# SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sources

arXiv: https://arxiv.org/abs/2009.09471 · HTML: https://mlsanity.com/paper/2009.09471/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2020.

## Built on by (most cited first, 17 of 17)

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- [GenSyn: A Multi-stage Framework for Generating Synthetic Microdata using Macro Data Sources](https://mlsanity.com/paper/2212.05975/) (2022)
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- [IsoEx: an explainable unsupervised approach to process event logs cyber investigation](https://mlsanity.com/paper/2306.09260/) (2023)
- [MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation](https://mlsanity.com/paper/2310.19454/) (2023)
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## Abstract (arXiv)

A synthetic dataset is a data object that is generated programmatically, and it may be valuable to creating a single dataset from multiple sources when direct collection is difficult or costly. Although it is a fundamental step for many data science tasks, an efficient and standard framework is absent. In this paper, we study a specific synthetic data generation task called downscaling, a procedure to infer high-resolution, harder-to-collect information (e.g., individual level records) from many low-resolution, easy-to-collect sources, and propose a multi-stage framework called SYNC (Synthetic Data Generation via Gaussian Copula). For given low-resolution datasets, the central idea of SYNC is to fit Gaussian copula models to each of the low-resolution datasets in order to correctly capture dependencies and marginal distributions, and then sample from the fitted models to obtain the desired high-resolution subsets. Predictive models are then used to merge sampled subsets into one, and finally, sampled datasets are scaled according to low-resolution marginal constraints. We make four key contributions in this work: 1) propose a novel framework for generating individual level data from aggregated data sources by combining state-of-the-art machine learning and statistical techniques, 2) perform simulation studies to validate SYNC's performance as a synthetic data generation algorithm, 3) demonstrate its value as a feature engineering tool, as well as an alternative to data collection in situations where gathering is difficult through two real-world datasets, 4) release an easy-to-use framework implementation for reproducibility and scalability at the production level that easily incorporates new data.