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
- 2. Daniel Kang
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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 |
|---|---|---|---|---|
| 1,161 | VerdictDB: Universalizing Approximate Query Processing | 2018 | SIGMOD | 0.00013579831 |
| 1,320 | Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters | 2016 | SIGMOD | 0.00012606067 |
| 2,420 | Lambada: Interactive Data Analytics on Cold Data Using Serverless Cloud Infrastructure | 2020 | SIGMOD | 8.8474951e-05 |
| 2,995 | A Sampling Algebra for Aggregate Estimation | 2013 | VLDB | 7.7606324e-05 |
| 4,100 | A Bi-Level Bernoulli Scheme for Database Sampling | 2004 | SIGMOD | 6.4473679e-05 |
| 4,703 | Accelerating Approximate Aggregation Queries with Expensive Predicates | 2021 | VLDB | 5.9793615e-05 |
| 7,911 | Accelerating Aggregation Queries on Unstructured Streams of Data | 2023 | VLDB | 4.6143141e-05 |
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