Reduce the Customer’s Complaint Resolution Time in Migration of Oracle Database Servers in Vmware

The following report titled “Reduce the Customer’s Complaint Resolution time in migration of oracle database servers in VMware” clearly elaborates about the process, technical design and flow of the VMware support project. The whole processed is carried out at NTT data, which is a client of VMware technologies, Inc. Since 1967, NTT DATA has played an instrumental role in establishing and advancing Japanese IT infrastructure, in particular large-scale and mission critical IT systems. Originally Data Communication Headquarter of Nippon Telegraph and Telephone Public Corporation, its heritage contributed to social benefits with a quality-first mindset. Having spun-off from NTT in 1988 and going public in 1995, the company maintained a hybrid culture of long-term commitment and challenge to innovation in order to contribute to the progression of business and society. Tier-1 IP network is driven by financially-backed service level agreements (SLAs), secure networking and managed VPNs, reliable content distribution capability via Smart Content Delivery. Across the organization without any geographical specification, an average of 185 support tickets is being generated on a daily basis. Generally a Service level agreement is done initially between the company and the in order to achieve the success rate of the projects. An initial study has been conducted In VMware support project to see whether if the resolution time in SLA is meeting the client requirement. But It is observed that process mean of resolution time is at is at 9.146 hrs. and Standard deviation is 3.081 hrs. To improve the Resolution time of oracle support database project on VMware to meet the client SLA of 8hrs. Also to 3 consider if any external and internal factors affect the root cause of the study. The Process improvement initiative for making this process as capable. DMAIC Methodology, Box plot, probability chart are used in the project for the measuring and study of the process

Machine Learning Techniques in Indoor Environmental Quality Assessment

This chapter provides a comprehensive exploration of the evolving role of machine learning in Indoor Environmental Quality (IEQ) assessment. As urban living spaces become increasingly enclosed, the importance of maintaining optimal IEQ for human health and well-being has surged. Traditional methods for IEQ assessment, while effective, often fail to provide real-time monitoring and control. This gap is increasingly being addressed by the integration of machine learning techniques, allowing for enhanced predictive modeling, real-time optimization, and robust anomaly detection. The chapter delves into a comparative analysis of various machine learning techniques including supervised, unsupervised, and reinforcement learning, demonstrating their unique benefits in IEQ assessment. Practical implementations of these techniques in residential, commercial, and specialized environments are further illustrated through detailed case studies. The chapter also addresses the existing challenges in implementing machine learning for IEQ assessment and provides an outlook on future trends and potential research directions. The comprehensive review offered in this chapter encourages continued innovation and research in leveraging machine learning. for more efficient and effective IEQ assessment