Research on Distributed Redundant Data Estimation Based on LIC

Abstract

The problem of data redundancy in distributed storage has become increasingly pronounced, posing significant challenges for the estimation of target variables. This study introduces a distributed redundant data estimation method that employs the LIC criterion. Through simulation, the method's predictive accuracy is rigorously estimated, and its stability and sensitivity are thoroughly investigated. Results demonstrate the method's effectiveness in extracting valuable information from redundant distributed data. By identifying the optimal data subset, it enhances data quality and boosts efficiency, making it a potent strategy for tackling data analysis challenges inherent in big data environments.