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)
Incoming Non-self Citations Over Time
Authors
- 1. Suraj Shetiya (University of Texas)
- 2. Saravanan Thirumuruganathan (Hamad Bin Khalifa University; Qatar Computing Research Institute)
- 3. Nick Koudas (University of Toronto)
- 4. Gautam Das (University of Texas)
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}
}
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