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AI4DB & DB4AI-Papers
Table of Contents
- 0. Survey & Tutorial
- 1. Database Configuration
- 2. Query Optimization
- 3. Database Design
- 4. Database Monitoring
- 5. Database Diagnosis
- 6. Autonomous Database
- 7. Demonstrations
- 8. Talks
0. Survey & Tutorial
[Survey] Xuanhe Zhou, Chengliang Chai, Guoliang Li, Ji Sun. Database Meets AI: A Survey. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020. [paper]
[Survey] Wang, W., Zhang, M., Chen, G., Jagadish, H. V., Ooi, B. C., & Tan, K. L. (2016). Database meets deep learning: Challenges and opportunities. SIGMOD, 2016. [paper]
[Survey] Cai, Q., Cui, C., Xiong, Y., Xie, Z., & Zhang, M. (2021). A Survey on Deep Reinforcement Learning for Data Processing and Analytics, 2021. [paper]
[Tutorial] Idreos, S., & Kraska, T. (2019). From auto-tuning one size fits all to self-designed and learned data-intensive systems. Proceedings of the ACM SIGMOD International Conference on Management of Data, 2054–2059. [paper]
[Tutorial] Guoliang Li, Xuanhe Zhou, Lei Cao. AI Meets Database: AI4DB and DB4AI. SIGMOD 2021. [paper] [slides]
[Tutorial] Guoliang Li, Xuanhe Zhou, Lei Cao. Machine Learning for Databases. VLDB 2021. [paper] [slides]
[Tutorial] Lu, J., Chen, Y., Herodotou, H., Babu, S. (2019). Speedup Your Analytics : Automatic Parameter Tuning for Databases and Big Data Systems, VLDB, 2019. [paper] [slides]
[Tutorial] Jindal, A., & Interlandi, M. (n.d.). Machine Learning for Cloud Data Systems : the Promise , the Progress , and the Path Forward. VLDB, 2021. [paper]
[Tutorial] Zhengtong Yan, Jiaheng Lu, Naresh Chainani, Chunbin Lin. Workload-Aware Performance Tuning for Autonomous DBMSs. ICDE, 2021. [paper]
[Tutorial] Brad Glasbergen, Michael Abebe, Khuzaima Daudjee. Tutorial: Adaptive Replication and Partitioning in Data Systems. Middleware, 2018. [paper]
1. Database Configuration
Knob Tuner
[DB] Zhang, J., Liu, Y., Zhou, K., Li, G., Xiao, Z., Cheng, B., … Li, Z. (2019). An end-to-end automatic cloud database tuning system using deep reinforcement learning. SIGMOD, 2019. [paper]
[DB] Aken, D. Van, Pavlo, A., Gordon, G. J., & Zhang, B. (2017). Automatic database management system tuning through large-scale machine learning. SIGMOD, 2017. [paper]
[DB] Li, G., Zhou, X., Gao, B., & Li, S. (2019). QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning. VLDB, 2019. [paper]
[DB] Zhang, X., Tan, J., & Cui, B. (n.d.). ResTune : Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases. SIGMOD, 2021. [paper]
[DB] Tan, J., Zhang, T., Li, F., Chen, J., Zheng, Q., & Zhang, P. (2019). iBTune : Individualized Buffer Tuning for Large-scale Cloud Databases. VLDB, 2021. [paper]
[DB] Van Aken, D., Yang, D., Brillard, S., Fiorino, A., Zhang, B., Bilien, C., & Pavlo, A. (2021). An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems. VLDB, 2021. [paper]
[DB] Thummala, V., & Babu, S. (2010). iTuned: A tool for configuring and visualizing database parameters. SIGMOD, 2010. [paper]
[DB] Zhu, Y., Liu, J., Guo, M., Bao, Y., & Ma, W. (2017). BestConfig : Tapping the Performance Potential of Systems via Automatic Configuration Tuning, SoCC, 2017. [paper]
[KV Store] Jiake Ge, Ynagfeng Chai, Yunpeng Chai. A Workload-aware Tuning System of Key-Value Stores with Attention-Based Deep Reinforcement Learning 1. (n.d.). JCST, 2021.
[Spark] Gallinucci, E., & Golfarelli, M. (2019). SparkTune : tuning Spark SQL through query cost modeling. EDBT, 546–549. [paper]
[Spark] Kunjir, M., & Babu, S. (2020). Black or White? How to Develop an AutoTuner for Memory-based Analytics [Extended Version]. SIGMOD, 2020. [paper]
[SSD] Kakaraparthy, A., & Kroth, B. P. (n.d.). Optimizing Databases by Learning Hidden Parameters of Solid State Drives. VLDB, 2019. [paper]
[IT Stack] Cereda, S., Doni, S., & Milano, P. (n.d.). CGPTuner : a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions. VLDB, 2021. [paper]
[App] Xu, Q., Hu, Y. C., & Jindal, A. (2020). App Parameter Energy Profiling: Optimizing App Energy Drain by Finding Tunable App Parameters. arXiv, 2020. [paper]
View Advisor
A. Jindal, K. Karanasos, S. Rao, and H. Patel. Selecting subexpressions to materialize at datacenter scale. PVLDB, 11(7):800–812, 2018.[paper]
Ahmed, R., Bello, R., Witkowski, A., & Kumar, P. (2020). Automated generation of materialized views in Oracle. VLDB, 2020. [paper]
Yuan, H., Sun, J., & Li, G. (2020). Automatic View Generation for Equivalent Subqueries with Deep Learning and Reinforcement Learning. ICDE, 2020. [paper]
Han, Y., Li, G., Yuan, H., & Sun, J. (n.d.). An Autonomous Materialized View Management System with Deep Reinforcement Learning. ICDE, 2021. [paper]
Index Advisor
Agrawal, S., Bruno, N., Chaudhuri, S., & Narasayya, V. (2006). AutoAdmin : Self-Tuning Database Systems Technology 2 Physical Database Design Tuning. Data Engineering, 2006. [paper]
Sadri, Z., & Gruenwald, L. (2020). Online Index Selection Using Deep Reinforcement Learning for a Cluster Database. ICDE Workshop, 2020. [paper]
Kossmann, J., Halfpap, S., Jankrift, M., & Schlosser, R. (2020). Magic mirror in my hand, which is the best in the land? An experimental evaluation of index selection algorithms. VLDB, 2020. [paper]
Ding, B., Das, S., Marcus, R., Wu, W., Chaudhuri, S., & Narasayya, V. R. (2019). AI meets AI: Leveraging query executions to improve index recommendations. SIGMOD, 2019. [paper]
Borovica-gajic, R., Perera, M., Oetomo, B., & Rubinstein, B. I. P. (2019). DBA bandits : Self-driving physical design tuning under ad-hoc workloads with safety guarantees. arXiv, 2019. [paper]
Paludo, G., Julia, L., Couto, C., Fátima, P. De, & Renata, M. (n.d.). S MART IX : A database indexing agent based on reinforcement learning. 2020. [paper]
Hai Lan, Zhifeng Bao, Yuwei Peng. An Index Advisor Using Deep Reinforcement Learning. CIKM, 2020. [paper]
Vishal Sharma, Curtis E. Dyreson, Nicholas Flann. MANTIS: Multiple Type and Attribute Index Selection using Deep Reinforcement Learning. IDEAS, 2021. [paper]
Query Rewriter
[performance] Pirahesh, H., & Hellerstein, J. M. (1992). Extensible / Rule Based Query Rewrite Optimization in Starburst. SIGMOD, 1992. [paper]
[performance] 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.
[performance] 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]
[performance] Wu, W., Bernstein, P. A., Raizman, A., & Pavlopoulou, C. (2020). Cost-based Query Rewriting Techniques for Optimizing Aggregates Over Correlated Windows. arXiv, 2020. [paper]
[performance] Wu, J. (2021). Sia : Optimizing Queries using Learned Predicates. SIGMOD, 2021. [paper]
[performance] Xuanhe Zhou, Guoliang Li, Chengliang Chai, Jianhua Feng. A Learned Query Rewrite System using Monte Carlo Tree Search. VLDB, 2022. [paper]
[redundancy] Sarthi, P., Rajan, K., Lal, A., Jain, P., Liu, M., Gosalia, A., & Kalikar, S. (2020). Generalized Sub-Query Fusion for Eliminating Redundant I / O from Big-Data Queries. OSDI, 2020. [paper]
[equivalence] Chu, S., Weitz, K., Cheung, A., & Suciu, D. (2017). HoTTSQL: Proving query rewrites with univalent SQL semantics. ACM SIGPLAN Notices, 52(6), 510–524. [paper]
Partition Advisor
[horizontal] Boissier, M., & Daniel, K. (2018). Workload-driven horizontal partitioning and pruning for large HTAP systems. Proceedings - IEEE 34th International Conference on Data Engineering Workshops, ICDEW 2018, (April 2018), 116–121. [paper]
[horizontal] Agrawal, S., Chu, E., & Narasayya, V. (2006). Automatic physical design tuning: Workload as a sequence. Proceedings of the ACM SIGMOD International Conference on Management of Data, 683–694. [paper]
[horizontal] Curino, C., Jones, E., Zhang, Y., & Madden, S. (2010). Schism: A workload-driven approach to database replication and partitioning. Proceedings of the VLDB Endowment, 3(1), 48–57. [paper]
[horizontal] Bandle, M., Giceva, J., & Neumann, T. (2021). To Partition, or Not to Partition, That is the Join Question in a Real System. SIGMOD, 2021. [paper]
[horizontal] Parchas, P., Naamad, Y., Van Bouwel, P., Faloutsos, C., & Petropoulos, M. (2020). Fast and effective distribution-key recommendation for amazon redshift. Proceedings of the VLDB Endowment, 13(11), 2411–2423. [paper]
[horizontal] Hilprecht, B., Binnig, C., & Röhm, U. (2019). Towards learning a partitioning advisor with deep reinforcement learning. Proceedings of the ACM SIGMOD International Conference on Management of Data. [paper]
[co-partition] Zamanian, E., Binnig, C., & Salama, A. (2015). Locality-aware partitioning in parallel database systems. Proceedings of the ACM SIGMOD International Conference on Management of Data, 2015-May, 17–30. [paper]
[co-partition] Rabl, T., & Jacobsen, H. A. (2017). Query centric partitioning and allocation for partially replicated database systems. Proceedings of the ACM SIGMOD International Conference on Management of Data, Part F1277, 315–330. [paper]
[situ] Olma, M., Karpathiotakis, M., Alagiannis, I., Athanassoulis, M., & Ailamaki, A. (2020). Adaptive partitioning and indexing for in situ query processing. VLDB Journal, 29(1), 569–591. [paper]
Sun, L., Franklin, M. J., Krishnan, S., & Xin, R. S. (2014). Fine-grained partitioning for aggressive data skipping. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1115–1126. [paper]
2. Query Optimization
Cost Estimation
[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] 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] Hayek, R., & Shmueli, O. (2020). NN-based Transformation of Any SQL Cardinality Estimator for Handling DISTINCT, AND, OR and NOT. arXiv， 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] 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] 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] 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] Hilprecht, B., Schmidt, A., Kulessa, M., Molina, A., Kersting, K., & Binnig, C. (2020). DeepDB: Learn from data, not from queries! Proceedings of the VLDB Endowment, 13(7), 992–1005, 2020. [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] 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, 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] Hasan S, Thirumuruganathan S, Augustine J, et al. Deep learning models for selectivity estimation of multi-attribute queries. SIGMOD, 2020. [paper]
[Card, Data-based] Heimel M, Kiefer M, Markl V. Self-tuning, GPU-accelerated kernel density models for multidimensional selectivity estimation. Proceedings of the ACM SIGMOD, 2015. [paper]
[Card, Data-based] Jiayi Wang, Chengliang Chai, Jiabin Liu, Guoliang Li. FACE: A Normalizing Flow based Cardinality Estimator. 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]
[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]
[ 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 ] Harmouch, H., & Naumann, F. (2018). Cardinality Estimation: An Experimental Survey. Pvldb, 11(4), 4999–512, 2017. [paper]
Join Enumerator
Marcus, R., Negi, P., Mao, H., Zhang, C., Alizadeh, M., Kraska, T., … Tatbul, N. (2018). Neo: A Learned query optimizer. Proceedings of the VLDB Endowment, 12(11), 1705–1718, 2018. [paper]
Marcus, R., & Papaemmanouil, O. (2018). Deep reinforcement learning for join order enumeration. Proceedings of the 1st International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, AiDM 2018, 0–3. [paper]
Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., & Neumann, T. (2016). How Good Are Query Optimizers, Really? Proceedings of the VLDB Endowment, 9(3), 204–215. [paper]
Trummer, I., Wang, J., Maram, D., Moseley, S., Jo, S., & Antonakakis, J. (n.d.). SkinnerDB : Regret-Bounded Query Evaluation via Reinforcement Learning. SIGMOD, 2019. [paper]
Ding, M., Chen, S., & Manegold, S. (2021). *Progressive Join Algorithms Considering User Preference. CIDR, 2021. [paper]
Yu, X., Li, G., Tang, N. (n.d.). Reinforcement Learning with Tree-LSTM for Join Order Selection. ICDE, 2020. [paper]
Chenggang Wu, Alekh Jindal, Saeed Amizadeh, Hiren Patel, Wangchao Le, Shi Qiao, Sriram Rao. Towards a Learning Optimizer for Shared Clouds. Proc. VLDB Endow. 12(3): 210-222, 2018. [paper]
Plan Hinter
Pasupuleti, K., Park, M., & Valluri, S. (n.d.). SQL Plan Observability through Hints in Oracle Autonomous Database.
Marcus, R., Negi, P., Mao, H., Tatbul, N., Alizadeh, M., & Kraska, T. (2020). Bao: Making Learned Query Optimization Practical. SIGMOD, 2021. [paper]
Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc Friedman, Alekh Jindal. Steering Query Optimizers: A Practical Take on Big Data Workloads. SIGMOD, 2021. [paper]
3. Database Design
Physical Design
[Learned Index, mutable] Galakatos, A., Markovitch, M., Binnig, C., Fonseca, R., & Kraska, T. (2019). Fiting-tree: A data-aware index structure. Proceedings of the 2019 International Conference on Management of Data, 1189-1206. [paper]
[Learned Index, mutable] Ferragina, P., & Vinciguerra, G. (2020). The PGM-index : a fully-dynamic compressed learned index with provable worst-case bounds. The Proceedings of the VLDB Endowment (PVLDB), 13(8), 1162–1175. [paper]
[Learned Index, mutable] Hadian, A., & Heinis, T. (n.d.). MADEX : Learning-augmented Algorithmic Index Structures. ( Regular Papers ). [paper] [slides]
[Learned Index, immutable] Kraska, T., Beutel, A., Chi, E. H., Dean, J., & Polyzotis, N. (2018). The case for learned index structures. Proceedings of the ACM SIGMOD International Conference on Management of Data, 489–504. [paper]
[Learned Index, immutable, multi-d] Nathan, V., Ding, J., Alizadeh, M., & Kraska, T. (2020). Learning multi-dimensional indexes. Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, 985-1000. [paper]
[Learned Index, immutable, multi-d] Ding, J., Nathan, V., Alizadeh, M., & Kraska, T. (2020). Tsunami: A learned multi-dimensional index for correlated data and skewed workloads. Proceedings of the VLDB Endowment, 14(2), 74-86. [paper]
[Learned Index, immutable, multi-d] Wu, J., Zhang, Y., Chen, S., Wang, J., Chen, Y., & Xing, C. (2021). Updatable Learned Index with Precise Positions. Proceedings of the VLDB Endowment, 14(8), 1276-1288. [paper]
Anders Hammershøj Jensen, Frederik Lauridsen, Fatemeh Zardbani, Stratos Idreos, Panagiotis Karras. Revisiting Multidimensional Adaptive Indexing [Experiment & Analysis]. EDBT 2021: 469-474. [paper]
[Learned Layout] Yang, Z., Chandramouli, B., Wang, C., Gehrke, J., Li, Y., Minhas, U. F., … Acharya, R. (n.d.). Qd-tree : Learning Data Layouts for Big Data Analytics. SIGMOD, 2020. [paper]
Query Execution
Zhang, C., Marcus, R., Kleiman, A., & Papaemmanouil, O. (2020). Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning. AIDB@VLDB, 2020. [paper]
4. Database Monitoring
[Trend Prediction] L. Ma, D. V. Aken, A. Hefny, G. Mezerhane, A. Pavlo, and G. J. Gordon, “Query-based Workload Forecasting for Self-driving Database Management Systems,” in SIGMOD, 2018. [paper]
[Performance Prediction] Dorn, J., Apel, S., & Siegmund, N. (n.d.). Mastering Uncertainty in Performance Estimations of Configurable Software Systems. (3).
[Performance Prediction] Marcus, R., & Papaemmanouil, O. (2019). Plan-structured deep neural network models for query performance prediction. Proceedings of the VLDB Endowment, 12(11), 1733–1746. [paper]
[Performance Prediction] Wu, W., Chi, Y., Hacig̈um̈uş, H., & Naughton, J. F. (2013). Towards predicting query execution time for concurrent and dynamic database workloads. Proceedings of the VLDB Endowment, 6(10), 925–936. [paper]
[Performance Prediction] Duggan, J., Papaemmanouil, O., Cetintemel, U., & Upfal, E. (2014). Contender: A resource modeling approach for concurrent query performance prediction. Advances in Database Technology - EDBT 2014: 17th International Conference on Extending Database Technology, Proceedings, 109–120. [paper]
[Performance Prediction] Wu, W., Chi, Y., Zhu, S., Tatemura, J., Hacigümüş, H., & Naughton, J. F. (2013). Predicting query execution time: Are optimizer cost models really unusable? Proceedings - International Conference on Data Engineering, (1), 1081–1092. [paper]
[Performance Prediction] Higginson, A. S., Dediu, M., Arsene, O., Paton, N. W., & Embury, S. M. (2020). Database Workload Capacity Planning using Time Series Analysis and Machine Learning. Proceedings of the ACM SIGMOD International Conference on Management of Data, 769–783. [paper]
[Performance Prediction] Unterbrunner, P., Giannikis, G., Alonso, G., Fauser, D., & Kossmann, D. (2009). Predictable performance for unpredictable workloads. Proceedings of the VLDB Endowment, 2(1), 706–717. [paper]
[Performance Prediction] Xuanhe Zhou, Ji Sun, Guoliang Li, Jianhua Feng. Query Performance Prediction for Concurrent Queries using Graph Embedding. [paper]
5. Database Diagnosis
System Diagnosis
Yoon, D. Y., Niu, N., & Mozafari, B. (2016). DBSherlock: A performance diagnostic tool for transactional databases. Proceedings of the ACM SIGMOD International Conference on Management of Data, 26-June-20(i), 1599–1614. [paper]
Kalmegh, P., Babu, S., & Roy, S. (2019). iQCAR: inter-Query Contention Analyzer for Data Analytics Frameworks. SIGMOD. [paper]
Ma, M., Yin, Z., Zhang, S., Wang, S., Zheng, C., & Jiang, X. (2020). Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases. PVLDB Endowment. [paper]
Query Diagnosis
6. Autonomous Database
[AutoDB] Pavlo, A., Angulo, G., Arulraj, J., Lin, H., Lin, J., Ma, L., … Zhang, T. (2017). Self-Driving Database Management Systems. CIDR, 2017. [paper]
[NLP] James Thorne, Majid Yazdani, Marzieh Saeidi, Fabrizio Silvestri, Sebastian Riedel, Alon Y. Levy. From Natural Language Processing to Neural Databases. VLDB, 2021. [paper]
[Embedding] Raasveldt, M. (2018). MonetDBLite: An embedded analytical database. Proceedings of the ACM SIGMOD International Conference on Management of Data, 1837–1838. [paper]
[AutoDB] Li, F. (2018). Cloud native database systems at Alibaba: Opportunities and challenges. Proceedings of the VLDB Endowment, 2018. [paper]
[AutoDB] Kraska, T., Alizadeh, M., Beutel, A., Chi, E. H., Ding, J., Kristo, A., … Nathan, V. (2019). SageDB: A learned database system. CIDR, 2019. [paper]
[AutoDB] Li, G., Zhou, X., Li, S. (2019). XuanYuan: An AI-Native Database. Data Eng., 2019. [paper]
[AutoDB] Hilprecht, B., Bang, T., El-Hindi, M., Hättasch, B., Khanna, A., Rehrmann, R., … Binnig, C. (2020). DBMS Fitting: Why should we learn what we already know? Cidr, 2020. [paper]
[AutoDB] Guoliang Li, Xuanhe Zhou, , Ji Sun, Xiang Yu, Yue Han, Lianyuan Jin, Wenbo Li, Tianqing Wang, Shifu Li. openGauss: An Autonomous Database System. VLDB, 2021. [paper]
[AutoDB] Ma, L., Zhang, W., Jiao, J., Wang, W., Butrovich, M., Lim, W. S., … Pavlo, A. (2021). MB2 : Decomposed Behavior Modeling for Self-Driving Database Management Systems. SIGMOD, 2021. [paper]
7. Demonstrations
[DB Tuning] Zhang, B., Van Aken, D., Wang, J., Dai, T., Jiang, S., Lao, J., . A Demonstration of the ottertune automatic database management system tuning service. VLDB, 1910–1913. [paper]
[DB Tuning] Junxiong Wang, Immanuel Trummer, Debabrota Basu. Demonstrating UDO: A Unified Approach for Optimizing Transaction Code, Physical Design, and System Parameters via Reinforcement Learning. SIGMOD, 2021. [paper]
[AutoDB] Xuanhe Zhou, Lianyuan Jin, Ji Sun, Xinyang Zhao, Xiang Yu, Shifu Li, Tianqing Wang, Kun Li, luyang liu. DBMind: A Self-Driving Platform in openGauss. [paper] [website]
8. Talks
[AutoDB] Pavlo, A., Butrovich, M., Ma, L., Menon, P., Lim, W. S., Aken, D. Van, & Zhang, W. (n.d.). Make Your Database System Dream of Electric Sheep : Towards Self-Driving Operation. VLDB, 2021. [paper]
[AutoDB] Kraska, T.. Towards instance-optimized data systems. VLDB, 2021. [paper]
[AutoDB] Guoliang Li. AI-Native Database. VLDB, 2021.