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LAQy: Efficient and Reusable Query Approximations via Lazy Sampling
Summary: LAQy enables lazy, reusable samples for online AQP that adapt to changing predicates. It merges and expands samples to cross-query reuse, avoiding per-query sampling, with 2.5–19.3x speedups on a code-generation-based in-memory engine under unpredictable workloads.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6677
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 4.5280102e-05
- Overall Rank
- 8,393 | 41.62%
- DOI
-
10.1145/3589319
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 23 of 23 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 14 |
Online Aggregation |
1997 |
SIGMOD |
0.0010801504 |
| 60 |
Efficiently Compiling Efficient Query Plans for Modern Hardware |
2011 |
VLDB |
0.00064439773 |
| 113 |
Encapsulation of Parallelism in the Volcano Query Processing System |
1990 |
SIGMOD |
0.00046764513 |
| 205 |
Monitoring Streams – A New Class of Data Management Applications |
2002 |
VLDB |
0.00034731577 |
| 402 |
Mergeable Summaries |
2012 |
PODS |
0.00024196343 |
| 608 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019235898 |
| 1,204 |
VerdictDB: Universalizing Approximate Query Processing |
2018 |
SIGMOD |
0.00013319541 |
| 1,323 |
Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters |
2016 |
SIGMOD |
0.00012601997 |
| 1,574 |
Approximate Query Processing: No Silver Bullet |
2017 |
SIGMOD |
0.00011287495 |
| 1,683 |
Cardinality Estimation: An Experimental Survey |
2018 |
VLDB |
0.00010922679 |
| 1,797 |
Effective Use of Block-Level Sampling in Statistics Estimation |
2004 |
SIGMOD |
0.00010523169 |
| 2,011 |
Rapid Sampling for Visualizations with Ordering Guarantees |
2015 |
VLDB |
9.7964875e-05 |
| 2,129 |
IDEBench: A Benchmark for Interactive Data Exploration |
2020 |
SIGMOD |
9.480002e-05 |
| 2,501 |
DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models |
2019 |
SIGMOD |
8.6453446e-05 |
| 2,651 |
HetExchange: Encapsulating heterogeneous CPU-GPU parallelism in JIT compiled engines |
2019 |
VLDB |
8.3694317e-05 |
| 3,378 |
General Incremental Sliding-Window Aggregation |
2015 |
VLDB |
7.1622572e-05 |
| 3,944 |
AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics |
2018 |
SIGMOD |
6.6078243e-05 |
| 4,326 |
Fast Queries Over Heterogeneous Data Through Engine Customization |
2016 |
VLDB |
6.288323e-05 |
| 5,005 |
Adaptive HTAP through Elastic Resource Scheduling |
2020 |
SIGMOD |
5.7641797e-05 |
| 5,530 |
Permutable Compiled Queries: Dynamically Adapting Compiled Queries without Recompiling |
2021 |
VLDB |
5.4554282e-05 |
| 8,240 |
Experiences with Approximating Queries in Microsoft’s Production Big-Data Clusters |
2019 |
VLDB |
4.5522563e-05 |
| 9,631 |
Approximate Query Processing for Interactive Data Science |
2017 |
SIGMOD |
4.3135157e-05 |
| 11,427 |
Accelerating Complex Analytics using Speculation |
2021 |
CIDR |
4.1945683e-05 |
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| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 2,365 |
The Analytical Bootstrap: a New Method for Fast Error Estimation in Approximate Query Processing |
2014 |
SIGMOD |
8.9551432e-05 |
| 6,493 |
Joins on Samples: A Theoretical Guide for Practitioners |
2020 |
VLDB |
5.0424713e-05 |
| 1,260 |
Dynamic Sample Selection for Approximate Query Processing |
2003 |
SIGMOD |
0.00012993347 |
| 11,285 |
Approximate Queries over Concurrent Updates |
2023 |
VLDB |
4.1945683e-05 |
| 3,944 |
AQP++: Connecting Approximate Query Processing With Aggregate Precomputation for Interactive Analytics |
2018 |
SIGMOD |
6.6078243e-05 |
| 1,323 |
Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters |
2016 |
SIGMOD |
0.00012601997 |
| 1,874 |
Knowing When You’re Wrong: Building Fast and Reliable Approximate Query Processing Systems |
2014 |
SIGMOD |
0.00010244443 |
| 2,580 |
Sample + Seek: Approximating Aggregates with Distribution Precision Guarantee |
2016 |
SIGMOD |
8.5058814e-05 |
| 6,740 |
Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing |
2021 |
SIGMOD |
4.944395e-05 |
| 10,337 |
Efficient Approximate Query Processing with Block Sampling |
2025 |
CIDR |
4.1945683e-05 |