Approximate Query Processing for Interactive Data Science

Summary: IDEA: Interactive Data Exploration Accelerator for instant, visual, interactive data science. New AQP model treats aggregates as random variables, enabling reuse, stratified sampling, and on-the-fly tail indexes for rare subpopulations, with no precomputation. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 5409
Venue: SIGMOD
Year: 2017
Pagerank: 5.2240002e-05
Overall Rank: 9,760 | 33.04%
DOI: 10.1145/3035918.3056099

Incoming Non-self Citations Over Time

Authors

1. Tim Kraska (Brown University)

BibTeX Citation

@inproceedings{kraska_sigmod17,
        title = {{Approximate Query Processing for Interactive Data Science}},
        author = {Kraska, Tim},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3056099},
        url = {https://dl.acm.org/doi/10.1145/3035918.3056099},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,161	LAQy: Efficient and Reusable Query Approximations via Lazy Sampling	2023	SIGMOD	5.4752972e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank

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7	363	Approximate Query Processing: Taming the TeraBytes! A Tutorial	2001	VLDB
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9	3,366	AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics	2018	SIGMOD
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