Efficient Approximate Query Processing with Block Sampling
Summary: B-AQP: an AQP framework that samples at block/page granularity to match page-oriented I/O, drastically reducing data-loading overhead that record-level sampling incurs. Provides a priori error bounds and achieves up to 185× speedup vs. uniform sampling and ~4 orders faster than exact queries. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yuxuan Zhu (University of Illinois Urbana-Champaign)
- 2. Daniel Kang (University of Illinois Urbana-Champaign)
BibTeX Citation
@inproceedings{zhu_cidr25,
address = {Amsterdam, Netherlands},
series = {{CIDR} '25},
title = {{Efficient Approximate Query Processing with Block Sampling}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Zhu, Yuxuan and Kang, Daniel},
year = {2025}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 772 | VerdictDB: Universalizing Approximate Query Processing | 2018 | SIGMOD | 0.00014147905 |
| 819 | Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters | 2016 | SIGMOD | 0.00013815639 |
| 1,784 | Lambada: Interactive Data Analytics on Cold Data Using Serverless Cloud Infrastructure | 2020 | SIGMOD | 9.7726335e-05 |
| 2,413 | A Sampling Algebra for Aggregate Estimation | 2013 | VLDB | 8.6116764e-05 |
| 3,803 | A Bi-Level Bernoulli Scheme for Database Sampling | 2004 | SIGMOD | 7.1114677e-05 |
| 3,907 | Accelerating Approximate Aggregation Queries with Expensive Predicates | 2021 | VLDB | 7.0278233e-05 |
| 7,648 | Accelerating Aggregation Queries on Unstructured Streams of Data | 2023 | VLDB | 5.575838e-05 |
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