NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks

Summary: Models RAQ answers by learning query behavior, not data, enabling query distribution dependent error bounds. NeuroSketch implements this approach and delivers faster, more accurate RAQ evaluation across real, TPC-benchmark, and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6604
Venue: SIGMOD
Year: 2023
Pagerank: 4.3959645e-05
Overall Rank: 9,078 | 36.91%
DOI: 10.1145/3588954

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
7,118	ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation	2024	SIGMOD	4.8204951e-05
8,414	PairwiseHist: Fast, Accurate and Space-Efficient Approximate Query Processing with Data Compression	2024	VLDB	4.5135713e-05

Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 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	0.0010813443
203	Learned Cardinalities: Estimating Correlated Joins with Deep Learning	2019	CIDR	0.00034868567
606	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00019251186
659	The Making of TPC-DS	2006	VLDB	0.00018514913
752	Deep Unsupervised Cardinality Estimation	2020	VLDB	0.00017138049
905	NeuroCard: One Cardinality Estimator for All Tables	2021	VLDB	0.00015423174
1,161	VerdictDB: Universalizing Approximate Query Processing	2018	SIGMOD	0.00013579831
2,494	DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models	2019	SIGMOD	8.6457436e-05
3,924	A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation	2021	SIGMOD	6.6227223e-05
7,034	A Neural Database for Differentially Private Spatial Range Queries	2022	VLDB	4.8504321e-05
7,442	Selectivity Functions of Range Queries are Learnable*	2022	SIGMOD	4.7248554e-05
7,626	A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy	2023	SIGMOD	4.6886327e-05

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