Article

Self-Tuning for SQL Performance in Oracle Database 11g

Authors:

Peter Belknap,

Benoit Dageville,

Karl Dias, and

Khaled YagoubAuthors Info & Claims

ICDE '09: Proceedings of the 2009 IEEE International Conference on Data Engineering

March 2009

Pages 1694 - 1700

https://doi.org/10.1109/ICDE.2009.165

Published: 29 March 2009 Publication History

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Abstract

Commercial database customers across the board list SQL performance tuning as one of the most time-consuming tasks for database administrators (DBAs). The 10g Oracle Database provides a feature called the SQL Tuning Advisor to simplify the task. The 11g release adds a new database feature, called Automatic SQL Tuning, that closes the feedback loop for the first time, fully automating the SQL tuning workflow and solving some SQL performance problems without any DBA intervention.

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Li GZhou XLi SGao B(2019)QTuneProceedings of the VLDB Endowment10.14778/3352063.335212912:12(2118-2130)Online publication date: 1-Aug-2019
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Chatzopoulos GDragojević AGuerraoui RPierre GFerreira PShrira L(2018)SPADEProceedings of the 19th International Middleware Conference10.1145/3274808.3274815(80-93)Online publication date: 26-Nov-2018
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Self-Tuning for SQL Performance in Oracle Database 11g

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Published In

ICDE '09: Proceedings of the 2009 IEEE International Conference on Data Engineering

March 2009

1772 pages

ISBN:9780769535456

Publisher

IEEE Computer Society

United States

Publication History

Published: 29 March 2009

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Li GZhou XLi SGao B(2019)QTuneProceedings of the VLDB Endowment10.14778/3352063.335212912:12(2118-2130)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.14778/3352063.3352129
Zhang JLiu YZhou KLi GXiao ZCheng BXing JWang YCheng TLiu LRan MLi ZBoncz PManegold SAilamaki ADeshpande AKraska T(2019)An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement LearningProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3300085(415-432)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3300085
Chatzopoulos GDragojević AGuerraoui RPierre GFerreira PShrira L(2018)SPADEProceedings of the 19th International Middleware Conference10.1145/3274808.3274815(80-93)Online publication date: 26-Nov-2018
https://dl.acm.org/doi/10.1145/3274808.3274815
Van Aken DPavlo AGordon GZhang BChirkova RYang JSuciu D(2017)Automatic Database Management System Tuning Through Large-scale Machine LearningProceedings of the 2017 ACM International Conference on Management of Data10.1145/3035918.3064029(1009-1024)Online publication date: 9-May-2017
https://dl.acm.org/doi/10.1145/3035918.3064029
źAtuszko M(2017)Cost effective storage space for data cubesJournal of Intelligent Information Systems10.1007/s10844-016-0408-548:2(243-261)Online publication date: 1-Apr-2017
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Yoon DNiu NMozafari BÖzcan FKoutrika GMadden S(2016)DBSherlockProceedings of the 2016 International Conference on Management of Data10.1145/2882903.2915218(1599-1614)Online publication date: 26-Jun-2016
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Ahmad MDuan SAboulnaga ABabu S(2011)Predicting completion times of batch query workloads using interaction-aware models and simulationProceedings of the 14th International Conference on Extending Database Technology10.1145/1951365.1951419(449-460)Online publication date: 21-Mar-2011
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Herodotou HBabu S(2010)XplusProceedings of the VLDB Endowment10.14778/1920841.19209843:1-2(1149-1160)Online publication date: 1-Sep-2010
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Oracle High-Performance SQL Tuning

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