Beyond Marginals: A Multi-Dimensional Evaluation Framework for Multi-Table Synthetic Data Generation
Synthetic data generation is critical for privacy compliance, machine learning augmentation, and software testing. While single-table evaluation is well established, multi-table (relational) synthesis, the dominant enterprise use case, lacks a unified evaluation framework. Existing approaches assess marginal column distributions in isolation, overlooking joint distributions, cross-table structural integrity, downstream utility, and production-readiness edge cases. We present SynEval, a six-dimensional evaluation framework for multi-table synthetic databases. SynEval jointly assesses per-column fidelity, multivariate structure preservation including a novel conditional distribution check, ...
| Authors | Aparana Gupta, Anurup Dey, Suyash Dwivedi |
|---|---|
| Primary category | cs.DB |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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