Abstract
Database Management Systems (DBMS) advancement has been crucial to Information Technology (IT). Traditional DBMS needed help managing large and varied datasets under strict time constraints due to the emergence of Big Data and the widespread use of Internet of Things (IoT) devices. The growing intricacy of data and the need for instantaneous processing presented substantial obstacles. This research suggests a Machine Learning-based Intelligent Database Management Systems (ML-IDMS) technique. This invention combines the skills of Machine Learning with DBMS, improving flexibility and decision-making capacities. The ML-IDMS is specifically developed to tackle current obstacles by providing capabilities such as instantaneous data retrieval, intelligent heat measurement, and effective neural network initialization. The simulation results showcase the effectiveness of ML-IDMS, as shown by impressive metrics such as query execution time (19.27 sec), storage efficiency (83.78%), data accuracy (90%), redundancy reduction (66.42%), network throughput (7.93 Gbps), and end-to-end delay (14.4 ms). The results highlight the efficacy of ML-IDMS in managing various data circumstances. ML-IDMS addresses current obstacles and establishes a standard for future intelligent data management and analytics progress.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Internet Services and Information Security
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
