Analytical Framework to Build Predictive and Optimization Functions From Manufacturing Industry Sensor Data Using Cross Sectional Sharing

Nowadays in industry sensor data are used. This needs to be shared in many areas for making the prediction. Also, it needs to be optimized for making the things to do automatically. This paper proposes a novel analytical framework to build predictive and optimization functions from manufacturing industry sensor data using cross sectional sharing which combines all different types of operation in a cross-sectional lab, which is a cooperative site in which huge quantities of data from numerous sites are composed as well as managed in a terrific way. The predictions and the optimization are made possible and store the same using the big data storage. Big Data Storage as well as Analytics Platform; Development Tools; Modelling Tools for Imitation Concepts as well as Power Framework are carried over in cross sectional lab. This is making the relations ship entities using Relational Data Base Management Systems (RDBMS). Various apache versions are used for the implementation of this which acts in a cloud platform. In the case study, the mean and variance were calculated and plotted.