Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/LumingSun/ML4DB-paper-list

Papers for database systems powered by artificial intelligence (machine learning for database)
https://github.com/LumingSun/ML4DB-paper-list

Last synced: 15 days ago
JSON representation

Papers for database systems powered by artificial intelligence (machine learning for database)

Awesome Lists containing this project

README 

        

# [Paper List] AI4DB / ML4DB / Autonomous Database / Self-driving Database / 智能数据库 / 自治数据库



Paper list for database systems with artificial intelligence (machine learning, deep learning, reinforcement learning)



New papers keep coming, remember to **Watch** this repo if you are interested in this topic.



有关机器学习、神经网络、强化学习、自调优技术等在数据库系统中的应用的文章列表，列表持续更新中，记得按赞、分享、打开小铃铛！



Welcome to PR!



欢迎大家补充！



There are so many papers emerging about Text-To-SQL! Sadly I'm not an expert with the topic and can not tell the quality of the papers.  

Looking forward to contributions (PR, comment, discussion) about Text-To-SQL！🫶



Table of Contents

=================

* [System & Tutorial](#system-and-tutorial)

* [Data Access](#data-access)

  * [Configuration Tuning](#configuration-tuning)

  * [Physical Design](#physical-design)

    * [Learned structure](#learned-structure)

    * [LSM-tree related](#lsm-tree-related)

    * [Index](#index)

      * [Index Structure](#index-structure)

      * [Index Recommendation](#index-recommendation)

    * [Materialized View](#materialized-view)

    * [Schema & Partition](#schema--partition)

      * [Offline](#offline)

      * [Online](#online)

  * [Cache related](#cache-related)

* [Workload](#workload)

    * [Resource Estimation and Auto-scaling](#resource-estimation-and-auto-scaling)

    * [Performance Diagnosis and Modeling](#performance-diagnosis-and-modeling)

    * [Workload Shift Detection](#workload-shift-detection)

    * [Metrics Prediction for Queries](#metrics-prediction-for-queries)

    * [Workload Characterization & Forecasting](#workload-characterization-&-forecasting)

* [Query Optimization](#query-optimization)

   * [Query Rewrite](#query-write)

   * [Cardinality Estimation](#cardinality-estimation)

     * [Data-based](#data-based)

     * [Query-based](#query-based)

  * [Cost Estimation](#cost-estimation)

    * [Single Query](#single-query)

    * [Concurrent](#Concurrent)

  * [Join Optimization](#join-optimization)

  * [Query Plan](#query-plan)

* [Query Execution](#query-execution)

  * [Sort](#sort)

  * [Join](#join)

  * [Adaptive Query Processing](#adaptive-query-processing)

  * [Approximate Query Processing](#Approximate-query-processing)

  * [Sheduling](#sheduling)

* [Text-to-SQL](#text-to-sql)

* [SQL Related](#sql-related)



## System and Tutorial

* ***SageDB: A Learned Database System (CIDR 2019)***

* Database Learning: Toward a Database that Becomes Smarter Every Time (SIGMOD 2017)

* Self-Driving Database Management Systems (CIDR 2017)

* Self-Driving : From General Purpose to Specialized DBMSs (Phd@PVLDB 2018)  

* Active Learning for ML Enhanced Database Systems (SIGMOD 2020)

* Database Meets Artificial Intelligence: A Survey (TKDE 2020)

* Self-driving database systems: a conceptual approach (Distributed and Parallel Databases 2020)

* One Model to Rule them All: Towards Zero-Shot Learning for Databases (arXiv 2021)

* UDO: Universal Database Optimization using Reinforcement Learning (arXiv 2021) [Source Code](https://github.com/jxiw/UDO)

* Towards a Benchmark for Learned Systems (SMDB workshop 2021)

* A Unified Transferable Model for ML-Enhanced DBMS [Vision] (arXiv 2021)

* AI Meets Database: AI4DB and DB4AI (SIGMOD 2021)

* Expand your Training Limits! Generating Training Data for ML-based Data Management (SIGMOD 2021)

* MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems (SIGMOD 2021)

* Towards instance-optimized data systems (VLDB 2021 from Tim Kraska)

* Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation (VLDB 2021 from Andy Pavlo)

* openGauss: An Autonomous Database System (VLDB 2021 from Guoliang Li)

* Experience-Enhanced Learning: One Size Still does not Fit All in Automatic Database Management (arXiv 2021)

* Baihe: SysML Framework for AI-driven Databases (arXiv 2022)

* Survey on Learnable Databases: A Machine Learning Perspective (Big Data Research 2021)

* Database Optimizers in the Era of Learning (ICDE 2022)

* Machine Learning for Data Management: A System View (ICDE 2022)

* Tastes Great! Less Filling! High Performance and Accurate Training Data Collection for Self-Driving Database Management Systems (SIGMOD 2022)

* SAM: Database Generation from Query Workload with Supervised Autoregressive Model (SIGMOD 2022) [Source code](https://github.com/Jamesyang2333/SAM)

* Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data (SIGMOD 2023) [Source code](https://github.com/meghdadk/DDUp)

* SageDB: An Instance-Optimized Data Analytics System (VLDB 2023)

* Towards Building Autonomous Data Services on Azure (SIGMOD-Companion ’23)

* Database Gyms (CIDR 2023)

* Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes (VLDB 2023)

* Machine Unlearning in Learned Databases: An Experimental Analysis (SIGMOD 2024) [Source code](https://github.com/meghdadk/DB_unlearning)

* PilotScope: Steering Databases with Machine Learning Drivers (VLDB 2024) [Source code](https://github.com/alibaba/pilotscope)

* Machine Learning for Databases: Foundations, Paradigms, and Open problems (SIGMOD 2024)

* NeurDB: An AI-powered Autonomous Data System (arXiv 2024)

* GaussML: An End-to-End In-Database Machine Learning System (ICDE 2024)

  

## Data Access

### Configuration Tuning

* SARD: A statistical approach for ranking database tuning parameters (ICDEW, 2008)

* Regularized Cost-Model Oblivious Database Tuning with Reinforcement Learning （2016）

* Automatic Database Management System Tuning Through Large-scale Machine Learning (SIGMOD 2017)

* The Case for Automatic Database Administration using Deep Reinforcement Learning ( 2018 ArXiv)

* An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning (SIGMOD 2019)

* External vs. Internal : An Essay on Machine Learning Agents for Autonomous Database Management Systems

* QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning (VLDB 2019)

* Optimizing Databases by Learning Hidden Parameters of Solid State Drives (VLDB 2019)

* iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases (VLDB 2019)

* Black or White? How to Develop an AutoTuner for Memory-based Analytics (SIGMOD 2020)

* Learning Efficient Parameter Server Synchronization Policies for Distributed SGD (ICLR 2020)

* Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs (HotStorage 2020)

* Dynamic Configuration Tuning of Working Database Management Systems (LifeTech 2020)

* Adaptive Multi-Model Reinforcement Learning for Online Database Tuning (EDBT 2021)

* An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems (VLDB 2021)

* The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that "Read the Manual" (VLDB 2021)

* CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions (VLDB 2021)

* ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases (SIGMOD 2021)

* KML: Using Machine Learning to Improve Storage Systems (arXiv 2021)

* Database Tuning using Natural Language Processing (SIGMOD Record 2021)

* Towards Dynamic and Safe Configuration Tuning for Cloud Databases (SIGMOD 2022)

* Automatic Performance Tuning for Distributed Data Stream Processing Systems (ICDE 2022)

* Adaptive Code Learning for Spark Configuration Tuning (ICDE 2022)

* DB-BERT: A Database Tuning Tool that "Reads the Manual" (SIGMOD 2022)

* HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements (SIGMOD 2022)

* LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications (SIGMOD 2022)

* Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation (VLDB 2022)

* LlamaTune: Sample-Efficient DBMS Configuration Tuning (VLDB 2022)

* BLUTune: Query-informed Multi-stage IBM Db2 Tuning via ML (CIKM 2022)

* A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning (arXiv 2023)

* Automatic Database Knob Tuning: A Survey (TKDE)

* Deep learning based Auto Tuning for Database Management System (arXiv 2023)

* KeenTune: Automated Tuning Tool for Cloud Application Performance Testing and Optimization (ISSTA 2023)

* ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems (arXiv 2023)

* GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization (arXiv 2023)

* An Eficient Transfer Learning Based Configuration Adviser for Database Tuning (VLDB 2024)

* DB‑GPT: Large Language Model Meets Database (DSE 2024)

* A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning (arXiv 2024)

* TIE: Fast Experiment-driven ML-based Configuration Tuning for In-memory Data Analytics (IEEE Transactions on Computers)

* VDTuner: Automated Performance Tuning for Vector Data Management Systems (ICDE 2024) [Source code](https://github.com/tiannuo-yang/VDTuner)

* Nautilus: A Benchmarking Platform for DBMS Knob Tuning (DEEM 2024) [Source code](https://github.com/uw-mad-dash/nautilus)



### Physical Design

* Tiresias: Enabling Predictive Autonomous Storage and Indexing (VLDB 2022)

#### Learned structure

* Stacked Filters: Learning to Filter by Structure (VLDB 2021)

* LEA: A Learned Encoding Advisor for Column Stores (aiDM 2021)

* Learning over Sets for Databases (EDBT 2024)

#### LSM-tree related

* Leaper: A Learned Prefetcher for Cache Invalidation in LSM-tree based Storage Engines （VLDB 2020）

* From WiscKey to Bourbon: A Learned Index for Log-Structured Merge Trees (OSDI 2020)

* TridentKV: A Read-Optimized LSM-Tree Based KV Store via Adaptive Indexing and Space-Efficient Partitioning (TPDS 2022)

#### Index

##### Index Structure

* Learning to hash for indexing big data - A survey (2016)

* The Case for Learned Index Structures (SIGMOD 2018)

* A-Tree: A Bounded Approximate Index Structure (2017)

* FITing-Tree: A Data-aware Index Structure (SIGMOD 2019)

* Learned Indexes for Dynamic Workloads (2019)

* SOSD: A Benchmark for Learned Indexes (2019)

* Learning Multi-dimensional Indexes (2019)

* ALEX: An Updatable Adaptive Learned Index (SIGMOD 2020)

* Effectively Learning Spatial Indices (VLDB 2020) [GitHub Link](https://github.com/Liuguanli/RSMI)

* Stable Learned Bloom Filters for Data Streams (VLDB 2020)

* START — Self-Tuning Adaptive Radix Tree (ICDEW 2020)

* Learned Data Structures (2020)

* RadixSpline: a single-pass learned index (aiDM2020)

* The ML-Index: A Multidimensional, Learned Index for Point, Range, and Nearest-Neighbor Queries (EDBT 2020)

* The PGM-index: a fully-dynamic compressed learned index with provable worst-case bounds (VLDB 2020)

* A Tutorial on Learned Multi-dimensional Indexes (SIGSPATIAL 2020)

* Why Are Learned Indexes So Effective? (ICML 2020)

* Learned Indexes for a Google-scale Disk-based Database (arXiv 2020)

* SIndex: A Scalable Learned Index for String Keys （APSys 2020)

* XIndex: A Scalable Learned Index for Multicore Data Storage （PPoPP 2020)

* Tsunami: A Learned Multi-dimensional Index for Correlated Data and Skewed Workloads (VLDB 2021)

* A Lazy Approach for Efficient Index Learning (2021)

* The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data (arXiv 2021)

* Spatial Interpolation-based Learned Index for Range and kNN Queries (arXiv 2021)

* APEX: A High-Performance Learned Index on Persistent Memory (arXiv 2021)

* RUSLI: Real-time Updatable Spline Learned Index (aiDM 2021)

* PLEX: Towards Practical Learned Indexing (arXiv 2021)

* SPRIG: A Learned Spatial Index for Range and kNN Queries (SSTD 2021)

* Benchmarking Learned Indexes (VLDB 2021)

* Updatable Learned Index with Precise Positions (VLDB 2021)

* The Case for Learned In-Memory Joins (arXiv 2021)

* Bounding the Last Mile: Efficient Learned String Indexing (arXiv 2021)

* FINEdex: A Fine-grained Learned Index Scheme for Scalable and Concurrent Memory Systems (VLDB 2022)

* The next 50 Years in Database Indexing or: The Case for Automatically Generated Index Structures (VLDB 2022)

* The Concurrent Learned Indexes for Multicore Data Storage (Transactions on Storage 2022)

* TONE: cutting tail-latency in learned indexes (CHEOPS 22)

* A Learned Index for Exact Similarity Search in Metric Spaces (ArXiv 2022)

* RW-tree: A Learned Workload-aware Framework for R-tree Construction (ICDE 2022)

* The "AI+R"-tree: An Instance-optimized R-tree (MDM 2022)

* LHI: A Learned Hamming Space Index Framework for Efficient Similarity Search (SIGMOD 2022)

* Entropy Learned Hashing: 10X Faster Hashing with Controllable Uniformity (SIGMOD 2022)

* Tuning Hierarchical Learned Indexes on Disk and Beyond (SIGMOD 2022)

* FLIRT: A Fast Learned Index for Rolling Time frames (EDBT 2022)

* Testing the Robustness of Learned Index Structures (arXiv 2022)

* The Case for ML-Enhanced High-Dimensional Indexes (2022)

* A Learned Index for Exact Similarity Search in Metric Spaces (arxiv 2022)

* PLIN: A Persistent Learned Index for Non-Volatile Memory with High Performance and Instant Recovery (VLDB 2023)

* A Data-aware Learned Index Scheme for Efficient Writes (ICPP 2022)

* Frequency Estimation in Data Streams: Learning the Optimal Hashing Scheme (TKDE)

* FILM: A Fully Learned Index for Larger-Than-Memory Databases (VLDB 2023)

* WISK: A Workload-aware Learned Index for Spatial Keyword Queries (arXiv 2023)

* Efficiently Learning Spatial Indices (ICDE 2023)

* Cutting Learned Index into Pieces: An In-depth Inquiry into Updatable Learned Indexes (ICDE 2023)

* DILI: A Distribution-Driven Learned Index (arXiv 2023)

* Learned Index: A Comprehensive Experimental Evaluation (VLDB 2023)

* LMSFC: A Novel Multidimensional Index based on Learned Monotonic Space Filling Curves (Extended Version) (arXiv 2023)

* One stone, two birds: A lightweight multidimensional learned index with cardinality support (arXiv 2023)

* A Simple Yet High-Performing On-disk Learned Index: Can We Have Our Cake and Eat it Too? (aiXiv 2023)

* Fast Partitioned Learned Bloom Filter (arXiv 2023)

* Efficient Index Learning via Model Reuse and Fine-tuning (ICDEW 2023)

* COAX: Correlation-Aware Indexing (ICDEW 2023)

* Learned Index with Dynamic e (openreview 2023)

* Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads (arXiv 2023)

* SALI: A Scalable Adaptive Learned Index Framework based on Probability Models (SIGMODE 2024)

* Sieve: A Learned Data-Skipping Index for Data Analytics (VLDB 2023)

* Demonstrating Waffle: A Self-driving Grid Index (VLDB Demo 2023)

* Can LSH (Locality-Sensitive Hashing) Be Replaced by Neural Network? (arXiv 2023)

* Workload-aware and Learned Z-Indexes (arXiv 2023)

* AirIndex: Versatile Index Tuning Through Data and Storage (SIGMOD 2024)

* A Fast Learned Key-Value Store for Concurrent and Distributed Systems (TKDE 2023)

* When Learned Indexes Meet Persistent Memory: The Analysis and the Optimization (TKDE 2023)

* PLATON: Top-down R-tree Packing with Learned Partition Policy (PACMMOD 2023)

* A Learned Cuckoo Filter for Approximate Membership Queries over Variable-sized Sliding Windows on Data Streams (PACMMOD 2023)

* WIPE: a Write-Optimized Learned Index for Persistent Memory (TACO 2023)

* Algorithmic Complexity Attacks on Dynamic Learned Indexes (VLDB 2024)

* A Fully On-disk Updatable Learned Index (ICDE 2024)

* Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines (SIGMOD 2024)

* AStore: Uniformed Adaptive Learned Index and Cache for RDMA-enabled Key-Value Store (TKDE 2024)

* Cabin: A Compressed Adaptive Binned Scan Index (SIGMOD 2024)

* SWIX: A Memory-efficient Sliding Window Learned Index (SIGMOD 2024)

* Limousine: Blending Learned and Classical Indexes to Self-Design Larger-than-Memory Cloud Storage Engines (SIGMOD 2024)

* A Survey of Learned Indexes for the Multi-dimensional Space (arXiv 2024)

* Hyper: A High-Performance and Memory-Efficient Learned Index via Hybrid Construction (Proceedings of the ACM on Management of Data 2024)

* TRAP: Tailored Robustness Assessment for Index Advisors via Adversarial Perturbation (NAACL 2024)

* Predicate caching: Query-driven secondary indexing for cloud data warehouses (SIGMOD 2024)

* AStore: Uniformed Adaptive Learned Index and Cache for RDMA-Enabled Key-Value Store (TKDE 2024)

* Can Learned Indexes be Built Efficiently? A Deep Dive into Sampling Trade-offs (SIGMOD 2024)

* Making In-Memory Learned Indexes Efficient on Disk (SIGMOD 2024)

  

##### Index Recommendation

* Index Selection in a Self- Adaptive Data Base Management System （SIGMOD 1976）

* AutoAdmin 'What-if' Index Analysis Utility (SIGMOD 1998)

* Self-Tuning Database Systems: A Decade of Progress (VLDB 2007)

* AI Meets AI: Leveraging Query Executions to Improve Index Recommendations (SIGMOD 2019) 

* Automated Database Indexing using Model-free Reinforcement Learning (ICAPS 2020)

* DRLindex: deep reinforcement learning index advisor for a cluster database (2020 Symposium on International Database Engineering & Applications)

* Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms (VLDB 2020) [GitHub Link](https://github.com/hyrise/index_selection_evaluation)

* An Index Advisor Using Deep Reinforcement Learning (CIKM 2020) [GitHub Link](https://github.com/rmitbggroup/IndexAdvisor)

* DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees (ICDE 2021)

* MANTIS: Multiple Type and Attribute Index Selection using Deep Reinforcement Learning (IDEAS 2021)

* AutoIndex: An Incremental Index Management System for Dynamic Workloads (ICDE 2022) [GitHub Link](https://github.com/zhouxh19/AutoIndex)

* SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning (EDBT 2022) [GitHub Link](https://github.com/hyrise/rl_index_selection)

* Indexer++: workload-aware online index tuning with transformers and reinforcement learning (ACM SIGAPP SAC, 2022)

* Budget-aware Index Tuning with Reinforcement Learning (SIGMOD 2022)

* ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning (SIGMOD 2022)

* DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning (VLDB 2022)

* SmartIndex: An Index Advisor with Learned Cost Estimator (CIKM 2022)

* HMAB: self-driving hierarchy of bandits for integrated physical database design tuning (VLDB 2022)

* Learned Index Benefits: Machine Learning Based Index Performance Estimation (VLDB 2023) [GitHub Link](https://github.com/JC-Shi/Learned-Index-Benefits)

* AIM: A practical approach to automated index management for SQL databases (ICDE 2023)

* Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices (SIGMOD 2023)

* Index Tuning with Machine Learning on Quantum Computers for Large-Scale Database Applications (AIDB@VLDB 2023)

* A Data-Driven Index Recommendation System for Slow Queries (CIKM 2023)

* ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges (arXiv 2023)

* Robustness of Updatable Learning-based Index Advisors against Poisoning Attack (SIGMOD 2024)

* Refactoring Index Tuning Process with Benefit Estimation (VLDB 2024) [GitHub Link](https://github.com/HIT-DB-Group/RIBE)

* Leveraging Dynamic and Heterogeneous Workload Knowledge to Boost the Performance of Index Advisors (VLDB 2024) [GitHub Link](https://github.com/XMUDM/BALANCE)



### Materialized View

* Automatic View Generation with Deep Learning and Reinforcement Learning (ICDE 2020)

* An Autonomous Materialized View Management System with Deep Reinforcement Learning (ICDE 2021)

* A Technical Report on Dynamic Materialized View Management using Graph Neural Network

* HMAB: self-driving hierarchy of bandits for integrated physical database design tuning (VLDB 2022)

* AutoView: An Autonomous Materialized View Management System with Encoder-Reducer (TKDE 2022)

* Dynamic Materialized View Management using Graph Neural Network (ICDE 2023)

#### Schema & Partition

* Schism: a Workload-Driven Approach to Database Replication and Partitioning (VLDB 2010)

* Skew-Aware Automatic Database Partitioning in Shared-Nothing, Parallel OLTP Systems (SIGMOD 2012)

* Automated Data Partitioning for Highly Scalable and Strongly Consistent Transactions (2016 Transactions on Parallel and distributed systems)

* GridFormation : Towards Self-Driven Online Data Partitioning using Reinforcement Learning (aiDM@SIGMOD 2018)

* Learning a Partitioning Advisor with Deep Reinforcement Learning (2019)

* Qd-tree: Learning Data Layouts for Big Data Analytics (SIGMOD 2020)

* A Genetic Optimization Physical Planner for Big Data Warehouses (2020)

* Lachesis: Automated Partitioning for UDF-Centric Analytics (VLDB 2021)

* Instance-Optimized Data Layouts for Cloud Analytics Workloads (SIGMOD 2021)

* Jigsaw: A Data Storage and Query Processing Engine for Irregular Table Partitioning (SIGMOD 2021)

* Dalton: Learned Partitioning for Distributed Data Streams (VLDB 2023)

* Grep: A Graph Learning Based Database Partitioning System (Management of Data 2023)

* Learned spatial data partitioning （arXiv 2023)

* Relax and Let the Database Do the Partitioning Online (BIRTE 2011)

* SWORD: Scalable Workload-Aware Data Placement for Transactional Workloads (EDBT 2013)

* Online Data Partitioning in Distributed Database Systems (EDBT 2015)

* A Robust Partitioning Scheme for Ad-Hoc Query Workloads (SOCC 2017)

* Automated multidimensional data layouts in Amazon Redshift (SIGMOD 2024)

* Oasis: An Optimal Disjoint Segmented Learned Range Filter (VLDB 2024)



### Cache related

* A Learned Cache Eviction Framework with Minimal Overhead (arXiv 2023)



## Workload



### Resource Management and Auto-scaling



* Automated Demand-driven Resource Scaling in Relational Database-as-a-Service (SIGMOD 2016)

* Database Workload Capacity Planning using Time Series Analysis and Machine Learning (SIGMOD 2020)

* Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation (VLDB 2020)

* FIRM: An Intelligent Fine-grained Resource Management Framework for SLO-Oriented Microservices (OSDI 2020)

* Optimal Resource Allocation for Serverless Queries (arXiv 2021)

* sinan: ml-based and qos-aware resource management for cloud microservices (ASPLOS 2021)

* Towards Optimal Resource Allocation for Big Data Analytics (EDBT 2022)

* Tenant Placement in Over-subscribed Database-as-a-Service Clusters (VLDB 2022)

* Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big Data Processing (arXiv 2022)

* SIMPPO: a scalable and incremental online learning framework for serverless resource management (SoCC 2022)

* SUFS: A Generic Storage Usage Forecasting Service Through Adaptive Ensemble Learning (ICDE 2023)

* Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift (SIGMOD-Companion ’23)

* SeLeP: Learning Based Semantic Prefetching for Exploratory Database Workloads (arXiv 2023)

* Intelligent scaling in Amazon Redshift (SIGMOD 2024)



### Performance Diagnosis and Modeling



- Performance and resource modeling in highly-concurrent OLTP workloads (SIGMOD 2013)

- DBSherlock: A Performance Diagnostic Tool for Transactional Databases (SIGMOD 2016)

- A Top-Down Approach to Achieving Performance Predictability in Database Systems (SIGMOD 2017)

- Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases (VLDB 2020)

- Workload-Aware Performance Tuning for Autonomous DBMSs (ICDE 2021)

- Sage: Practical and Scalable ML-Driven Performance Debugging in Microservices (ASPLOS 2021)

- D-Bot: Database Diagnosis System using Large Language Models (arXiv 2023)

- Modeling Shifting Workloads for Learned Database Systems (SIGMOD 2024)



### Workload Shift Detection



- Towards workload shift detection and prediction for autonomic databases (CIKM 2007)

- Consistent on-line classification of dbs workload events (CIKM 2009)

- On predictive modeling for optimizing transaction execution in parallel OLTP systems (VLDB 2011)



### Workload Characterization & Forecasting



* On Workload Characterization of Relational Database Environments (TSE 1992)

* Workload Models for Autonomic Database Management Systems (International Conference on Autonomic and Autonomous Systems 2006)

* Workload characterization and prediction in the cloud: A multiple time series approach (APNOMS 2012）

* Query-based Workload Forecasting for Self-Driving Database Management Systems (SIGMOD 2018）

* Query2Vec: An Evaluation of NLP Techniques for Generalized Workload Analytics (Arxiv 2018)

* Database Workload Characterization with Query Plan Encoders (arXiv 2021)

* Explaining Inference Queries with Bayesian Optimization (VLDB 2021)

* Statistical Schema Learning with Occam's Razor (SIGMOD 2022)

* Intelligent Automated Workload Analysis for Database Replatforming (SIGMOD 2022)

* Stitcher: Learned Workload Synthesis from Historical Performance Footprints (EDBT 2022)

* DBAugur: An Adversarial-based Trend Forecasting System for Diversified Workloads (ICDE 2023)

* An Efficient Online Prediction of Host Workloads Using Pruned GRU Neural Nets (arXiv 2023)

* Uncertainty-Aware Workload Prediction in Cloud Computing (arXiv 2023)

* Real-Time Workload Pattern Analysis for Large-Scale Cloud Databases (VLDB 2023)



## Query Optimization

* Learned Query Optimizer: What is New and What is Next (SIGMOD 2024)

### Query Rewrite

* Sia: Optimizing Queries using Learned Predicates (SIGMOD 2021)

* A Learned Query Rewrite System using Monte Carlo Tree Search (VLDB 2022)

* WeTune: Automatic Discovery and Verification of Query Rewrite Rules (SIGMOD 2022)

* A Learned Query Rewrite System (VLDB 2023)

### Cardinality Estimation

* Are We Ready For Learned Cardinality Estimation? (VLDB 2021) [GitHub Link](https://github.com/sfu-db/AreCELearnedYet)

* A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation (SIGMOD 2021)

* LATEST: Learning-Assisted Selectivity Estimation Over Spatio-Textual Streams (ICDE 2021)

* Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation (VLDB 2021)

* Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation (arXiv 2021) [GitHub Link](https://github.com/Nathaniel-Han/End-to-End-CardEst-Benchmark)

* Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation (VLDB 2022)

* Glue: Adaptively Merging Single Table Cardinality to Estimate Join Query Size (aiXiv 2021)

* Unsupervised Selectivity Estimation by Integrating Gaussian Mixture Models and an Autoregressive Model (EDBT 2022)

* Selectivity Functions of Range Queries are Learnable (SIGMOD 2022)

* Prediction Intervals for Learned Cardinality Estimation: An Experimental Evaluation (ICDE 2022)

* Learned Cardinality Estimation: An In-depth Study (SIGMOD 2022)

* FactorJoin: A New Cardinality Estimation Framework for Join Queries (SIGMOD 2023)

* AutoCE: An Accurate and Efficient Model Advisor for Learned Cardinality Estimation (ICDE 2023)

* Couper: Memory-Efficient Cardinality Estimation under Unbalanced Distribution (ICDE 2023)

* ALECE: An Attention-based Learned Cardinality Estimator for SPJ Queries on Dynamic Workloads (VLDB 2023)

* Advanced Dataset Discovery: When Multi-Query-Dataset Cardinality Estimation Matters (aiXiv 2024)

* Sample-Efficient Cardinality Estimation Using Geometric Deep Learning (VLDB 2024)

* PRICE: A Pretrained Model for Cross-Database Cardinality Estimation (arXiv 2024) [GitHub Lint](https://github.com/StCarmen/PRICE)

* ByteCard: Enhancing ByteDance's Data Warehouse with Learned Cardinality Estimation (SIGMOD 2024)

* ASM in Action: Fast and Practical Learned Cardinality Estimation (SIGMOD 2024)

#### Data-based

* Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation (SIGMOD 2015)

* Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models (VLDB 2017)

* DeepDB: Learn from Data, not from Queries! (VLDB 2020) [GitHub Link](https://github.com/DataManagementLab/deepdb-public)

* Deep Unsupervised Cardinality Estimation (VLDB 2019) 

* Multi-Attribute Selectivity Estimation Using Deep Learning (arXiv 2019)

* Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries (SIGMOD 2020)

* NeuroCard: One Cardinality Estimator for All Tables (VLDB 2020) [GitHub Link](https://github.com/neurocard/neurocard)

* Learning to Sample: Counting with Complex Queries (VLDB 2020)

* Selectivity estimation using probabilistic models (SIGMOD 2001)

* Lightweight graphical models for selectivity estimation without independence assumptions (VLDB 2011)

* Efficiently adapting graphical models for selectivity estimation (VLDB 2013)

* An Approach Based on Bayesian Networks for Query Selectivity Estimation (DASFAA 2019)

* BayesCard: A Unified Bayesian Framework for Cardinality Estimation (arXiv 2020) [GitHub Link](https://github.com/wuziniu/BayesCard)

* Online Sketch-based Query Optimization (arXiv 2021)

* LMKG: Learned Models for Cardinality Estimation in Knowledge Graphs (arXiv 2021)

* LHist: Towards Learning Multi-dimensional Histogram for Massive Spatial Data (ICDE 2021)

* FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation (VLDB 2021) [GitHub Link](https://github.com/wuziniu/FSPN)

* Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning (VLDB 2021)

* FACE: A Normalizing Flow based Cardinality Estimator (VLDB 2022)

* Pre-training Summarization Models of Structured Datasets for Cardinality Estimation (VLDB 2022)

* Cardinality Estimation of Approximate Substring Queries using Deep Learning (VLDB 2022)

* Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation (Proceedings of the ACM on Management of Data)

* Cardinality estimation with smoothing autoregressive models (WWW 2023)

* Cardinality estimation using normalizing flow (VLDBJ 2023)

* LPLM: A Neural Language Model for Cardinality Estimation of LIKE-Queries (SIGMOD 2024)

* ASM: Harmonizing Autoregressive Model, Sampling, and Multi-dimensional Statistics Merging for Cardinality Estimation (SIGMOD 2024)

* ASM in Action: Fast and Practical Learned Cardinality Estimation (SIGMOD 2024)

#### Query-based

* Adaptive selectivity estimation using query feedback (SIGMOD 1994)

* Selectivity Estimation in Extensible Databases -A Neural Network Approach （VLDB 1998）

* Effective query size estimation using neural networks.  (Applied Intelligence 2002)

* LEO - DB2's LEarning optimizer （VLDB 2011)

* A Black-Box Approach to Query Cardinality Estimation (CIDR 07)

* Cardinality Estimation Using Neural Networks (2015)

* Towards a learning optimizer for shared clouds (VLDB 2018)

* Learning State Representations for Query Optimization with Deep Reinforcement Learning  (DEEM@SIGMOD2018)

* Learned Cardinalities: Estimating Correlated Joins with Deep Learning （CIDR2019）[GitHub Link](https://github.com/andreaskipf/learnedcardinalities)

* Estimating Cardinalities with Deep Sketches (SIGMOD 2019) [GitHub Link](https://github.com/andreaskipf/learnedcardinalities)

* Selectivity estimation for range predicates using lightweight models (VLDB 2019)

* (Review) An Empirical Analysis of Deep Learning for Cardinality Estimation (arXiv 2019)

* Flexible Operator Embeddings via Deep Learning (arXiv 2019)

* Improved Cardinality Estimation by Learning Queries Containment Rates (EDBT 2020)

* NN-based Transformation of Any SQL Cardinality Estimator for Handling DISTINCT, AND, OR and NOT (2020)

* QuickSel: Quick Selectivity Learning with Mixture Models (SIGMOD 2020)

* Efficiently Approximating Selectivity Functions using Low Overhead Regression Models (VLDB 2020)

* Learned Cardinality Estimation for Similarity Queries (SIGMOD 2021)

* Uncertainty-aware Cardinality Estimation by Neural Network Gaussian Process (arXiv 2021)

* Flow-Loss: Learning Cardinality Estimates That Matter (VLDB 2021)

* Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts (SIGMOD 2022)

* Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process for Approximate Complex Event Processing (SIGMOD 2022)

* Enhanced Featurization of Queries with Mixed Combinations of Predicates for ML-based Cardinality Estimation (EDBT 2023)

* Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation (SIGMOD 2023)

* Robust Query Driven Cardinality Estimation under Changing Workloads (VLDB 2023)

* Learned Probing Cardinality Estimation for High-Dimensional Approximate NN Search (ICDE 2023)

* CEDA: Learned Cardinality Estimation with Domain Adaptation (VLDB 2023)

* Efficient Cardinality and Cost Estimation with Bidirectional Compressor-based Ensemble Learning (arXiv 2023)

* Adding Domain Knowledge to Query-Driven Learned Databases (arXiv 2023)

* PACE: Poisoning Attacks on Learned Cardinality Estimation (SIGMOD 2024)

* Sample-Efficient Cardinality Estimation Using Geometric Deep Learning (VLDB 2024)

### Cost Estimation

#### Single Query

* Statistical learning techniques for costing XML queries (VLDB 2005)

* Predicting multiple metrics for queries: Better decisions enabled by machine learning （icde 2009)

* The Case for Predictive Database Systems : Opportunities and Challenges （CIDR 2011)

* Learning-based query performance modeling and prediction (ICDE 2012)

* Robust estimation of resource consumption for SQL queries using statistical techniques (VLDB 2012)

* Learning-based SPARQL query performance modeling and prediction (WWW 2017)

* Plan-Structured Deep Neural Network Models for Query Performance Prediction (arXiv 2019)

* An End-to-End Learning-based Cost Estimator (arXiv 2019)(VLDB 2019)

* Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings (2020)

* DBMS Fitting: Why should we learn what we already know? (CIDR 2020)

* A Note On Operator-Level Query Execution Cost Modeling (2020)

* Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction (VLDB 2022)

* Efficient Learning with Pseudo Labels for Query Cost Estimation (CIKM 2022)

* gCBO: A Cost-based Optimizer for Graph Databases (CIKM 2022)

* QueryFormer: A Tree Transformer Model for Query Plan Representation (VLDB 2022)

* BASE: Bridging the Gap between Cost and Latency for Query Optimization (VLDB 2023)

* Rethinking Learned Cost Models: Why Start from Scratch? (PACMMOD 2023)

* Budget-aware Query Tuning: An AutoML Perspective (arXiv 2024)

* OS Pre-trained Transformer: Predicting Query Latencies across Changing System Contexts [GitHub Link](https://github.com/parimarjan/LatencyPredictor)

#### Concurrent

* PQR: Predicting query execution times for autonomous workload management （ICAC 2008）

* Performance Prediction for Concurrent Database Workloads (SIGMOD 2011)

* Predicting completion times of batch query workloads using interaction-aware models and simulation(EDBT 2011)

* Interaction-aware scheduling of report-generation workloads (VLDB 2011) （有调度策略）

* Towards predicting query execution time for concurrent and dynamic database workloads (not machine learning) （VLDB 2014）

* Contender: A Resource Modeling Approach for Concurrent Query Performance Prediction （EDBT 2014）

* Query Performance Prediction for Concurrent Queries using Graph Embedding (VLDB 2020)

* Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload (SIGMOD 2021)

* A Resource-Aware Deep Cost Model for Big Data Query Processing (ICDE 2022)

* Stage: Query Execution Time Prediction in Amazon Redshif (SIGMOD 2024)

### Join Optimization

* Adaptive Optimization of Very Large Join Queries (SIGMOD 2018) (Not machine learning

* Deep Reinforcement Learning for Join Order Enumeration (aiDM@SIGMOD 2018)

* Learning to Optimize Join Queries With Deep Reinforcement Learning (ArXiv)

* Reinforcement Learning with Tree-LSTM for Join Order Selection (ICDE 2020)

* Research Challenges in Deep Reinforcement Learning-based Join Query Optimization (aiDM 2020)

* Efficient Join Order Selection Learning with Graph-based Representation (KDD 2022)

* SOAR:A Learned Join Order Selector with Graph Attention Mechanism （IJCNN 2022）

* Query Join Order Optimization Method Based on Dynamic Double Deep Q-Network (Electronics 2023)

* Coral: federated query join order optimization based on deep reinforcement learning (WWW 2023)

* JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning (arXiv 2023)

* Join Order Selection with Deep Reinforcement Learning: Fundamentals, Techniques, and Challenges (VLDB 2023)

* Sub-optimal Join Order Identification with L1-error (SIGMOD 2024)

### Query Plan

* Plan Selection Based on Query Clustering （VLDB 2002)

* Cost-Based Query Optimization via AI Planning (AAAI 2014)

* Sampling-Based Query Re-Optimization (SIGMOD 2016)

* Learning State Representations for Query Optimization with Deep Reinforcement Learning  (DEEM@SIGMOD2018)

* Towards a Hands-Free Query Optimizer through Deep Learning （CIDR 2019)

* Neo: A Learned Query Optimizer (VLDB 2019)

* Bao: Learning to Steer Query Optimizers (2020)

* ML-based Cross-Platform Query Optimization (ICDE 2020)

* Learning-based Declarative Query Optimization (2021)

* **Bao: Making Learned Query Optimization Practical** (SIGMOD 2021 **Best Paper**!) [Doc](https://rmarcus.info/bao_docs/introduction.html) [GitHub Link](https://github.com/learnedsystems/BaoForPostgreSQL)

* Microlearner: A fine-grained Learning Optimizer for Big Data Workloads at Microsoft (2021)

* Steering Query Optimizers: A Practical Take on Big Data Workloads (SIGMOD 2021)

* A Unified Transferable Model for ML-Enhanced DBMS (CIDR 2021)

* Balsa: Learning a Query Optimizer Without Expert Demonstrations (SIGMOD 2022)

* Leveraging Query Logs and Machine Learning for Parametric Query Optimization (VLDB 2022)

* Deploying a Steered Query Optimizer in Production at Microsoft (SIGMOD 2022)

* Building Learned Federated Query Optimizers (VLDB 2022 PhD Workshop)

* Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection (VLDB 2022)

* Lero: A Learning-to-Rank Query Optimizer (VLDB 2023) [GitHub Link](https://github.com/AlibabaIncubator/Lero-on-PostgreSQL)

* Learned Query Superoptimization (arXiv 2023)

* Kepler: Robust Learning for Faster Parametric Query Optimization (SIGMOD 2023)

* LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans (VLDB 2023)

* BitE : Accelerating Learned Query Optimization in a Mixed-Workload Environment (arXiv 2023)

* Reinforcement Learning-based SPARQL Join Ordering Optimizer

* LEON: A New Framework for ML-Aided Query Optimization (VLDB 2023)

* AutoSteer: Learned Query Optimization for Any SQL Database (VLDB 2023)

* FASTgres: Making Learned Query Optimizer Hinting Effective (VLDB 2023)

* Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis (VLDB 2023)

* QO-Insight: Inspecting Steered Query Optimizer (VLDB Demo 2023)

* QPSeeker: An Efficient Neural Planner combining both data and queries through Variational Inference (EDBT 2024)

* FOSS: A Self-Learned Doctor for Query Optimizer (arXiv 2023)

* Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries (PACMMOD 2023)

* A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies (VLDB 2024)

* Learned Optimizer for Online Approximate Query Processing in Data Exploration (TKDE 2024)

* A learning-based framework for spatial join processing: estimation, optimization and tuning (VLDB 2024)

* Roq: Robust Query Optimization Based on a Risk-aware Learned Cost Model (arXiv 2024)

* PLAQUE: Automated Predicate Learning at Query Time (SIGMOD 2024)

* Eraser: Eliminating Performance Regression on Learned Query Optimizer (VLDB 2024)

* Low Rank Approximation for Learned Query Optimization (aiDM 2024)

* Lero: applying learning-to-rank in query optimizer (VLDB 2024)

* RobOpt: A Tool for Robust Workload Optimization Based on Uncertainty-Aware Machine Learning (SIGMOD 2024)

  

## Query Execution

### Sort

* The Case for a Learned Sorting Algorithm (SIGMOD 2020)

* Defeating duplicates: A re-design of the LearnedSort algorithm (aiXiv 2021)

* Towards Parallel Learned Sorting (arXiv 2022)

### Join

* SkinnerDB : Regret-Bounded Query Evaluation via Reinforcement Learning (VLDB 2018)

* The Case for Learned In-Memory Joins (arXiv 2021)

### Adaptive Query Processing

* Eddies: Continuously adaptive query processing. (SIGMOD 2000)

* Micro adaptivity in Vectorwise (SIGMOD 2013)

* Cuttlefish: A Lightweight Primitive for Adaptive Query Processing (2018)

* Scalable Multi-Query Execution using Reinforcement Learning (SIGMOD 2021)

### Approximate Query Processing

* DBEST: Revisiting approximate query processing engines with machine learning models (SIGMOD 2019)

* LAQP: Learning-based Approximate Query Processing (2020)

* Approximate Query Processing for Data Exploration using Deep Generative Models (ICDE 2020)

* ML-AQP: Query-Driven Approximate Query Processing based on Machine Learning (2020)

* Approximate Query Processing for Group-By Queries based on Conditional Generative Models (2021)

* Learned Approximate Query Processing: Make it Light, Accurate and Fast (CIDR 2021)

* NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks (SIGMOD 2023)

* Exploiting Machine Learning Models for Approximate Query Processing (Big Data 2022)

* Tuple Bubbles: Learned Tuple Representations for Tunable Approximate Query Processing (aiDM 2023)

### Sheduling

* Workload management for cloud databases via machine learning (ICDE 2016 WiseDB)

* A learning-based service for cost and performance management of cloud databases （ICDEW 2017）(short version for WiSeDB)

* WiSeDB: A Learning-based Workload Management Advisor for Cloud Databases (2016 VLDB)

* Learning Scheduling Algorithms for Data Processing Clusters (SIGCOMM 2019)

* CrocodileDB: Efficient Database Execution through Intelligent Deferment (CIDT 2020)

* Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning (2020)

* Self-Tuning Query Scheduling for Analytical Workloads (SIGMOD 2021)

* LSched: A Workload-Aware Learned Query Scheduler for Analytical Database Systems (SIGMOD 2022)

* DBMLSched: Scheduling In-database Machine Learning Jobs (AIDB@VLDB 2023)

* Learning Interpretable Scheduling Algorithms for Data Processing Clusters (arXiv 2024)



(transaction 👇)



* Scheduling OLTP transactions via learned abort prediction (aiDM@SIGMOD 2019)

* Scheduling OLTP Transactions via Machine Learning （2019）

* Polyjuice: High-Performance Transactions via Learned Concurrency Control (OSDI 2021)



## Text-to-SQL

* SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning (arXiv 2017)

* An End-to-end Neural Natural Language Interface for Databases (arXiv 2018)

* SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task (EMNLP 2018)

* Robust Text-to-SQL Generation with Execution-Guided Decoding (arXiv 2018)

* Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation (ACL 2019)

* Global Reasoning over Database Structures for Text-to-SQL Parsing (EMNLP 2019)

* Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing (ACL 2019)

* Natural language to SQL: Where are we today? (VLDB 2020)

* Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing (EMNLP Findings 2020)

* RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers (ACL 2020)

* Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing (ACL 2020)

* TAPAS: Weakly Supervised Table Parsing via Pre-training (ACL 2020)

* TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data (ACL 2020)

* Semantic Evaluation for Text-to-SQL with Distilled Test Suites (EMNLP 2020)

* SMBOP: Semi-autoregressive Bottom-up Semantic Parsing (NAACL-HLT 2021)

* Natural SQL: Making SQL Easier to Infer from Natural Language Specifications (EMNLP Findings 2021)

* LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations (ACL 2021)

* Structure-Grounded Pretraining for Text-to-SQL (NAACL-HLT 2021)

* GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing (ICLR 2021)

* SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL (NeurIPS 2021)

* GP: Context-free Grammar Pre-training for Text-to-SQL Parsers (arXiv 2021)

* Relation Aware Semi-autoregressive Semantic Parsing for NL2SQL (arXiv 2021)

* On Robustness of Neural Semantic Parsers (EACL 2021)

* MT-Teql: Evaluating and Augmenting Neural NLIDB on Real-world Linguistic and Schema Variations (VLDB 2021)

* PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models (EMNLP 2021)

* Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training (AAAI 2021)

* Towards robustness of text-to-sql models against synonym substitution (ACL 2021)

* Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization (EMNLP 2021)

* CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex (arXiv 2022)

* S2SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers (arXiv 2022)

* UNIFIEDSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models (EMNLP 2022)

* RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL (EMNLP 2022)

* UNISAR: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL (arXiv 2022)

* N-Best Hypotheses Reranking for Text-To-SQL Systems (SLT 2022)

* Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph (KDD 2022)

* SeaD: End-to-end Text-to-SQL Generation with Schema-aware Denoising (NAACL-HLT Findings 2022)

* STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing (EMNLP Findings 2022)

* Towards Generalizable and Robust Text-to-SQL Parsing (EMNLP Findings 2022)

* SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (COLING 2022)

* Towards robustness of text-to-sql models against natural and realistic adversarial table perturbation (ACL 2022)

* Evaluating the Text-to-SQL Capabilities of Large Language Models (arXiv 2022)

* A survey on deep learning approaches for text-to-SQL (VLDBJ 2023)

* GAR: A Generate-and-Rank Approach for Natural Language to SQL Translation (ICDE 2023)

* Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing (arXiv 2023)

* Improving Generalization in Language Model-Based Text-to-SQL Semantic Parsing: Two Simple Semantic Boundary-Based Techniques (arXiv 2023)

* Exploring Chain-of-Thought Style Prompting for Text-to-SQL (arXiv 2023)

* Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning (SIGMOD 2023)

* Multitask pretraining with structured knowledge for text-to-SQL generation (ACL 2023)

* Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4 (VLDB Demo 2023)

* Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing (AAAI 2023)

* SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (arXiv 2023)

* Teaching Large Language Models to Self-Debug (arXiv 2023)

* A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability (arXiv 2023)

* DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction (arXiv 2023)

* C3: Zero-shot Text-to-SQL with ChatGPT (arXiv 2023)

* RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL (AAAI 2023)

* Dr.spider: A Diagnostic Evaluation Benchmark Towards Text-To-Sql Robustness (ICLR 2023)

* Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL (arXiv 2024)

* Natural language to SQL [Resource repo](https://github.com/yechens/NL2SQL)



## SQL Related

* Query2Vec (ArXiv)

* Facilitating SQL Query Composition and Analysis (ArXiv 2020)

* From Natural Language Processing to Neural Databases (VLDB 2021)

* BERT Meets Relational DB: Contextual Representations of Relational Databases

* LearnedSQLGen: Constraint-aware SQL Generation using Reinforcement Learning (SIGMOD 2022)

* PreQR: Pre-training Representation for SQL Understanding (SIGMDO 2022)

* From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management (VLDB 2022)

* Query Generation based on Generative Adversarial Networks (arXiv 2023)



## Stargazers over time



[![Stargazers over time](https://starchart.cc/LumingSun/ML4DB-paper-list.svg)](https://starchart.cc/LumingSun/ML4DB-paper-list)