Knowing When You’re Wrong: Building Fast and Reliable Approximate Query Processing Systems
Summary: Sampling-based AQP for large-scale analytics; error bars often fail on real workloads. Fast diagnostics of error-estimation failures and a pipeline that yields approximate answers with reliable bootstrap-based error bars at interactive speeds. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sameer Agarwal (University of California Berkeley)
- 2. Henry Milner (University of California Berkeley)
- 3. Ariel Kleiner (University of California Berkeley)
- 4. Ameet Talwalkar (University of California Berkeley)
- 5. Michael Jordan (University of California Berkeley)
- 6. Samuel Madden (Massachusetts Institute of Technology)
- 7. Barzan Mozafari (University of Michigan)
- 8. Ion Stoica (University of California Berkeley)
BibTeX Citation
@inproceedings{agarwal_sigmod14,
title = {{Knowing When You’re Wrong: Building Fast and Reliable Approximate Query Processing Systems}},
author = {Agarwal, Sameer and Milner, Henry and Kleiner, Ariel and Talwalkar, Ameet and Jordan, Michael and Madden, Samuel and Mozafari, Barzan and Stoica, Ion},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2593667},
url = {https://dl.acm.org/doi/10.1145/2588555.2593667},
year = {2014}
}
Incoming Citations (Sorted by Pagerank)
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