The Neural Schema for Big Data

Abstract

The paper tries to resolve the complexity encountered in processing of Big Data through various methodology which includes Reverse Geometric Data Perturbation to estimate the spectral flow of data through new learning methods for underlying neural networks. It also has insights into the Banach-Tarsky Paradox to separate the different zones of spectrum, which helps in preventing the analysis of overlapping. The MapReduce implementation can have multiple p-values separation at sublevels to sample out the data and demarcate the different levels of spectrum along with inspecting out the uncertainty in each step as in Monty Hall Problem. It uses the statistical reference to the processing of data in Large Hadrons Collider which extracts out data in ratio 1:6000 for interesting to non-interesting physics which is further reduced in next step cumulating to 1:6000000. It also uses the data processing mechanism of Universe defined through Spiral Hashed Information Vessel.