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Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s10796-020-09995-2openalex-snapshot
Cache-Based Multi-Query Optimization for Data-Intensive Scalable Computing Frameworks
inferredcites →https://doi.org/10.1016/0169-023x(94)90014-0openalex-snapshot
Improvements on a heuristic algorithm for multiple-query optimization
inferredcites →https://doi.org/10.1016/b978-012088469-8.50085-1openalex-snapshot
The Case for Precision Sharing
inferredcites →https://doi.org/10.1016/s0304-0208(08)73237-7openalex-snapshot
Algorithms for Knapsack Problems
inferredcites →https://doi.org/10.1109/cloud.2018.00042openalex-snapshot
Intermediate Data Caching Optimization for Multi-Stage and Parallel Big Data Frameworks
inferredcites →https://doi.org/10.1109/icde.1988.105474openalex-snapshot
Using common subexpressions to optimize multiple queries
inferredcites →https://doi.org/10.1109/icde.2012.106openalex-snapshot
Exploiting Common Subexpressions for Cloud Query Processing
inferredcites →https://doi.org/10.1109/icde.2013.6544837openalex-snapshot
Recycling in pipelined query evaluation
inferredcites →https://doi.org/10.1109/tc.2001.970573openalex-snapshot
LRFU: a spectrum of policies that subsumes the least recently used and least frequently used policies
inferredcites →https://doi.org/10.1145/1247480.1247540openalex-snapshot
Efficient exploitation of similar subexpressions for query processing
inferredcites →https://doi.org/10.1145/342009.335419openalex-snapshot
Efficient and extensible algorithms for multi query optimization
inferredcites →https://doi.org/10.1145/3448016.3452799openalex-snapshot
Scalable Multi-Query Execution using Reinforcement Learning
inferredcites →https://doi.org/10.1145/3448016.3452838openalex-snapshot
Bao: Making Learned Query Optimization Practical
inferredcites →https://doi.org/10.1145/3514221.3517885openalex-snapshot
Balsa: Learning a Query Optimizer Without Expert Demonstrations
inferredcites →https://doi.org/10.1145/564691.564698openalex-snapshot
Continuously adaptive continuous queries over streams
inferredcites →https://doi.org/10.14778/1687627.1687659openalex-snapshot
A scalable, predictable join operator for highly concurrent data warehouses
inferredcites →https://doi.org/10.14778/2732279.2732280openalex-snapshot
Shared workload optimization
inferredcites →https://doi.org/10.14778/2850583.2850594openalex-snapshot
How good are query optimizers, really?
inferredcites →https://doi.org/10.14778/3397230.3397238openalex-snapshot
Query performance prediction for concurrent queries using graph embedding
inferredcites →https://doi.org/10.14778/3565838.3565846openalex-snapshot
Cost-Based or Learning-Based?
inferredcites →https://doi.org/10.14778/3583140.3583160openalex-snapshot
Lero: A Learning-to-Rank Query Optimizer
inferredcites →https://doi.org/10.1609/aaai.v30i1.10139openalex-snapshot
Convolutional Neural Networks over Tree Structures for Programming Language Processing
inferredcites →https://doi.org/10.1609/aaai.v35i12.17325openalex-snapshot
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
inferredcites →https://doi.org/10.7155/jgaa.00186openalex-snapshot
The Knapsack Problem with Conflict Graphs
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/s00778-025-00936-6openalex-snapshot
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis
inferred← citeshttps://doi.org/10.1007/s10791-026-10515-0openalex-snapshot
Self–Optimizing AI data systems: a systematic literature review of CQLE-Aware query optimization, Edge–cloud offloading, Cloud/HPC orchestration, Data–Centric MLOps, and RAG
inferred← citeshttps://doi.org/10.1007/s10844-025-00921-zopenalex-snapshot
PolyCard: A learned cardinality estimator for intersection queries on spatial polygons
inferred← citeshttps://doi.org/10.1016/j.ins.2024.120650openalex-snapshot
A learned cost model for big data query processing
inferred← citeshttps://doi.org/10.1109/icde65448.2025.00133openalex-snapshot
LORE: Learning-Based Resource Recommendation for Big Data Queries
inferred← citeshttps://doi.org/10.1109/icebe62490.2024.00019openalex-snapshot
Saro: A Simplified Attention-Based Learning-to-Rank Query Optimizer
inferred← citeshttps://doi.org/10.1145/3626246.3654686openalex-snapshot
Machine Learning for Databases: Foundations, Paradigms, and Open problems
inferred← citeshttps://doi.org/10.1145/3654962openalex-snapshot
Optimizing Time Series Queries with Versions
inferred← citeshttps://doi.org/10.1145/3709659openalex-snapshot
An Elephant Under the Microscope: Analyzing the Interaction of Optimizer Components in PostgreSQL
inferred← citeshttps://doi.org/10.1145/3715902openalex-snapshot
Vehicle Trajectory Data Processing, Analytics, and Applications: A Survey
inferred← citeshttps://doi.org/10.1145/3725316openalex-snapshot
Learned Offline Query Planning via Bayesian Optimization
inferred← citeshttps://doi.org/10.1145/3802008openalex-snapshot
AgenticScholar: Agentic Data Management with Pipeline Orchestration for Scholarly Corpora
inferred← citeshttps://doi.org/10.1145/3802061openalex-snapshot
Finding Missed Optimizations in DBMSs through Unbalanced Short-circuit Query Construction
inferred← citeshttps://doi.org/10.14778/3742728.3742759openalex-snapshot
AQETuner: Reliable Query-Level Configuration Tuning for Analytical Query Engines
inferred← citeshttps://doi.org/10.14778/3749646.3749712openalex-snapshot
LIMAO: A Framework for Lifelong Modular Learned Query Optimization
inferred← citeshttps://doi.org/10.14778/3827998.3828034openalex-snapshot
Towards Industrial-Scale Parametric Query Optimization
inferred← citeshttps://doi.org/10.2139/ssrn.6927998openalex-snapshot
Deep Learning-Powered Query Optimization for MySQL
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