LumingSun/ML4DB-paper-list: Papers for database systems powered by artificial in ...

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LumingSun/ML4DB-paper-list

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

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开源软件介绍：

[Paper List] AIDB / ML4DB / Autonomous Database / 智能数据库 / Self-driving Database

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

Welcome to PR!

欢迎大家补充！

Stargazers over time

System & Tutorial
Data Access
- Configuration Tuning
- Physical Design
Workload
Query Optimization
Query Execution
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)
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)

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)

Physical Design

Learned structure

Stacked Filters: Learning to Filter by Structure (VLDB 2021)
LEA: A Learned Encoding Advisor for Column Stores (aiDM 2021)

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)

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
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)
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)

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 (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)
AutoIndex: An Incremental Index Management System for Dynamic Workloads (ICDE 2022)
SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning (EDBT 2022)

Schema & Partition

Offline

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)

Online

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)

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)

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)

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)

Metrics Prediction for Queries

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)

Workload Characterization

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)

Query Optimization

Query Rewrite

Sia: Optimizing Queries using Learned Predicates (SIGMOD 2021)
A Learned Query Rewrite System using Monte Carlo Tree Search (VLDB 2022)

Cardinality Estimation

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)
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)

Data-based

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

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
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)

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)
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 (arXiv 2022)

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)

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)

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 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)
Balsa: Learning a Query Optimizer Without Expert Demonstrations (arXiv 2022)
Leveraging Query Logs and Machine Learning for Parametric Query Optimization (VLDB 2022)

Query Execution

Sort

The Case for a Learned Sorting Algorithm (SIGMOD 2020)
Defeating duplicates: A re-design of the LearnedSort algorithm (aiXiv 2021)

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)

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)
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