Dscaler: Synthetically Scaling A Given Relational Database
Summary: Dscaler is the first solution to non-uniform dataset scaling, allowing each relational table to grow at a distinct rate. Its correlation database models fine-grained per-tuple PK–FK dependencies, yielding more faithful distributions and join aggregates than prior scalers. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. J.W. Zhang (National University of Singapore)
- 2. Y.C. Tay (National University of Singapore)
BibTeX Citation
@article{zhang_vldb16,
title = {{Dscaler: Synthetically Scaling A Given Relational Database}},
author = {Zhang, J.W. and Tay, Y.C.},
journal = {PVLDB},
series = {{VLDB} '16},
volume = {9},
number = {14},
doi = {10.14778/3007328.3007333},
url = {https://doi.org/10.14778/3007328.3007333},
year = {2016}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,037 | HYDRA: A Dynamic Big Data Regenerator | 2018 | VLDB | 5.5025567e-05 |
| 9,536 | Database Gyms | 2023 | CIDR | 5.2529727e-05 |
| 13,524 | A Collaborative Framework for Tweaking Properties in A Synthetic Dataset | 2018 | VLDB | - |
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Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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