The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
The Synthetic Data Vault
Abstract
The goal of this paper is to build a system that automatically creates synthetic data to enable data science endeavors. To achieve this, we present the Synthetic Data Vault (SDV), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name SDV. When implementing the SDV, we also developed an algorithm that computes statistics at the intersection of related database tables. We then used a state-of-the-art multivariate modeling approach to model this data. The SDV iterates through all possible relations, ultimately creating a model for the entire database. Once this model is computed, the same relational information allows the SDV to synthesize data by sampling from any part of the database. After building the SDV, we used it to generate synthetic data for five different publicly available datasets. We then published these datasets, and asked data scientists to develop predictive models for them as part of a crowdsourced experiment. By analyzing the outcomes, we show that synthetic data can successfully replace original data for data science. Our analysis indicates that there is no significant difference in the work produced by data scientists who used synthetic data as opposed to real data. We conclude that the SDV is a viable solution for synthetic data generation.
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Cites
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Mathematical Risk Analysis: Dependence, Risk Bounds, Optimal Allocations and Portfolios
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EXPERIMENTS IN PREDICTING BIODEGRADABILITY
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Artificial Intelligence in Society
OECD (2019) · doi:10.1787/eedfee77-en · Cited by 487
A Comprehensive Review on Generative AI for Education
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Fairness and Bias in Algorithmic Hiring: A Multidisciplinary Survey
Fabris, A., Baranowska, N., Dennis, M. J., Graus, D., Hacker, P., Saldivar, J., Zuiderveen Borgesius, F., & Biega, A. J. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3696457 · Cited by 117
Wang, Z., Myles, P., & Tucker, A. (2021) · Computational Intelligence · doi:10.1111/coin.12427 · Cited by 65
Enhancing discrete-event simulation with big data analytics: A review
Greasley, A., & Edwards, J. S. (2021) · Journal of the Operational Research Society · doi:10.1080/01605682.2019.1678406 · Cited by 63
Metadata
- Authors
- Patki, N., Wedge, R., & Veeramachaneni, K.
- Venue
- 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
- Publisher
- IEEE
- Published
- 2016-10
- Volume
- 2016
- Pages
- 399-410
- OpenAlex
- W2565167788
- Cited by
- 616
- References
- 10
- FWCI
- 8.17
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Patki, N., Wedge, R., & Veeramachaneni, K. (2016). The Synthetic Data Vault. 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). https://doi.org/10.1109/dsaa.2016.49
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
