Survey on performance optimization for database systems

[1]  Milo Tomasevic,et al.  Automatic Database Troubleshooting of Azure SQL Databases , 2022, IEEE Transactions on Cloud Computing.

[2]  Bin Cui,et al.  Facilitating database tuning with hyper-parameter optimization , 2022, Proceedings of the VLDB Endowment.

[3]  Durgesh Samariya,et al.  A New Dimensionality-Unbiased Score for Efficient and Effective Outlying Aspect Mining , 2022, Data Science and Engineering.

[4]  Bin Cui,et al.  Towards Dynamic and Safe Configuration Tuning for Cloud Databases , 2022, SIGMOD Conference.

[5]  Bin Cui,et al.  Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation , 2021, Proc. VLDB Endow..

[6]  Chengliang Chai,et al.  Database Meets Artificial Intelligence: A Survey , 2020, IEEE Transactions on Knowledge and Data Engineering.

[7]  Yunpeng Chai,et al.  WATuning: A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning , 2021, Journal of Computer Science and Technology.

[8]  Jie Jiao,et al.  MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems , 2021, SIGMOD Conference.

[9]  Xinyi Zhang,et al.  ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases , 2021, SIGMOD Conference.

[10]  Jiawei Jiang,et al.  OpenBox: A Generalized Black-box Optimization Service , 2021, KDD.

[11]  Christian S. Jensen,et al.  Dragoon: a hybrid and efficient big trajectory management system for offline and online analytics , 2021, The VLDB Journal.

[12]  Zhifeng Bao,et al.  A Survey on Advancing the DBMS Query Optimizer: Cardinality Estimation, Cost Model, and Plan Enumeration , 2021, Data Science and Engineering.

[13]  Jiahai Yang,et al.  FluxInfer: Automatic Diagnosis of Performance Anomaly for Online Database System , 2020, 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC).

[14]  Z. Bao,et al.  An Index Advisor Using Deep Reinforcement Learning , 2020, CIKM.

[15]  Tao Xie,et al.  Database-Access Performance Antipatterns in Database-Backed Web Applications , 2020, 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[16]  Le Gruenwald,et al.  DRLindex: deep reinforcement learning index advisor for a cluster database , 2020, IDEAS.

[17]  Khuzaima Daudjee,et al.  Sentinel: Universal Analysis and Insight for Data Systems , 2020, Proc. VLDB Endow..

[18]  Stefan Halfpap,et al.  Magic mirror in my hand, which is the best in the land? , 2020, Proc. VLDB Endow..

[19]  Vivek Narasayya,et al.  Anytime Algorithm of Database Tuning Advisor for Microsoft SQL Server , 2020 .

[20]  Lucian Carata,et al.  To Tune or Not to Tune?: In Search of Optimal Configurations for Data Analytics , 2020, KDD.

[21]  Jianhua Feng,et al.  Query performance prediction for concurrent queries using graph embedding , 2020, Proc. VLDB Endow..

[22]  Joy Arulraj,et al.  SQLCheck: Automated Detection and Diagnosis of SQL Anti-Patterns , 2020, SIGMOD Conference.

[23]  Guoliang Li,et al.  Automatic View Generation with Deep Learning and Reinforcement Learning , 2020, 2020 IEEE 36th International Conference on Data Engineering (ICDE).

[24]  Le Gruenwald,et al.  Online Index Selection Using Deep Reinforcement Learning for a Cluster Database , 2020, 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW).

[25]  Shenglin Zhang,et al.  Diagnosing root causes of intermittent slow queries in cloud databases , 2020, Proc. VLDB Endow..

[26]  Haibo Chen,et al.  Optimistic Transaction Processing in Deterministic Database , 2020, Journal of Computer Science and Technology.

[27]  Shivnath Babu,et al.  Black or White? How to Develop an AutoTuner for Memory-based Analytics , 2020, SIGMOD Conference.

[28]  Robert B. Ross,et al.  Mochi: Composing Data Services for High-Performance Computing Environments , 2020, Journal of Computer Science and Technology.

[29]  Shenglin Zhang,et al.  Diagnosing Root Causes of Intermittent Slow Queries in Large-Scale Cloud Databases. , 2020, VLDB 2020.

[30]  Yunpeng Chai,et al.  Smart Intra-query Fault Tolerance for Massive Parallel Processing Databases , 2019, Data Science and Engineering.

[31]  Guoliang Li,et al.  QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning , 2019, Proc. VLDB Endow..

[32]  Ke Zhou,et al.  An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning , 2019, SIGMOD Conference.

[33]  Surajit Chaudhuri,et al.  AI Meets AI: Leveraging Query Executions to Improve Index Recommendations , 2019, SIGMOD Conference.

[34]  Guoliang Li,et al.  An End-to-End Learning-based Cost Estimator , 2019, Proc. VLDB Endow..

[35]  Shivnath Babu,et al.  iQCAR: inter-Query Contention Analyzer for Data Analytics Frameworks , 2019, SIGMOD Conference.

[36]  Jeffrey C. Mogul,et al.  Nines are Not Enough: Meaningful Metrics for Clouds , 2019, HotOS.

[37]  Rainer Schlosser,et al.  Efficient Scalable Multi-attribute Index Selection Using Recursive Strategies , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).

[38]  Sanjay Krishnan,et al.  Opportunistic View Materialization with Deep Reinforcement Learning , 2019, ArXiv.

[39]  Olga Papaemmanouil,et al.  Plan-Structured Deep Neural Network Models for Query Performance Prediction , 2019, Proc. VLDB Endow..

[40]  Aaron J. Elmore,et al.  MgCrab: Transaction Crabbing for Live Migration in Deterministic Database Systems , 2019, Proc. VLDB Endow..

[41]  Olga Papaemmanouil,et al.  NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning , 2018, SIGMOD Conference.

[42]  Michael Stonebraker,et al.  P-Store: An Elastic Database System with Predictive Provisioning , 2018, SIGMOD Conference.

[43]  Yu Xie,et al.  TcpRT: Instrument and Diagnostic Analysis System for Service Quality of Cloud Databases at Massive Scale in Real-time , 2018, SIGMOD Conference.

[44]  Hiren Patel,et al.  Computation Reuse in Analytics Job Service at Microsoft , 2018, SIGMOD Conference.

[45]  Yunjun Gao,et al.  UlTraMan: A Unified Platform for Big Trajectory Data Management and Analytics , 2018, Proc. VLDB Endow..

[46]  Hiren Patel,et al.  Selecting Subexpressions to Materialize at Datacenter Scale , 2018, Proc. VLDB Endow..

[47]  Jens Dittrich,et al.  The Case for Automatic Database Administration using Deep Reinforcement Learning , 2018, ArXiv.

[48]  Yuqing Zhu,et al.  BestConfig: tapping the performance potential of systems via automatic configuration tuning , 2017, SoCC.

[49]  Shivnath Babu,et al.  Analyzing Query Performance and Attributing Blame for Contentions in a Cluster Computing Framework , 2017, ArXiv.

[50]  Twittie Senivongse,et al.  SQL antipatterns detection and database refactoring process , 2017, 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[51]  Geoffrey J. Gordon,et al.  Automatic Database Management System Tuning Through Large-scale Machine Learning , 2017, SIGMOD Conference.

[52]  Claudio Martella,et al.  Spinner: Scalable Graph Partitioning in the Cloud , 2014, 2017 IEEE 33rd International Conference on Data Engineering (ICDE).

[53]  Lin Ma,et al.  Self-Driving Database Management Systems , 2017, CIDR.

[54]  Michael Stonebraker,et al.  Clay: Fine-Grained Adaptive Partitioning for General Database Schemas , 2016, Proc. VLDB Endow..

[55]  Barzan Mozafari,et al.  DBSherlock: A Performance Diagnostic Tool for Transactional Databases , 2016, SIGMOD Conference.

[56]  Stéphane Bressan,et al.  Regularized Cost-Model Oblivious Database Tuning with Reinforcement Learning , 2016, Trans. Large Scale Data Knowl. Centered Syst..

[57]  Yuval Tassa,et al.  Continuous control with deep reinforcement learning , 2015, ICLR.

[58]  Viktor Leis,et al.  How Good Are Query Optimizers, Really? , 2015, Proc. VLDB Endow..

[59]  Barzan Mozafari,et al.  DBSeer: Pain-free Database Administration through Workload Intelligence , 2015, Proc. VLDB Endow..

[60]  Divyakant Agrawal,et al.  Squall: Fine-Grained Live Reconfiguration for Partitioned Main Memory Databases , 2015, SIGMOD Conference.

[61]  Murat Ali Bayir,et al.  Robust heuristic algorithms for exploiting the common tasks of relational cloud database queries , 2015, Appl. Soft Comput..

[62]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[63]  Cynthia Rudin,et al.  The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification , 2014, NIPS.

[64]  Michael Stonebraker,et al.  E-Store: Fine-Grained Elastic Partitioning for Distributed Transaction Processing , 2014, Proc. VLDB Endow..

[65]  Ashraf Aboulnaga,et al.  Accordion: Elastic Scalability for Database Systems Supporting Distributed Transactions , 2014, Proc. VLDB Endow..

[66]  Ying Feng,et al.  CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling , 2014, Appl. Soft Comput..

[67]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[68]  Carlo Curino,et al.  Performance and resource modeling in highly-concurrent OLTP workloads , 2013, SIGMOD '13.

[69]  Sam Shah,et al.  Root cause detection in a service-oriented architecture , 2013, SIGMETRICS '13.

[70]  Jeffrey F. Naughton,et al.  Predicting query execution time: Are optimizer cost models really unusable? , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[71]  Carlo Curino,et al.  DBSeer: Resource and Performance Prediction for Building a Next Generation Database Cloud , 2013, CIDR.

[72]  Nicolas Bruno,et al.  SCOPE: parallel databases meet MapReduce , 2012, The VLDB Journal.

[73]  Michael J. Franklin,et al.  Resilient Distributed Datasets: A Fault-Tolerant Abstraction for In-Memory Cluster Computing , 2012, NSDI.

[74]  Jennifer Neville,et al.  Structured Comparative Analysis of Systems Logs to Diagnose Performance Problems , 2012, NSDI.

[75]  Eli Upfal,et al.  Learning-based Query Performance Modeling and Prediction , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[76]  Divyakant Agrawal,et al.  Zephyr: live migration in shared nothing databases for elastic cloud platforms , 2011, SIGMOD '11.

[77]  Divyakant Agrawal,et al.  Albatross: Lightweight Elasticity in Shared Storage Databases for the Cloud using Live Data Migration , 2011, Proc. VLDB Endow..

[78]  Philip A. Bernstein,et al.  Adapting microsoft SQL server for cloud computing , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[79]  Anastasia Ailamaki,et al.  CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads , 2011, Proc. VLDB Endow..

[80]  Kevin Leyton-Brown,et al.  Sequential Model-Based Optimization for General Algorithm Configuration , 2011, LION.

[81]  Ahmed Syed Irshad,et al.  Markov Decision Process , 2011 .

[82]  Adam Silberstein,et al.  Benchmarking cloud serving systems with YCSB , 2010, SoCC '10.

[83]  Dong,et al.  A Distributed In-Memory Database Solution for Mass Data Applications , 2010 .

[84]  Michael Werman,et al.  Fast and robust Earth Mover's Distances , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[85]  Shivnath Babu,et al.  Tuning Database Configuration Parameters with iTuned , 2009, Proc. VLDB Endow..

[86]  Lise Getoor,et al.  Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications , 2009, Proc. VLDB Endow..

[87]  Archana Ganapathi,et al.  Predicting Multiple Metrics for Queries: Better Decisions Enabled by Machine Learning , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[88]  Ah Chung Tsoi,et al.  The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.

[89]  Michael Stonebraker,et al.  H-store: a high-performance, distributed main memory transaction processing system , 2008, Proc. VLDB Endow..

[90]  Jingren Zhou,et al.  SCOPE: easy and efficient parallel processing of massive data sets , 2008, Proc. VLDB Endow..

[91]  Suman Nath,et al.  Energy-Aware Server Provisioning and Load Dispatching for Connection-Intensive Internet Services , 2008, NSDI.

[92]  Alan Fekete,et al.  The Cost of Serializability on Platforms That Use Snapshot Isolation , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[93]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[94]  Sam Lightstone,et al.  Adaptive self-tuning memory in DB2 , 2006, VLDB.

[95]  Anastasia Ailamaki,et al.  Continuous resource monitoring for self-predicting DBMS , 2005, 13th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems.

[96]  Surajit Chaudhuri,et al.  Automatic physical database tuning: a relaxation-based approach , 2005, SIGMOD '05.

[97]  Graham Wood,et al.  Automatic Performance Diagnosis and Tuning in Oracle , 2005, CIDR.

[98]  Francisco Herrera,et al.  Real-Coded Memetic Algorithms with Crossover Hill-Climbing , 2004, Evolutionary Computation.

[99]  Margo I. Seltzer,et al.  Using probabilistic reasoning to automate software tuning , 2004, SIGMETRICS '04/Performance '04.

[100]  Wenpu Xing,et al.  Weighted PageRank algorithm , 2004, Proceedings. Second Annual Conference on Communication Networks and Services Research, 2004..

[101]  Hamid Pirahesh,et al.  Recommending materialized views and indexes with the IBM DB2 design advisor , 2004, International Conference on Autonomic Computing, 2004. Proceedings..

[102]  Michail G. Lagoudakis,et al.  Least-Squares Policy Iteration , 2003, J. Mach. Learn. Res..

[103]  Patrick Martin,et al.  Techniques for automatically sizing multiple buffer pools in DB2 , 2003, CASCON.

[104]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

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