Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs
Summary: Analyzes two classes of summary-based cardinality estimators for graph DBMS: optimistic estimators with precomputed statistics and pessimistic LP-based estimators. Using Cardinality Estimation Graphs (CEGs), demonstrates that path choice (max vs min weight) depends on query structure, unifying optimistic and pessimistic approaches for cross-optimization. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jeremy Chen (University of Waterloo)
- 2. Yuqing Huang (University of Waterloo)
- 3. Mushi Wang (University of Waterloo)
- 4. Semih Salihoglu (University of Waterloo)
- 5. Ken Salem (University of Waterloo)
BibTeX Citation
@article{chen_vldb22,
title = {{Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs}},
author = {Chen, Jeremy and Huang, Yuqing and Wang, Mushi and Salihoglu, Semih and Salem, Ken},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {8},
pages = {1533--1545},
doi = {10.14778/3529337.3529339},
url = {https://doi.org/10.14778/3529337.3529339},
year = {2022}
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