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An End-to-End Learning-based Cost Estimator

Summary: End-to-end learning-based cost estimator with a tree-structured model that jointly estimates cost and cardinality. Features span queries and physical operators; a pattern-based string embedding handles predicates without enumerating values, yielding superior accuracy over baselines on real data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12191
Venue
VLDB
Year
2020
Pagerank
0.00016434274
Overall Rank
806 | 94.40%
DOI
10.14778/3368289.3368296

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 4 of 104 citing papers.

Rank Citing Paper Year Venue Pagerank
10,859 Graph Transformers for Query Plan Representation: Potentials and Challenges 2025 VLDB 4.1945683e-05
10,868 LEAP: A Low-cost Spark SQL Query Optimizer using Pairwise Comparison 2025 VLDB 4.1945683e-05
10,880 RankPQO: Learning-to-Rank for Parametric Query Optimization 2025 VLDB 4.1945683e-05
11,350 DeepO: A Learned Query Optimizer 2022 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 13 of 13 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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