Data Kernel Compression for Large-Scale File Re-Transformation
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Solution Overview
Problem
Current data storage and transportation methods are inefficient due to the high cost and time required for handling large data volumes, which limits the effectiveness of data processing speed improvements and hinders the utilization of multi-core architecture.
Innovation Solution
The system employs a Data Compiler (DC) to transform large data sets into a smaller 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
1Loss of time
If large data sets are stored and transported using conventional methods, then data availability is maintained, but storage costs and transportation time increase substantially
Solution Approach 1:
The patent extracts only the essential pattern information from large data sets, separating the core computational pattern from the raw data volume. This allows the system to work with a small kernel representation rather than transporting entire data sets, dramatically reducing transportation time while maintaining data availability through pattern recalculation.
Solution Approach 2:
Instead of copying and transporting actual large data sets, the system creates a compressed kernel representation that can be rapidly recopied and expanded back to the original data form when needed. This virtual copying mechanism eliminates the need for physical data transportation while preserving data accessibility.
2Productivity
If data processing speed is improved through faster processors, then computation efficiency increases, but the bottleneck of data storage and transportation costs remains
Solution Approach 1:
The system performs preliminary pattern analysis and kernel extraction during data ingestion, preparing a compressed representation in advance. This preliminary action ensures that when data needs to be accessed or processed, only the small kernel needs to be manipulated rather than moving large data sets, eliminating the ongoing storage and transportation cost bottleneck.
Solution Approach 2:
The patent fundamentally changes the parameter of data representation from full-resolution data sets to compressed kernel patterns. This parameter change transforms the problem from one requiring expensive storage and transportation of large volumes to one where small kernels can be rapidly expanded and processed, making processing speed improvements effective without incurring storage/transportation costs.
3Productivity
If multi-core architecture is utilized for parallel processing, then computation throughput increases, but effectiveness is limited by data storage and transportation overhead
Solution Approach 1:
The patent segments the data management task into two distinct phases: kernel extraction (compression) and kernel expansion (decompression). This segmentation allows multi-core architectures to efficiently handle parallel kernel expansions without the complexity of managing large data set distributions across cores, as each core works independently with its own kernel representation.
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.