Paper list for database systems with artificial intelligence (machine learning, deep learning, reinforcement learning)
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
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欢迎大家补充!
- 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)
- 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)
- 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)
- Stacked Filters: Learning to Filter by Structure (VLDB 2021)
- LEA: A Learned Encoding Advisor for Column Stores (aiDM 2021)
- 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)
- 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
- 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)
- 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)
- 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 (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)
- An Index Advisor Using Deep Reinforcement Learning (CIKM 2020)
- DBA bandits: Self-driving index tuning under ad-hoc, analytical workloads with safety guarantees (ICDE 2021)
- 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)
- 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 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)
- Optimal Resource Allocation for Serverless Queries (arXiv 2021)
- 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)
- 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)
- PQR: Predicting Query Execution Times for Autonomous Workload Management (ICAC 2008)
- Predicting multiple metrics for queries: Better decisions enabled by machine learning (ICDE 2009)
- Learning-based SPARQL query performance modeling and prediction (WWW 2017)
- 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)
- Database Workload Characterization with Query Plan Encoders (arXiv 2021)
- Explaining Inference Queries with Bayesian Optimization (VLDB 2021)
- Sia: Optimizing Queries using Learned Predicates (SIGMOD 2021)
- A Learned Query Rewrite System using Monte Carlo Tree Search (VLDB 2022)
- Are We Ready For Learned Cardinality Estimation? (VLDB 2021) GitHub Link
- 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
- Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation (VLDB 2022)
(kernal density model)
- Self-Tuning, GPU-Accelerated Kernel Density Models for Multidimensional Selectivity Estimation (SIGMOD 2015)
- Estimating Join Selectivities using Bandwidth-Optimized Kernel Density Models (VLDB 2017) (sum-product network)
- DeepDB: Learn from Data, not from Queries! (VLDB 2020) GitHub Link
(autoregressive model)
- 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
- Learning to Sample: Counting with Complex Queries (VLDB 2020) (graphical models)
- 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
- 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
- Astrid: Accurate Selectivity Estimation for String Predicates using Deep Learning (VLDB 2021)
- FACE: A Normalizing Flow based Cardinality Estimator (VLDB 2022)
- 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
- Estimating Cardinalities with Deep Sketches (SIGMOD 2019) GitHub Link
- 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)
- 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)
- 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)
- 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)
- 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)
- 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 GitHub Link
- 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)
- The Case for a Learned Sorting Algorithm (SIGMOD 2020)
- Defeating duplicates: A re-design of the LearnedSort algorithm (aiXiv 2021)
- SkinnerDB : Regret-Bounded Query Evaluation via Reinforcement Learning (VLDB 2018)
- The Case for Learned In-Memory Joins (arXiv 2021)
- 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)
- 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)
- 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)
- CrocodileDB: Efficient Database Execution through Intelligent Deferment (CIDT 2020)
- Buffer Pool Aware Query Scheduling via Deep Reinforcement Learning (2020)
- Polyjuice: High-Performance Transactions via Learned Concurrency Control (arXiv 2021)
(transaction 👇)
- Scheduling OLTP transactions via learned abort prediction (aiDM@SIGMOD 2019)
- Scheduling OLTP Transactions via Machine Learning (2019)
- Query2Vec (ArXiv)
- An End-to-end Neural Natural Language Interface for Databases
- SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning (ArXiv)
- Facilitating SQL Query Composition and Analysis (ArXiv 2020)
- Natural language to SQL: Where are we today? (VLDB 2020)
- From Natural Language Processing to Neural Databases (VLDB 2021)
- BERT Meets Relational DB: Contextual Representations of Relational Databases
- Natural language to SQL Resource repo