Selectivity Estimation for Range Predicates using Lightweight Models
Summary: Framing selectivity for multi-dimensional range predicates as regression and evaluating lightweight neural nets and tree ensembles; naive use underperforms simple baselines. It introduces regression-label transformation and feature engineering, delivering fast, accurate estimates across diverse datasets for optimizers. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anshuman Dutt (Microsoft)
- 2. Chi Wang (Microsoft)
- 3. Azade Nazi (Google)
- 4. Srikanth Kandula (Microsoft)
- 5. Vivek Narasayya (Microsoft)
- 6. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@article{dutt_vldb19,
title = {{Selectivity Estimation for Range Predicates using Lightweight Models}},
author = {Dutt, Anshuman and Wang, Chi and Nazi, Azade and Kandula, Srikanth and Narasayya, Vivek and Chaudhuri, Surajit},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {9},
pages = {1044--1057},
doi = {10.14778/3329772.3329780},
url = {https://doi.org/10.14778/3329772.3329780},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
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