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

• Papers_AIDB
  • Systems
  • Data Access
    • Configuration Tuning
    • Physical Design
      • Index
        • Index Structure
        • Index Recommendation
      • Schema & Partition
  • Workload
  • Query Optimization
    • Cardinality Estimation
      • Based on data distribution
      • Based on bayesian network (graphical models)
      • Based on history workload
      • Combining data and workload
    • Cost Estimation
      • Single Query
      • Concurrent
    • Join Optimization
    • Query Plan
  • Query Execution
    • Join
    • Adaptive Query Processing
    • Approximate Query Processing
    • Sheduling
  • SQL Related

• 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)

• 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)

• 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)

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

• 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)

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

• 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)

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

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

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

• Adaptive selectivity estimation using query feedback (SIGMOD 1994)

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

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

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

• DeepDB: Learn from Data, not from Queries! (VLDB 2020)

• Deep Unsupervised Cardinality Estimation (VLDB 2019) (Unsupervised)

• Selectivity estimation using probabilistic models (SIGMOD 2001)

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

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

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

• 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)

• 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 (2020)

• Bao: Learning to Steer Query Optimizers (2020)

• Learned Cardinalities: Estimating Correlated Joins with Deep Learning （CIDR2019）

• Estimating Cardinalities with Deep Sketches (SIGMOD 2019)

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

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

• 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)

• 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）

• 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)

• 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)

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

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

• Micro adaptivity in Vectorwise (SIGMOD 2013)

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

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

• 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)

• 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)