RL_QOptimizer: A Reinforcement Learning Based Query Optimizer
With the current availability of massive datasets and scalability requirements, different systems are required to provide their users with the best performance possible in terms of speed. On the physical level, performance can be translated into queries’ execution time in database managem...
Main Authors: | Mohamed Ramadan, Ayman El-Kilany, Hoda M. O. Mokhtar, Ibrahim Sobh |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2022-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/9810215/ |
Similar Items
-
Query Recommendation Using Hybrid Query Relevance
by: Jialu Xu, et al.
Published: (2018-11-01) -
Shared Execution Approach to ε-Distance Join Queries in Dynamic Road Networks
by: Hyung-Ju Cho
Published: (2018-07-01) -
Learned Query Optimizers: Evaluation and Improvement
by: Artem Mikhaylov, et al.
Published: (2022-01-01) -
Skyline computation for frequent queries in update intensive environment
by: R.D. Kulkarni, et al.
Published: (2016-10-01) -
Query Refinement into Information Retrieval Systems: An Overview
by: Mawloud Mosbah
Published: (2023-01-01)