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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
h55157da4d1966a28
Venue
VLDB
Year
2024
Pagerank
5.9639223e-05
Overall Rank
5,871 | 60.53%
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 11 of 11 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
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
30 SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets 2008 VLDB 0.00050495102
40 The Case for Learned Index Structures 2018 SIGMOD 0.00046284649
78 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00036684414
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034106982
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
149 Efficient Mid-Query Re-Optimization of Sub-Optimal Query Execution Plans 1998 SIGMOD 0.00028981723
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028672526
314 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00021282642
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
430 ALEX: An Updatable Adaptive Learned Index 2020 SIGMOD 0.00018409112
481 Robust Query Processing through Progressive Optimization 2004 SIGMOD 0.00017603972
512 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017050173
751 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.0001425375
779 FITing-Tree: A Data-aware Index Structure 2019 SIGMOD 0.00014030069
884 Dynamic Programming Strikes Back 2008 SIGMOD 0.00013267935
982 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012714044
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202282
1,199 Balsa: Learning a Query Optimizer Without Expert Demonstrations 2022 SIGMOD 0.00011563985
1,257 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011310561
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011226878
1,397 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010789242
1,603 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 0.00010097649
1,815 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5791737e-05
1,857 Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases 2020 VLDB 9.4929201e-05
2,004 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.2065719e-05
2,210 Lero: A Learning-to-Rank Query Optimizer 2023 VLDB 8.8257742e-05
2,522 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.3477168e-05
2,690 Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection 2022 VLDB 8.1258173e-05
2,772 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.035288e-05
2,834 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9560627e-05
2,842 Query Performance Prediction for Concurrent Queries using Graph Embedding 2020 VLDB 7.949193e-05
2,908 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.8742664e-05
3,051 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.7055931e-05
3,337 The Composable Data Management System Manifesto 2023 VLDB 7.4104865e-05
3,545 Computation Reuse in Analytics Job Service at Microsoft 2018 SIGMOD 7.2134803e-05
3,590 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.1865343e-05
3,682 openGauss: An Autonomous Database System 2021 VLDB 7.1013922e-05
3,949 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9052796e-05
3,965 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.8918628e-05
3,978 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.8807882e-05
4,258 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.6994722e-05
4,683 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4716143e-05
5,110 Steering Query Optimizers: A Practical Take on Big Data Workloads 2021 SIGMOD 6.269351e-05
5,456 Eraser: Eliminating Performance Regression on Learned Query Optimizer 2024 VLDB 6.1239873e-05
6,800 ROX: Run-time Optimization of XQueries 2009 SIGMOD 5.6792071e-05
7,429 Learning to be a Statistician: Learned Estimator for Number of Distinct Values 2022 VLDB 5.5300853e-05
7,915 Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems 2022 SIGMOD 5.4276987e-05
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