Mathematical Data Kernels for Lossless Large-File Reconstruction
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Solution Overview
Problem
The high cost and time-consuming process of storing and transporting large data volumes due to the mismatch between data processing speeds and storage/transportation costs, as well as network congestion issues, are exacerbated by outdated data representation and processing methods.
Innovation Solution
The system employs a Data Compiler (DC) to transform large data into a smaller, highly compressed kernel, which can be rapidly recalculated into the original form using a Turing Dedekind device (TD), eliminating the need for storing and transporting large data sets, and leveraging multi-core architecture for efficient data recalculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large data is stored and transported using traditional methods, then data integrity is maintained, but storage cost and transportation time increase substantially
Solution Approach 1:
The patent extracts the essential information from large datasets by transforming them into compact mathematical representations (kernels). Instead of storing and transporting the entire dataset, only the kernel and transformation parameters are stored, which can be rapidly reconstructed back to the original data when needed.
Solution Approach 2:
The patent changes the representation parameters of data by applying mathematical transformations (such as Fourier transforms or other basis transformations) that convert data from its original form into a compressed spectral domain representation, reducing storage requirements while preserving reconstructability.
2Reliability
If large data is stored using traditional methods, then complete data is preserved, but storage cost increases
Solution Approach 1:
The patent extracts the essential information from large datasets by transforming them into compact mathematical representations (kernels). Instead of storing and transporting the entire dataset, only the kernel and transformation parameters are stored, which can be rapidly reconstructed back to the original data when needed.
Solution Approach 2:
The patent creates a mathematical model (kernel) that serves as a compact copy of the original data. This kernel contains sufficient information to reconstruct the original dataset, acting as a space-efficient representation that preserves data completeness.
3Productivity
If traditional data processing methods are used, then data is handled sequentially, but processing speed is limited
Solution Approach 1:
The patent segments the data processing task into two distinct phases: a preprocessing phase that creates the mathematical kernel representation, and a reconstruction phase that generates the original data from the kernel. This segmentation allows each phase to be optimized independently and enables parallel processing capabilities.
Solution Approach 2:
The patent performs preliminary action by pre-computing the mathematical transformation and storing the kernel representation before any actual data processing or transmission is needed. This preliminary transformation enables rapid reconstruction and processing later without repeating the computationally intensive transformation steps.
Data Source
AI summary
A system transmits a target data file as a set of mathematical functions and data values representative of the target data file to a receiver, the system comprising at least one hardware processor and memory storing computer instructions, the computer instructions when executed by the at least one hardware processor configured to cause the system to identify a target bit pattern of a target data file; generate a set of mathematical functions and data values operative to generate the target bit pattern; and transmit the set of mathematical functions and data values to a receiver, which can use the set of mathematical functions and data values to generate the target data file.