.09068 · HTML: https://mlsanity.com/paper/2304.09068/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 68 references that are in the map.
- First posted on arXiv 18 Apr 2023.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Tutorial on Thompson Sampling](https://mlsanity.com/paper/1707.02038/) (2017)
- [Guarantees for Greedy Maximization of Non-submodular Functions with Applications](https://mlsanity.com/paper/1703.02100/) (2017)
- [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)
- [Leveraging Transitive Relations for Crowdsourced Joins](https://mlsanity.com/paper/1408.6916/) (2014)
- [Visus: An Interactive System for Automatic Machine Learning Model Building and Curation](https://mlsanity.com/paper/1907.02889/) (2019)
- [Correlation Sketches for Approximate Join-Correlation Queries](https://mlsanity.com/paper/2104.03353/) (2021)
- [Data Market Platforms: Trading Data Assets to Solve Data Problems](https://mlsanity.com/paper/2002.01047/) (2020)
- [Ver: View Discovery in the Wild](https://mlsanity.com/paper/2106.01543/) (2021)
- [ARDA: Automatic Relational Data Augmentation for Machine Learning](https://mlsanity.com/paper/2003.09758/) (2020)
- [Can Deep Neural Networks Predict Data Correlations from Column Names?](https://mlsanity.com/paper/2107.04553/) (2021)
- [Data Lake Organization](https://mlsanity.com/paper/1812.07024/) (2018)

## Built on by (most cited first, 4 of 4)

- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [Retrieve, Merge, Predict: Augmenting Tables with Data Lakes](https://mlsanity.com/paper/2402.06282/) (2024)
- [GNN: Graph Neural Network and Large Language Model for Data Discovery](https://mlsanity.com/paper/2408.13609/) (2024)
- [A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search](https://mlsanity.com/paper/2604.16394/) (2026)

## Abstract (arXiv)

Data is a central component of machine learning and causal inference tasks. The availability of large amounts of data from sources such as open data repositories, data lakes and data marketplaces creates an opportunity to augment data and boost those tasks' performance. However, augmentation techniques rely on a user manually discovering and shortlisting useful candidate augmentations. Existing solutions do not leverage the synergy between discovery and augmentation, thus under exploiting data. In this paper, we introduce METAM, a novel goal-oriented framework that queries the downstream task with a candidate dataset, forming a feedback loop that automatically steers the discovery and augmentation process. To select candidates efficiently, METAM leverages properties of the: i) data, ii) utility function, and iii) solution set size. We show METAM's theoretical guarantees and demonstrate those empirically on a broad set of tasks. All in all, we demonstrate the promise of goal-oriented data discovery to modern data science applications.