Data compression and prediction using machine learning for industrial IoT
JunMin Park, Hyunjae Park, Young‐June Choi · 2018
Industrial IoT generates big data that is useful for getting insight from data analysis but storing all the data is a burden. To resolve it, we propose to compress the industrial data using neural network regression into a representative vector with lossy compression. For efficiency of the compression, we use the divide-and-conquer method such that the industrial data can be handled by the chunk size of data. Through our experiments, we verify that industrial data is represented by a function and predicted with high accuracy.