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@article{180105,
        author = {Sachin Sudhir Shinde},
        title = {Enhancing Data processing Services by optimizing Database queries and implementing Multithreading},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {538-545},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180105},
        abstract = {In the face of growing data complexity and 
volume, enhancing data processing performance is a 
critical challenge for modern information systems. This 
review investigates the dual approach of optimizing 
database queries and implementing multithreading to 
significantly boost the performance and scalability of 
data-driven applications. By examining foundational 
techniques, recent advancements in AI-driven query 
optimization, 
and 
multithreaded 
execution 
frameworks, the review highlights how hybrid models 
outperform traditional query processing paradigms. 
Furthermore, it discusses theoretical models, system 
architectures, experimental evaluations, and the 
integration of adaptive feedback mechanisms. The 
article concludes with a discussion on emerging 
research trends and suggests future directions to build 
faster, more intelligent data processing systems. This 
study aims to provide researchers and practitioners 
with a detailed understanding of the evolution, current 
state, and future potential of optimization and 
parallelism in database systems.},
        keywords = {Query Optimization, Multithreading,  Data Processing Systems, Machine Learning, Database  Performance, Adaptive Feedback, Parallel Execution,  AI-driven Optimization, Cost Estimation, Data  processing, Big Data.},
        month = {May},
        }