Synner: Generating Realistic Synthetic Data
Summary: Synner is an interactive, declarative synthetic-data generator for field domains, distributions, and inter-field relations. It supports custom distributions, offers previews, and suggests generation specs from examples to produce realistic data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Miro Mannino (New York University)
- 2. Azza Abouzied (New York University)
BibTeX Citation
@inproceedings{mannino_sigmod20,
title = {{Synner: Generating Realistic Synthetic Data}},
author = {Mannino, Miro and Abouzied, Azza},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3384696},
url = {https://dl.acm.org/doi/10.1145/3318464.3384696},
year = {2020}
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