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Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning

Summary: Astrid blends traditional pruning sketches with deep learning to estimate string selectivity for prefix, substring, and suffix queries. It offers a query-type aware embedding and a revised neural language-model objective with an efficient optimizer, achieving state-of-the-art results on benchmarks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h652966c32e439e9f
Venue
VLDB
Year
2021
Pagerank
7.1877332e-05
Overall Rank
3,555 | 76.18%
DOI
10.14778/3436905.3436907
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{shetiya_vldb21,
        title = {{Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning}},
        author = {Shetiya, Suraj and Thirumuruganathan, Saravanan and Koudas, Nick and Das, Gautam},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {4},
        pages = {471--484},
        doi = {10.14778/3436905.3436907},
        url = {https://doi.org/10.14778/3436905.3436907},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 16 of 16 citing papers.

Rank Citing Paper Year Venue Pagerank
983 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012677179
1,175 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011630532
2,169 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.9151935e-05
2,348 Learned Cardinality Estimation: An In-depth Study 2022 SIGMOD 8.5910072e-05
3,269 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.463682e-05
4,971 Debunking the Myth of Join Ordering: Toward Robust SQL Analytics 2025 SIGMOD 6.3209003e-05
5,484 A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies 2024 VLDB 6.0980944e-05
6,725 Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis 2023 VLDB 5.6826988e-05
6,906 LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries 2024 SIGMOD 5.634971e-05
7,440 Cardinality Estimation of Approximate Substring Queries using Deep Learning 2022 VLDB 5.5118924e-05
10,070 Cardinality Estimation of LIKE Predicate Queries using Deep Learning 2025 SIGMOD 5.0733319e-05
10,094 SPACE: Cardinality Estimation for Path Queries Using Cardinality-Aware Sequence-based Learning 2025 SIGMOD 5.068372e-05
10,355 SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index 2026 SIGMOD 5.0156513e-05
10,370 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.0010514e-05
10,762 The Case For Language Model Approximated LIKE Predicate 2026 SIGMOD 4.9605343e-05
10,764 Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking 2026 SIGMOD 4.9605343e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 33 of 33 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061463175
37 Improved Histograms for Selectivity Estimation of Range Predicates 1996 SIGMOD 0.00047654188
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046249672
79 Practical Selectivity Estimation through Adaptive Sampling 1990 SIGMOD 0.00036394222
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035920657
142 Neo: A Learned Query Optimizer 2019 VLDB 0.00029114937
171 Wavelet-Based Histograms for Selectivity Estimation 1998 SIGMOD 0.00027063233
192 Fast Exact Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labeling 2013 SIGMOD 0.00025740385
254 The History of Histograms (abridged) 2003 VLDB 0.00022999992
277 Optimal Histograms with Quality Guarantees 1998 VLDB 0.0002235043
287 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.00022060616
320 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021128745
372 STHoles: A Multidimensional Workload-Aware Histogram 2001 SIGMOD 0.00019768921
405 Deep Unsupervised Cardinality Estimation 2020 VLDB 0.00019020138
453 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001801482
459 Distributed Representations of Tuples for Entity Resolution 2018 VLDB 0.00017863654
550 SageDB: A Learned Database System 2019 CIDR 0.00016525249
689 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014725904
690 Selectivity Estimation for Range Predicates using Lightweight Models 2019 VLDB 0.00014703258
745 Dynamic Multidimensional Histograms 2002 SIGMOD 0.00014267268
1,061 Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions 2011 VLDB 0.00012207625
1,068 VGRAM: Improving Performance of Approximate Queries on String Collections Using Variable-Length Grams 2007 VLDB 0.00012170201
1,164 Estimating Alphanumeric Selectivity in the Presence of Wildcards 1996 SIGMOD 0.0001170903
1,348 Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks 2020 SIGMOD 0.00010943046
1,466 Substring Selectivity Estimation 1999 PODS 0.00010546176
1,541 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 0.00010288394
1,583 Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries 2020 SIGMOD 0.00010148362
2,105 Selectivity Estimation For Boolean Queries 2000 PODS 9.0253094e-05
2,158 Cost-Based Variable-Length-Gram Selection for String Collections to Support Approximate Queries Efficiently 2008 SIGMOD 8.9272802e-05
2,228 Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models 2017 VLDB 8.7912743e-05
2,310 Selectivity Estimation in Extensible Databases - A Neural Network Approach 1998 VLDB 8.6493038e-05
2,981 Extending Q-Grams to Estimate Selectivity of String Matching with Low Edit Distance 2007 VLDB 7.7739779e-05
3,371 Conditional Selectivity for Statistics on Query Expressions 2004 SIGMOD 7.3493132e-05
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