Continuously update the autonomous database works based on our past tutorials.
Kindly let us know if we have missed any great papers. Thank you!
- 0. Survey and Tutorial (15)
- 1. Database Configuration
- 2. Query Optimization
- 3. Workload Scheduling (2)
- 4. Database Design
- 5. Database Monitoring (9)
- 6. Database Diagnosis
- 7. General Techniques
- 8. Database Frameworks (16)
- 9. Demonstrations
- 10. Talks
- S1. Large Language Models Meet Database (7)
- S2. Open Datasets And SQLs (3)
Database meets deep learning: Challenges and opportunities.
Wei Wang, Meihui Zhang, Gang Chen, et al. SIGMOD Record, 2016. [paper]
Database Meets Artificial Intelligence: A Survey.
Xuanhe Zhou, Chengliang Chai, Guoliang Li, et al. TKDE, 2020. [paper]
A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration.
Hai Lan, Zhifeng Bao, Yuwei Peng. Data Science and Engineering, 2021. [paper]
A Survey on Deep Reinforcement Learning for Data Processing and Analytics.
Qingpeng Cai, Can Cui, Yiyuan Xiong, et al. TKDE, 2022. [paper]
Automatic Database Knob Tuning: A Survey.
Xinyang Zhao, Xuanhe Zhou, Guoliang Li. TKDE, 2023. [paper] [code]
From auto-tuning one size fits all to self-designed and learned data-intensive systems.
Stratos Idreos, Tim Kraska. SIGMOD, 2019. [paper]
Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems.
Jiaheng Lu, Yuxing Chen, Herodotos Herodotou, Shivnath Babu. VLDB, 2019. [paper] [slides]
Tutorial: Adaptive Replication and Partitioning in Data Systems.
Brad Glasbergen, Michael Abebe, Khuzaima Daudjee. Middleware, 2018. [paper]
A Tutorial on Learned Multi-dimensional Indexes.
Abdullah Al-Mamun, Hao Wu, Walid G. Aref. SIGSPATIAL, 2020. [paper]
AI Meets Database: AI4DB and DB4AI.
Guoliang Li, Xuanhe Zhou, Lei Cao. SIGMOD, 2021. [paper] [slides]
Machine Learning for Databases.
Guoliang Li, Xuanhe Zhou, Lei Cao. VLDB, 2021. [paper][slides]
Machine Learning for Cloud Data Systems: the Promise, the Progress, and the Path Forward.
Alekh Jindal, Matteo Interlandi. VLDB, 2021. [paper]
Workload-Aware Performance Tuning for Autonomous DBMSs.
Zhengtong Yan, Jiaheng Lu, Naresh Chainani, et al. ICDE, 2021. [paper]
Learned Query Optimizer: At the Forefront of AI-Driven Databases.
Zhu, Rong, Ziniu Wu, Chengliang Chai, et al. EDBT, 2022. [paper]
From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management.
Immanuel Trummer. VLDB, 2022. [paper]
PGTune: https://pgtune.leopard.in.ua.
OpenTuner: An Extensible Framework for Program Autotuning
Ansel J, Kamil S, Veeramachaneni K, et al. PACT, 2014. [paper]
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
Zhu Y, Liu J, Guo M, et al. SoCC, 2017. [paper]
Tuning Database Configuration Parameters with iTuned
Duan, S., Thummala, V., & Babu, S. VLDB, 2009. [paper]
Automatic database management system tuning through large-scale machine learning
Van Aken D, Pavlo A, Gordon G J, et al. SIGMOD, 2017. [paper]
Black or White? How to Develop an AutoTuner for Memory-based Analytics
Kunjir M, Babu S. SIGMOD, 2020. [paper]
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases
Zhang X, Wu H, Chang Z, et al. SIGMOD, 2021. [paper]
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions
Cereda S, Valladares S, Cremonesi P, et al. VLDB, 2021. [paper]
Towards Dynamic and Safe Configuration Tuning for Cloud Databases
Zhang X, Wu H, Li Y, et al. SIGMOD, 2022. [paper]
LlamaTune: Sample-Efficient DBMS Configuration Tuning
Kanellis K, Ding C, Kroth B, et al. VLDB, 2022. [paper]
iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases
Jian Tan, Tieying Zhang, Feifei Li, et al. VLDB, 2019. [paper]
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning
Ji Zhang, Yu Liu, Ke Zhou, Guoliang Li, et al. SIGMOD, 2019. [paper]
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning
Li G, Zhou X, Li S, et al. VLDB, 2019. [paper]
Watuning: A workload-aware tuning system with attention-based deep reinforcement learning
Ge J K, Chai Y F, Chai Y P. JCST, 2021. [paper]
The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that "Read the Manual"
Trummer I. VLDB, 2021. [paper]
DB-BERT: a Database Tuning Tool that “Reads the Manual”
Trummer I. SIGMOD, 2022. [paper]
HUNTER- An Online Cloud Database Hybrid Tuning System for Personalized Requirements
Cai B, Liu Y, Zhang C, et al. SIGMOD, 2022. [paper]
SARD: A statistical approach for ranking database tuning parameters
Debnath B K, Lilja D J, Mokbel M F. ICDE Workshops 2008. [paper]
Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs
Kanellis K, Alagappan R, Venkataraman S. HotStorage 2020. [paper]
An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems
Van Aken D, Yang D, Brillard S, et al. VLDB, 2021. [paper]
Facilitating Database Tuning with Hyper-Parameter Optimization- A Comprehensive Experimental Evaluation
Zhang X, Chang Z, Li Y, et al. VLDB, 2022. [paper]
Selecting subexpressions to materialize at datacenter scale
A. Jindal, K. Karanasos, S. Rao, and H. Patel. PVLDB, 11(7):800–812, 2018. [paper]
Automated generation of materialized views in Oracle
Ahmed, R., Bello, R., Witkowski, A., & Kumar, P. (2020). VLDB, 2020. [paper]
Computation reuse in analytics job service at microsoft
Jindal, A., Qiao, S., Patel, H., Yin, Z., Di, J., Bag, M., Friedman, M., Lin, Y., Karanasos, K. and Rao, S., SIGMOD, 2018 (pp. 191-203). [paper]
Automatic View Generation for Equivalent Subqueries with Deep Learning and Reinforcement Learning
Yuan, H., Sun, J., & Li, G. (2020). ICDE, 2020. [paper]
An Autonomous Materialized View Management System with Deep Reinforcement Learning
Han, Y., Li, G., Yuan, H., & Sun, J. ICDE, 2021. [paper]
AutoView: An Autonomous Materialized View Management System with Encoder-Reducer
Han, Y., Li, G., Yuan, H. and Sun, J., TKDE, 2022. [paper]
Dynamic Materialized View Management using Graph Neural Network
Yue Han, Chengliang Chai, Jiabin Liu, Guoliang Li, Chuangxian Wei, Chaoqun Zhan. ICDE 2023. [paper]
A novel coral reefs optimization algorithm for materialized view selection in data warehouse environments
Azgomi, H. and Sohrabi, M.K., Applied Intelligence, 2019, 49, pp.3965-3989. [paper]
[EA & B] Jan Kossmann, Stefan Halfpap, Marcel Jankrift, Rainer Schlosser: Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms. Proc. VLDB Endow. 13(11): 2382-2395 (2020) [paper]
[Industry, Microsoft Azure] Sudipto Das, Miroslav Grbic, Igor Ilic, Isidora Jovandic, Andrija Jovanovic, Vivek R. Narasayya, Miodrag Radulovic, Maja Stikic, Gaoxiang Xu, Surajit Chaudhuri: Automatically Indexing Millions of Databases in Microsoft Azure SQL Database. SIGMOD Conference 2019: 666-679 [paper]
[Industry, Meta] Ritwik Yadav, Satyanarayana R. Valluri, Mohamed Zait: AIM: A practical approach to automated index management for SQL databases. ICDE 2023 [paper]
[Heuristic-based, AutoAdmin] Surajit Chaudhuri, Vivek R. Narasayya: An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. VLDB 1997: 146-155 [paper]
[Heuristic-based, DB2Advis] Gary Valentin, Michael Zuliani, Daniel C. Zilio, Guy M. Lohman, Alan Skelley: DB2 Advisor: An Optimizer Smart Enough to Recommend Its Own Indexes. ICDE 2000: 101-110 [paper]
[Heuristic-based, Relaxation] Nicolas Bruno, Surajit Chaudhuri: Automatic Physical Database Tuning: A Relaxation-based Approach. SIGMOD Conference 2005: 227-238 [paper]
[Heuristic-based, COLT] Karl Schnaitter, Serge Abiteboul, Tova Milo, Neoklis Polyzotis: On-Line Index Selection for Shifting Workloads. ICDE Workshops 2007: 459-468 [paper]
[Heuristic-based, Extend] Rainer Schlosser, Jan Kossmann, Martin Boissier: Efficient Scalable Multi-attribute Index Selection Using Recursive Strategies. ICDE 2019: 1238-1249 [paper]
[Learning-based, DQN] Hai Lan, Zhifeng Bao, Yuwei Peng: An Index Advisor Using Deep Reinforcement Learning. CIKM 2020: 2105-2108 [paper]
[Learning-based, DQN] Zahra Sadri, Le Gruenwald, Eleazar Leal: Online Index Selection Using Deep Reinforcement Learning for a Cluster Database. ICDE Workshops 2020: 158-161 [paper]
[Learning-based, DQN] Gabriel Paludo Licks, Júlia Mara Colleoni Couto, Priscilla de Fátima Miehe, Renata De Paris, Duncan Dubugras A. Ruiz, Felipe Meneguzzi: SmartIX: A Database Indexing Agent based on Reinforcement Learning. Appl. Intell. 50(8): 2575-2588 (2020) [paper]
[Learning-based, DQN] Vishal Sharma, Curtis E. Dyreson, Nicholas Flann: MANTIS: Multiple Type and Attribute Index Selection using Deep Reinforcement Learning. IDEAS 2021: 56-64 [paper]
[Learning-based, DQN] Yu Yan, Shun Yao, Hongzhi Wang, Meng Gao: Index selection for NoSQL database with deep reinforcement learning. Inf. Sci. 561: 20-30 (2021) [paper]
[Learning-based, DQN] Vishal Sharma, Curtis E. Dyreson: Indexer++: Workload-aware Online Index Tuning with Transformers and Reinforcement Learning. SAC 2022: 372-380 [paper]
[Learning-based, MAB] R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic: DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees. ICDE 2021: 600-611 [paper]
[Learning-based, MAB] R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, Renata Borovica-Gajic: HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning. Proc. VLDB Endow. 16(2): 216-229 (2022) [paper]
[Learning-based, MCTS] Xuanhe Zhou, Luyang Liu, Wenbo Li, Lianyuan Jin, Shifu Li, Tianqing Wang, Jianhua Feng: AutoIndex: An Incremental Index Management System for Dynamic Workloads. ICDE 2022: 2196-2208 [paper]
[Learning-based, MCTS] Wentao Wu, Chi Wang, Tarique Siddiqui, Junxiong Wang, Vivek R. Narasayya, Surajit Chaudhuri, Philip A. Bernstein: Budget-aware Index Tuning with Reinforcement Learning. SIGMOD Conference 2022: 1528-1541 [paper]
[Optimization, Learned Cost] Bailu Ding, Sudipto Das, Ryan Marcus, Wentao Wu, Surajit Chaudhuri, Vivek R. Narasayya: AI Meets AI: Leveraging Query Executions to Improve Index Recommendations. SIGMOD Conference 2019: 1241-1258 [paper]
[Optimization, Learned Cost] Jiachen Shi, Gao Cong, Xiaoli Li: Learned Index Benefits: Machine Learning Based Index Performance Estimation. Proc. VLDB Endow. 15(13): 3950-3962 (2022) [paper]
[Optimization, Learned Cost] Jianling Gao, Nan Zhao, Ning Wang, Shuang Hao: SmartIndex: An Index Advisor with Learned Cost Estimator. CIKM 2022: 4853-4856 [paper]
[Optimization, Workload Summarization] Tarique Siddiqui, Saehan Jo, Wentao Wu, Chi Wang, Vivek R. Narasayya, Surajit Chaudhuri: ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning. SIGMOD Conference 2022: 660-673 [paper]
[Optimization, What-if Call] Tarique Siddiqui, Wentao Wu, Vivek R. Narasayya, Surajit Chaudhuri: DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning. Proc. VLDB Endow. 15(10): 2019-2031 (2022) [paper]
Automating physical database design in a parallel database.
Jun Rao, Chun Zhang, Nimrod Megiddo, Guy M. Lohman. SIGMOD, 2002. [paper]
Schism: a Workload-Driven Approach to Database Replication and Partitioning.
Carlo Curino, Yang Zhang, Evan P. C. Jones, Samuel Madden. PVLDB, 2010. [paper]
Locality-aware partitioning in parallel database systems.
Erfan Zamanian, Carsten Binnig, Abdallah Salama. SIGMOD, 2015. [paper]
Query centric partitioning and allocation for partially replicated database systems.
Tilmann Rabl, Hans-Arno Jacobsen. SIGMOD, 2017. [paper]
Workload-driven horizontal partitioning and pruning for large HTAP systems.
Martin Boissier, Kurzynski Daniel. ICDE Workshop, 2018. [paper]
Towards learning a partitioning advisor with deep reinforcement learning.
Benjamin Hilprecht, Carsten Binnig, Uwe Röhm. aiDM@SIGMOD, 2019. [paper]
Automated vertical partitioning with deep reinforcement learning.
Campero Durand G, Piriyev R, Pinnecke M, et al. ADBIS, 2019. [paper]
Fast and effective distribution-key recommendation for amazon redshift.
Panos Parchas, Yonatan Naamad, Peter Van Bouwel, et al. PVLDB, 2020. [paper]
Adaptive partitioning and indexing for in situ query processing.
Olma, M., Karpathiotakis, M., Alagiannis, I., Athanassoulis, et al. VLDB Journal. [paper]
Learning a Partitioning Advisor for Cloud Databases.
Benjamin Hilprecht, Carsten Binnig, Uwe Röhm. SIGMOD, 2020. [paper]
Grep: A Graph Learning Based Database Partitioning System.
Xuanhe Zhou, Guoliang Li, Jianhua Feng, et al. SIGMOD, 2023. [paper] [demo]
Universal Database Optimization using Reinforcement Learning
Wang J, Trummer I, Basu D. VLDB, 2021. [paper]
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning
Xinyi Zhang, Zhuo Chang, HONG WU, et al. SIGMOD, 2023. [paper]
(note other interesting problems like text2SQL are not within the scope)
[Rewrite Rules] Béatrice Finance, Georges Gardarin. A Rule-Based Query Rewriter in an Extensible DBMS. ICDE 1991. [paper]
[Rewrite Rules] Hamid Pirahesh, Joseph M. Hellerstein, Waqar Hasan. Extensible/Rule Based Query Rewrite Optimization in Starburst. SIGMOD Conference 1992. [paper]
[Cost/Heuristic Rewrite] Rafi Ahmed, Allison W. Lee, Andrew Witkowski, et al. Cost-Based Query Transformation in Oracle. VLDB 2006: 1026-1036. [paper]
[Heuristic Rewrite] De Araújo, A. H. M., Monteiro, J. M., Antônio, J., De Macêdo, F., Tavares, J. A., Brayner, A., & Lifschitz, S. (2014). ARe-SQL: An Online, Automatic and Non-Intrusive Approach for Rewriting SQL Queries. JIDM, 2014. [paper]
[Semantic Equivalence] Shumo Chu, Konstantin Weitz, Alvin Cheung, Dan Suciu. HoTTSQL: proving query rewrites with univalent SQL semantics. PLDI 2017: 510-524. [paper]
[Optimization Engine] Begoli, E., Camacho-Rodríguez, J., Hyde, J., Mior, M. J., & Lemire, D. (2018). Apache calcite: A foundational framework for optimized query processing over heterogeneous data sources. SIGMOD, 2018. [paper]
[Map-Reduce Rewrite] Partho Sarthi, Kaushik Rajan, Akash Lal, Abhishek Modi, et al. Generalized Sub-Query Fusion for Eliminating Redundant I/O from Big-Data Queries. OSDI 2020: 209-224. [paper]
[Streaming] Wentao Wu, Philip A. Bernstein, Alex Raizman, Christina Pavlopoulou. Cost-based Query Rewriting Techniques for Optimizing Aggregates Over Correlated Windows. CoRR abs/2008.12379 (2020) [paper]
[Rewrite Rules] Zhaoguo Wang, Zhou Zhou, Yicun Yang, Haoran Ding, Gansen Hu, Ding Ding, Chuzhe Tang, Haibo Chen, Jinyang Li. WeTune: Automatic Discovery and Verification of Query Rewrite Rules. SIGMOD Conference 2022: 94-107. [paper]
[Rewrite Rules] Qiushi Bai, Sadeem Alsudais, Chen Li. QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting. arXiv, 2023. [paper]
[Predicate Rewrite] Qi Zhou, Joy Arulraj, Shamkant B. Navathe, William Harris, Jinpeng Wu. Sia : Optimizing Queries using Learned Predicates. SIGMOD, 2021. [paper]
[Rewrite Strategy] Xuanhe Zhou, Guoliang Li, Chengliang Chai, Jianhua Feng. A Learned Query Rewrite System using Monte Carlo Tree Search. VLDB, 2022. [paper]
[Card, Query-based] Dutt, A., Wang, C., Nazi, A., Kandula, S., Narasayya, V., & Chaudhuri, S. (2018). Selectivity estimation for range predicates using lightweight models. Proceedings of the VLDB Endowment, 12(9), 1044–1057, 2018. [paper]
[Card, Query-based] Kipf A, Kipf T, Radke B, et al. Learned cardinalities: Estimating correlated joins with deep learning. CIDR, 2019. [paper]
[Card, Query-based] Woltmann L, Hartmann C, Thiele M, et al. Cardinality estimation with local deep learning models. aiDM, 2019. [paper]
[Card, Query-based] Hayek, R., & Shmueli, O. (2020). NN-based Transformation of Any SQL Cardinality Estimator for Handling DISTINCT, AND, OR and NOT. arXiv， 2020. [paper]
[Card, Query-based] Tzoumas K, Deshpande A, Jensen C S. Lightweight graphical models for selectivity estimation without independence assumptions[J]. Proceedings of the VLDB Endowment, 4(11): 852-863, 2011. [paper]
[Card, Query-based] Xiao Hu, Yuxi Liu, Haibo Xiu, Pankaj K. Agarwal, Debmalya Panigrahi, Sudeepa Roy, Jun Yang. Selectivity Functions of Range Queries are Learnable. SIGMOD, 2022. [paper]
[Card, Query-based, Adaptability] Beibin Li, Yao Lu, Srikanth Kandula: Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts. SIGMOD Conference 2022: 1920-1933 [paper]
[Card, Query-based, Robust Encoding & Training] Negi, Parimarjan, Ziniu Wu, Andreas Kipf, Nesime Tatbul, Ryan Marcus, Sam Madden, Tim Kraska, and Mohammad Alizadeh: Robust Query Driven Cardinality Estimation under Changing Workloads. VLDB, 2023. [paper]
[Card, Data-based] Leis, V., Radke, B., Gubichev, A., Kemper, A., & Neumann, T. (2017). Cardinality estimation done right: Index-based join sampling. CIDR, 2017. [paper]
[Card, Data-based] Yang, Z., Liang, E., Kamsetty, A., Wu, C., Duan, Y., Chen, X., … Stoica, I. (2019). Deep Unsupervised Cardinality Estimation. VLDB, 2019. [paper]
[Card, Data-based] Yang, Z., Kamsetty, A., Luan, S., Liang, E., Duan, Y., Chen, X., & Stoica, I. (2020). Neurocard: One cardinality estimator for all tables. Proceedings of the VLDB Endowment, 14(1), 61–73, 2020. [paper]
[Card, Data-based] Zhu R, Wu Z, Han Y, et al. FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation[J]. arXiv preprint arXiv:2011.09022, 2020. [paper]
[Card, Data-based] Wu Z, Shaikhha A, Zhu R, et al. BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation. arXiv preprint arXiv: 2012.14743, 2020. [paper]
[Card, Data-based] Hilprecht, B., Schmidt, A., Kulessa, M., Molina, A., Kersting, K., & Binnig, C. (2020). DeepDB: Learn from data, not from queries! VLDB, 13(7), 992–1005, 2020. [paper]
[Card, Data-based] Yongjoo Park, Shucheng Zhong, and Barzan Mozafari. Quicksel: Quick selectivity learning with mixture models. SIGMOD 2020. [paper]
[Card, Data-based] Lu Y, Kandula S, König A C, et al. Pre-training summarization models of structured datasets for cardinality estimation[J]. Proceedings of the VLDB Endowment, 2021. [paper]
[Card, Data-based] Zhu, R., Wu, Z., Han, Y., Zeng, K., Pfadler, A., Qian, Z., … Cui, B. (2020). FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation. VLDB, 2021. [paper]
[Card, Data-based] Jiayi Wang, Chengliang Chai, Jiabin Liu, Guoliang Li. FACE: A Normalizing Flow based Cardinality Estimator. VLDB 2022. [paper]
[Card, Data-based] Yao Lu, Srikanth Kandula, Arnd Christian König, Surajit Chaudhuri. Pre-training summarization models of structured datasets for cardinality estimation. VLDB 2022. [paper]
[Card, Query&Data-based] Wu P, Cong G. A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation[C]//Proceedings of the 2021 International Conference on Management of Data. 2021: 2009-2022. [paper]
[Card] Parimarjan Negi, Ryan C. Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh. Flow-Loss: Learning Cardinality Estimates That Matter. VLDB Endow, 14(11): 2019-2032, 2021. [paper]
[Card, Model Selection] Jintao Zhang, Chao Zhang, Guoliang Li, Chengliang Chai. AutoCE: An Accurate and Efficient Model Advisor for Learned Cardinality Estimation. ICDE, 2023. [paper]
[Card] Xiaoye Miao, Yangyang Wu, Jiazhen Peng, et al. Efficient and Effective Cardinality Estimation for Skyline Family. SIGMOD, 2023. [paper]
[Card] Ziniu Wu, Parimarjan Negi, Mohammad Alizadeh, Tim Kraska, Samuel Madden. FactorJoin: A New Cardinality Estimation Framework for Join Queries. SIGMOD, 2023. [paper]
[Cost] Marcus, R., & Papaemmanouil, O. (2019). Plan-Structured Deep Neural Network Models for Query Performance Prediction. 1733–1746. [paper]
[Cost] Sun, J., & Li, G. (n.d.). An End-to-End Learning-based Cost Estimator. VLDB, 2020. [paper]
[Cost] Benjamin Hilprecht, Carsten Binnig. Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction. VLDB, 2022. [paper]
[ EA&B ] Wang, X., Qu, C., Wu, W., Wang, J., & Zhou, Q. (2021). Are We Ready For Learned Cardinality Estimation? Proc. VLDB Endow. 14(9): 1640-1654 (2021). [paper]
[ EA&B ] Sun, J., Zhang, J., Sun, Z., Li, G., & Tang, N. (n.d.). Learned Cardinality Estimation : A Design Space Exploration and a Comparative Evaluation [ EA & B ]. 14(1). VLDB, 2022. [paper]
[ EA&B ] Yuxing Han, Ziniu Wu, Peizhi Wu, et al. Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation Yuxing. VLDB, 2022. [paper]
[ EA&B ] Kyoungmin Kim, Jisung Jung, In Seo, Wook-Shin Han, Kangwoo Choi, Jaehyok Chong: Learned Cardinality Estimation: An In-depth Study. SIGMOD Conference 2022: 1214-1227 [paper]
[ EA&B ] Harmouch, H., & Naumann, F. (2018). Cardinality Estimation: An Experimental Survey. Pvldb, 11(4), 4999–512, 2017. [paper]
Continuously Adaptive Query Processing
Ron Avnur, Joseph M. Hellerstein. Eddies. SIGMOD, 2000. [paper]
How Good Are Query Optimizers, Really?
Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., & Neumann, T. Proceedings of the VLDB Endowment (2016), 9(3), 204–215. [paper]
Neo: A Learned query optimizer
Marcus, R., Negi, P., Mao, H., Zhang, C., Alizadeh, M., Kraska, T., … Tatbul, N. (2018). Proceedings of the VLDB Endowment, 12(11), 1705–1718, 2018. [paper]
Deep reinforcement learning for join order enumeration
Marcus, R., & Papaemmanouil, O. (2018). Proceedings of the 1st International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, AiDM 2018, 0–3. [paper]
SkinnerDB : Regret-Bounded Query Evaluation via Reinforcement Learning
Trummer, I., Wang, J., Maram, D., Moseley, S., Jo, S., & Antonakakis, J. (n.d.). SIGMOD, 2019. [paper]
Progressive Join Algorithms Considering User Preference
Ding, M., Chen, S., & Manegold, S. (2021). CIDR, 2021. [paper]
Reinforcement Learning with Tree-LSTM for Join Order Selection
Yu, X., Li, G., Tang, N. (n.d.). ICDE, 2020. [paper]
Towards a Learning Optimizer for Shared Clouds
Chenggang Wu, Alekh Jindal, Saeed Amizadeh, Hiren Patel, Wangchao Le, Shi Qiao, Sriram Rao. Proc. VLDB Endow. 12(3): 210-222, 2018. [paper]
SQL Plan Observability through Hints in Oracle Autonomous Database
Pasupuleti, K., Park, M., & Valluri, S. (n.d.).
Bao: Making Learned Query Optimization Practical
Marcus, R., Negi, P., Mao, H., Tatbul, N., Alizadeh, M., & Kraska, T. (2020). SIGMOD, 2021. [paper]
Steering Query Optimizers: A Practical Take on Big Data Workloads
Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc Friedman, Alekh Jindal. SIGMOD, 2021. [paper]
SkinnerMT: Parallelizing for Efficiency and Robustness in Adaptive Query Processing on Multicore Platforms
Ziyun Wei, Immanuel Trummer. PVLDB, 2022. [paper]
Learning a Query Optimizer Without Expert Demonstrations
Zongheng Yang, Wei-Lin Chiang, Sifei Luan, Gautam Mittal, Michael Luo, Ion Stoica. Balsa. SIGMOD, 2022 [paper]
Workload-driven, Lazy Discovery of Data Dependencies for Query Optimization
Jan Kossmann. CIDR, 2022 [paper]
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans
Tianyi Chen, Jun Gao, Hedui Chen, and Yaofeng Tu. PVLDB, 2023. [paper]
BASE: Bridging the Gap between Cost and Latency for Query Optimization
Chen, Xu, Zhen Wang, Shuncheng Liu, et al. [paper]
COOOL: A Learning-To-Rank Approach for SQL Hint Recommendations
Xu, Xianghong, Zhibing Zhao, Tieying Zhang, et al. [paper]
Lero: A Learning-to-Rank Query Optimizer
Rong Zhu, Wei Chen, Bolin Ding, Xingguang Chen, Andreas Pfadler, Ziniu Wu, Jingren Zhou. VLDB 2023. [paper]
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection
Xiang Yu, Chengliang Chai, Guoliang Li, Jiabin Liu. VLDB 2023. [paper]
Leveraging Query Logs and Machine Learning for Parametric Query Optimization
Kapil Vaidya, Anshuman Dutt, Vivek Narasayya, Surajit Chaudhuri. VLDB 2022. [paper]
Kepler: Robust Learning for Parametric Query Optimization
Lyric Doshi, Vincent Zhuang, Gaurav Jain, Ryan C Marcus, Haoyu Huang, Deniz Altınbüken, Eugene Brevdo, Campbell Fraser. SIGMOD 2023.[paper] (to appear)
Ibrahim Sabek, Tenzin Samten Ukyab, Tim Kraska. LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems. SIGMOD, 2022. [paper]
Chi Zhang, Ryan Marcus, and et al. Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning. In VLDB, 2020. [paper]
[1-D, Immutable] Kraska, T., Beutel, A., Chi, E. H., Dean, J., & Polyzotis, N. (2018). The case for learned index structures. SIGMOD, 2018. [paper] [code]
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