Spectral Key Mapping for File Grouping and Compression
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Solution Overview
Problem
Current data compression techniques fail to recognize similarities in data files, leading to inefficient storage and transmission, and lack the ability to organize and identify unstructured digital data effectively, especially in compliance and business intelligence contexts.
Innovation Solution
The method involves mapping files into a multi-dimensional space based on their digital spectrum, allowing for the grouping and differentiation of files by identifying highly occurring patterns and replacing them with new symbols, thereby achieving better compression ratios and optimizing data storage and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional compression techniques are used, then storage capacity is preserved and bandwidth is reduced, but similarity recognition between data files is lost and data organization capability deteriorates
Solution Approach 1:
The patent segments data files into fixed-size blocks and generates spectral keys for each block, allowing individual analysis of data patterns while maintaining overall file compression. This segmentation enables similarity detection at the block level without compromising storage efficiency.
Solution Approach 2:
The patent introduces spectral keys as an intermediary representation between original data and compressed data. These keys capture essential patterns and similarities in data blocks while being more compact, enabling both compression and similarity recognition functions simultaneously.
2Quantity of substance
If block level operations are applied, then storage efficiency is improved through de-duplication, but the ability to interpret and organize unstructured data deteriorates
Solution Approach 1:
The patent transforms data blocks into spectral representations that highlight distinctive patterns and characteristics, analogous to color changes making objects distinguishable. This transformation enables intuitive organization and interpretation of unstructured data while maintaining compression efficiency.
Solution Approach 2:
The patent maps data blocks into a spectral domain (another dimension), where patterns and similarities become more apparent. This dimensional transformation allows for better data organization and interpretation beyond simple block-level operations.
3Ease of operation
If search engines are used to organize data, then data accessibility is improved, but the ability to eliminate duplication and migrate redundancies deteriorates
Solution Approach 1:
The patent merges the functions of search engines and de-duplication systems by using spectral keys for both data organization and similarity detection. This unified approach maintains data accessibility while effectively identifying and eliminating duplicates.
Solution Approach 2:
The patent creates a universal data representation (spectral keys) that serves multiple functions: organization, search, similarity detection, and de-duplication. This multi-functional approach replaces separate systems with a single efficient mechanism.
Data Source
AI summary
Methods and apparatus teach a digital spectrum of a file. The digital spectrum is used to map a file's position in a multi-dimensional space. This position relative to another file's position reveals distances between the files. Closest files can be grouped together. When contemplating voluminous numbers of files for digital spectrums, various methods include: concatenating all such files together to get a single key useful for creating a file's spectrum; or compressing files individually and combining their collective dictionaries into a single dictionary with or without the use of tree mechanisms that defines the digital spectrum. Each provides advantage over the other. The latter consumes considerably less run time because each compression event can be distributed to a separate processor. Method two provides better spectrums because it is more “informationally” valid than is method one.