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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)

Paper ID
11494
Venue
VLDB
Year
2016
Pagerank
5.5281143e-05
Overall Rank
7,866 | 46.04%
DOI
10.14778/3007328.3007333

Incoming Non-self Citations Over Time

Authors

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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