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Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries
Summary: Lemo is a cache-enhanced learned optimizer for concurrent multi-query workloads. A shared buffer caches subquery results with a replacement policy, leveraging cached data to guide plan generation in PostgreSQL.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 6750
- Venue
- SIGMOD
- Year
- 2023
- Pagerank
- 4.7563708e-05
- Overall Rank
- 7,326 | 49.09%
- DOI
-
10.1145/3626734
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 20 of 20 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 71 |
How Good Are Query Optimizers, Really? |
2016 |
VLDB |
0.00059446482 |
| 179 |
Efficient and Extensible Algorithms for Multi Query Optimization |
2000 |
SIGMOD |
0.00037637319 |
| 244 |
Continuously Adaptive Continuous Queries over Streams |
2002 |
SIGMOD |
0.00031037999 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 515 |
QPipe: A Simultaneously Pipelined Relational Query Engine |
2005 |
SIGMOD |
0.00021209491 |
| 634 |
Bao: Making Learned Query Optimization Practical |
2021 |
SIGMOD |
0.00018844568 |
| 934 |
SharedDB: Killing One Thousand Queries With One Stone |
2012 |
VLDB |
0.00015201246 |
| 977 |
Pipelining in Multi-Query Optimization |
2001 |
PODS |
0.000148774 |
| 1,296 |
The DataPath System: A Data-Centric Analytic Processing Engine for Large Data Warehouses |
2010 |
SIGMOD |
0.00012742585 |
| 1,430 |
A Scalable, Predictable Join Operator for Highly Concurrent Data Warehouses |
2009 |
VLDB |
0.0001202506 |
| 1,475 |
Efficient Exploitation of Similar Subexpressions for Query Processing |
2007 |
SIGMOD |
0.00011765071 |
| 2,090 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5668285e-05 |
| 2,928 |
Shared Workload Optimization |
2014 |
VLDB |
7.881485e-05 |
| 3,167 |
QueryFormer: A Tree Transformer Model for Query Plan Representation |
2022 |
VLDB |
7.4561078e-05 |
| 3,345 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1908499e-05 |
| 3,580 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
6.9460425e-05 |
| 3,729 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8078013e-05 |
| 4,263 |
The Case for Precision Sharing |
2004 |
VLDB |
6.3014952e-05 |
| 5,298 |
MQJoin: Efficient Shared Execution of Main-Memory Joins |
2016 |
VLDB |
5.5762694e-05 |
| 7,461 |
Scalable Multi-Query Execution using Reinforcement Learning |
2021 |
SIGMOD |
4.7193668e-05 |
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Paper |
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Venue |
Pagerank |
| 10,876 |
LASER: Buffer-Aware Learned Query Scheduling in Master-Standby Databases |
2025 |
VLDB |
4.1905499e-05 |
| 5,682 |
LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems |
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5.3752251e-05 |
| 9,581 |
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2025 |
SIGMOD |
4.3186744e-05 |
| 5,073 |
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2015 |
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5.7118738e-05 |
| 11,352 |
DeepO: A Learned Query Optimizer |
2022 |
SIGMOD |
4.1905499e-05 |
| 3,580 |
Query Performance Prediction for Concurrent Queries using Graph Embedding |
2020 |
VLDB |
6.9460425e-05 |
| 7,220 |
Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation |
2023 |
SIGMOD |
4.7926382e-05 |
| 6,639 |
Leveraging Query Logs and Machine Learning for Parametric Query Optimization |
2022 |
VLDB |
4.976781e-05 |
| 3,345 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1908499e-05 |
| 9,350 |
LIMAO: A Framework for Lifelong Modular Learned Query Optimization |
2025 |
VLDB |
4.3494621e-05 |