@inproceedings{li2021cardinality,
author = {Li, Beibin and Lu, Yao and Wang, Chi and Kandula, Srikanth},
title = {Cardinality Estimation: Is Machine Learning a Silver Bullet?},
booktitle = {AIDB},
year = {2021},
month = {August},
abstract = {Cardinality estimation (CE) aims for high accuracy, small storage, fast building and low query answering latency. We analyze the upper error bounds of random uniform sampling for single-table CE and use them as the accuracy target for machine learning (ML)-based CE. Our analysis indicates
that ML-based CE exhibits no Pareto advantage over random uniform sampling but provides a tradeoff among the metrics of interest. We outline such tradeoffs and point out the scenarios when ML-based CE can be useful and when sampling can help.},
url = {http://approjects.co.za/?big=en-us/research/publication/cardinality-estimation-is-machine-learning-a-silver-bullet/},
}