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<p>Query optimization is a critical aspect of database management and information retrieval system. It ensure efficient, accurate, and timely access to data. Query optimization is vital to maintain efficient, cost-effective, and scalable database operations. It directly impact system performance and user satisfaction. It is a cornerstone for any data-intensive application. This study investigates strategies to optimize database performance under different query loads. Which focus on key performance metrics such as latency, scalability, consistency, availability, replication lag, and fault tolerance. The proposed hybrid ML-DBMS engine provides smooth communication between the ML models and the query optimization procedure. This integration helps in significant gains in query response times and system throughput. which also enables dynamic workload management and automated model retraining. The findings highlight that the optimized approach consistently outperforms alternatives technique in managing key lookups, handling collisions, and maintaining threshold stability under high query loads. The results of this study demonstrate that the optimized proposed approach (Hybrid ML-DBMS Engine) significantly outperforms both the initial proposed approach and the existing approach.</p>

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Query load management: an approach for optimizing database performance

  • 期刊论文

  • 发表日期：

    2025年9月6日

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  • Kewal Krishan,
  • Gaurav Gupta,
  • Gurjit Singh Bhathal

摘要

Query optimization is a critical aspect of database management and information retrieval system. It ensure efficient, accurate, and timely access to data. Query optimization is vital to maintain efficient, cost-effective, and scalable database operations. It directly impact system performance and user satisfaction. It is a cornerstone for any data-intensive application. This study investigates strategies to optimize database performance under different query loads. Which focus on key performance metrics such as latency, scalability, consistency, availability, replication lag, and fault tolerance. The proposed hybrid ML-DBMS engine provides smooth communication between the ML models and the query optimization procedure. This integration helps in significant gains in query response times and system throughput. which also enables dynamic workload management and automated model retraining. The findings highlight that the optimized approach consistently outperforms alternatives technique in managing key lookups, handling collisions, and maintaining threshold stability under high query loads. The results of this study demonstrate that the optimized proposed approach (Hybrid ML-DBMS Engine) significantly outperforms both the initial proposed approach and the existing approach.