ASM in Action: Fast and Practical Learned Cardinality Estimation
Author:
Affiliation:
1. GSAI, POSTECH, Pohang, Republic of Korea
2. EPFL, Lausanne, Switzerland
Funder
Institute of Information & communications Technology Planning & evaluation (IITP) grant funded by the Korea government (MSIT)
National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3626246.3654728
Reference14 articles.
1. N. Bruno and S. Chaudhuri. 2002. Exploiting statistics on query expressions for optimization. In SIGMOD. pp. 263--274.
2. M. Germain et al. 2015. MADE: masked autoencoder for distribution estimation. In ICML, Vol. 37. pp. 881--889.
3. 2021. Cardinality estimation in DBMS: a comprehensive benchmark evaluation;Han Y.;PVLDB,2021
4. B. Hilprecht et al. 2020. DeepDB: learn from data not from queries! PVLDB Vol. 13 7 (2020) pp. 992--1005.
5. K. Kim et al. 2022. Learned cardinality estimation: an in-depth study. In SIGMOD. pp. 1214--1227.
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