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PilotScope: Steering Databases with Machine Learning Drivers

Summary: PilotScope is middleware for deploying AI4DB algorithms, separating ML-centric AI4DB drivers from engine-specific database interactors. This abstraction lowers integration cost and enables portable, cross-database benchmarking of tuning and optimization techniques. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13958
Venue
VLDB
Year
2024
Pagerank
5.8717744e-05
Overall Rank
6,462 | 55.67%
DOI
10.14778/3641204.3641209

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{zhu_vldb24,
        title = {{PilotScope: Steering Databases with Machine Learning Drivers}},
        author = {Zhu, Rong and Weng, Lianggui and Wei, Wenqing and Wu, Di and Peng, Jiazhen and Wang, Yifan and Ding, Bolin and Lian, Defu and Zheng, Bolong and Zhou, Jingren},
        journal = {PVLDB},
        series = {{VLDB} '24},
        volume = {17},
        number = {5},
        pages = {980--993},
        doi = {10.14778/3641204.3641209},
        url = {https://doi.org/10.14778/3641204.3641209},
        year = {2024}
}

Incoming Citations (Sorted by Pagerank)

Showing 9 of 9 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 50 of 51 cited papers.

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

Rank Cited Paper Year Venue Pagerank
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
30 SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets 2008 VLDB 0.00051174276
43 The Case for Learned Index Structures 2018 SIGMOD 0.00046060254
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
100 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034385207
151 Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans 1998 SIGMOD 0.00029161879
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
156 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028636811
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
334 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00020875082
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
447 ALEX: An Updatable Adaptive Learned Index 2020 SIGMOD 0.00018322593
492 Robust Query Processing through Progressive Optimization 2004 SIGMOD 0.0001756877
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
768 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014173242
790 FITing-Tree: A Data-aware Index Structure 2019 SIGMOD 0.0001401445
1,013 Dynamic Programming Strikes Back 2008 SIGMOD 0.00012652549
1,061 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012369764
1,122 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.0001209124
1,241 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011521639
1,256 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011457194
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011361878
1,481 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010644613
1,712 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 9.9492299e-05
1,904 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5040429e-05
1,949 Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases 2020 VLDB 9.430385e-05
1,988 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.3501502e-05
2,420 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.605257e-05
2,723 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.2049453e-05
2,740 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.1855759e-05
2,762 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1539867e-05
2,812 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 8.0979597e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
2,888 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.9941489e-05
3,343 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.4983591e-05
3,582 The Composable Data Management System Manifesto 2023 VLDB 7.2869486e-05
3,586 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.2834069e-05
3,605 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.2640711e-05
3,662 openGauss: An Autonomous Database System 2021 VLDB 7.2166682e-05
3,953 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.996368e-05
3,961 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.987575e-05
3,998 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9676473e-05
4,434 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7079088e-05
4,929 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4423294e-05
5,059 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.3807509e-05
5,573 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1682747e-05
6,688 ROX: Run-time Optimization of XQueries 2009 SIGMOD 5.8015211e-05
7,290 Learning to be a Statistician: Learned Estimator for Number of Distinct Values 2022 VLDB 5.6540503e-05
7,785 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.5450355e-05
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