Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries
Summary: Two DL-based estimators for multi-attribute selectivity: autoregressive density estimation of the joint distribution and a supervised predictor (range-query support). Tackles featurization, workload awareness, and dynamic data, delivering fast, accurate, compact estimates for many predicates at low selectivity. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Shohedul Hasan (University of Texas)
- 2. Saravanan Thirumuruganathan (Hamad Bin Khalifa University; Qatar Computing Research Institute)
- 3. Jees Augustine (University of Texas)
- 4. Nick Koudas (University of Toronto)
- 5. Gautam Das (University of Texas)
BibTeX Citation
@inproceedings{hasan_sigmod20,
title = {{Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries}},
author = {Hasan, Shohedul and Thirumuruganathan, Saravanan and Augustine, Jees and Koudas, Nick and Das, Gautam},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389741},
url = {https://dl.acm.org/doi/10.1145/3318464.3389741},
year = {2020}
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