A Collaborative Framework for Tweaking Properties in A Synthetic Dataset
Summary: ASPECT modularizes synthetic-data generation: scale first, then compose independent property-enforcing tools rather than using monolithic generators. Users interactively select properties and enforcement order, observing per-property error and runtime trade-offs. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. J.W. Zhang (National University of Singapore)
- 2. Yu Wang (National University of Singapore)
- 3. Y.C. Tay (National University of Singapore)
BibTeX Citation
@article{zhang_vldb18,
title = {{A Collaborative Framework for Tweaking Properties in A Synthetic Dataset}},
author = {Zhang, J.W. and Wang, Yu and Tay, Y.C.},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {2010--2013},
doi = {10.14778/3229863.3236247},
url = {https://doi.org/10.14778/3229863.3236247},
year = {2018}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,404 | Data Generation for Application-Specific Benchmarking | 2011 | VLDB | 7.4411075e-05 |
| 7,866 | Dscaler: Synthetically Scaling A Given Relational Database | 2016 | VLDB | 5.5281143e-05 |
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