A Real-Time Hadoop Bigdata Maintenance Model using A Software-Defined and U-Net Deep Learning Mode - Kumar, G. Nanda Kishor; Reddy, M. Srinivasa; Malleswari, D. Naga; Rao, K. Madhusudhana; Saikumar, K. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
An advanced big data platform will include several functions, such as the ability to administer servers, the cloud, and Hadoop. However, the present big data infrastructure, which employs Hadoop models, has limitations that prevent the quick delivery of dynamic operations. Problems with storage and latency are threatening the robustness of applications. The failure to leverage the Internet and big data platforms to direct manufacturing activities is another problem with cloud storage maintenance. It is critical to handle these challenges by continuously growing and evolving big data cloud systems, which are driven by the effective processing of enormous data at cloud gateways. The sponsored software arrangement enables the DL-enabled Operations Facilities concept, which is introduced in this work. Through the use of intelligent closed-loop video monitoring, this technology expedites the processing and updating of procedures for managing large data files. The primary objective of this research is to develop more efficient methods for creating large data files on the cloud. The research is conducted using U-net, a network architecture that has been tested on Python 3.7 and is built on Hadoop and Spark. The achieved performance parameters outperform the industry standard with values of 97.23 percent recall, 98.92 percent sensitivity, and 99.23 percent throughput. The presented U-net-based big data analytics solution outperforms state-of-the-art technology.