Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries
Summary: Moment-based quantile sketch for mergeable, high-cardinality aggregations; 200-byte footprint and 50ns merges by tracking a compact set of moments, with estimation via method of moments and maximum entropy. Cascade boost for threshold predicates; <1% quantile error, ~15× lower overhead than alternatives, and end-to-end speedups up to 7× (MacroBase) and 60× (Druid). (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Edward Gan (Stanford University)
- 2. Jialin Ding (Stanford University)
- 3. Kai Sheng Tai (Stanford University)
- 4. Vatsal Sharan (Stanford University)
- 5. Peter Bailis (Stanford University)
BibTeX Citation
@article{gan_vldb18,
title = {{Moment-Based Quantile Sketches for Efficient High Cardinality Aggregation Queries}},
author = {Gan, Edward and Ding, Jialin and Tai, Kai Sheng and Sharan, Vatsal and Bailis, Peter},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {11},
pages = {1647--1660},
doi = {10.14778/3236187.3236212},
url = {https://doi.org/10.14778/3236187.3236212},
year = {2018}
}
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