Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee
Summary: Proposes distribution precision as a strict error guarantee for AQP of group-by aggregates, enabling distribution-level accuracy rather than point estimates. Introduces measure-biased sampling and two in-memory indexes to support selective predicates and any aggregate estimate, delivering ~100x speedups with ~5% distribution error vs a commercial DB. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bolin Ding (Microsoft)
- 2. Silu Huang (University of Illinois Urbana-Champaign)
- 3. Surajit Chaudhuri (Microsoft)
- 4. Kaushik Chakrabarti (Microsoft)
- 5. Chi Wang (Microsoft)
BibTeX Citation
@inproceedings{ding_sigmod16,
title = {{Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee}},
author = {Ding, Bolin and Huang, Silu and Chaudhuri, Surajit and Chakrabarti, Kaushik and Wang, Chi},
series = {{SIGMOD} '16},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2882903.2915249},
url = {https://dl.acm.org/doi/10.1145/2882903.2915249},
year = {2016}
}
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