Demonstration of ScroogeDB: Getting More Bang For the Buck with Deterministic Approximation in the Cloud

Summary: ScroogeDB lowers pay-per-byte cloud aggregation costs via deterministic approximate query processing, rewriting queries over on-the-fly synopses with 100%-coverage bounds. A BigQuery prototype interleaves synopsis generation and execution, visualizing precision and savings. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 12363
Venue: VLDB
Year: 2020
Pagerank: 5.093636e-05
Overall Rank: 11,815 | 18.94%
DOI: 10.14778/3415478.3415519

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Saehan Jo (Cornell University)
2. Jialing Pei (Cornell University)
3. Immanuel Trummer (Cornell University)

BibTeX Citation

@article{jo_vldb20,
        title = {{Demonstration of ScroogeDB: Getting More Bang For the Buck with Deterministic Approximation in the Cloud}},
        author = {Jo, Saehan and Pei, Jialing and Trummer, Immanuel},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {12},
        pages = {2961--2964},
        doi = {10.14778/3415478.3415519},
        url = {https://doi.org/10.14778/3415478.3415519},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
2,206	DAQ: A New Paradigm for Approximate Query Processing	2015	VLDB	8.957715e-05
3,595	Plato: Approximate Analytics over Compressed Time Series with Tight Deterministic Error Guarantees	2020	VLDB	7.2736195e-05
11,749	BitGourmet: Deterministic Approximation via Optimized Bit Selection	2020	CIDR	5.093636e-05

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