Optimizing Query Performance in Distributed Databases Using Machine Learning Techniques: A Comprehensive Analysis and Implementation

@article{1702344,
      author = {Dheerender Thakur},
      title = {Optimizing Query Performance in Distributed Databases Using Machine Learning Techniques: A Comprehensive Analysis and Implementation},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {12},
      pages = {266-276},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17023441.pdf},
      abstract = {The optimization of query performance in distributed databases is a critical challenge due to the complex nature of data distribution, network latency, and varying system loads. Traditional query optimization techniques often struggle to address these issues effectively. This paper explores the application of machine learning techniques to enhance query optimization in distributed environments. By leveraging supervised learning, unsupervised learning, and reinforcement learning, we aim to improve the efficiency of query execution and resource utilization. The study involves implementing these techniques, evaluating their performance through comprehensive experiments, and analyzing their impact on query processing. Results indicate that machine learning models, particularly reinforcement learning, offer significant improvements in handling dynamic workloads and optimizing query performance. This research highlights the potential of machine learning to address the limitations of traditional methods and provides insights into future directions for advancing query optimization in distributed databases.},
      keywords = {Distributed databases, Query optimization. Machine learning, Supervised learning, Unsupervised learning, Reinforcement learning},
      month = {June},
  }