Sequence-Aligned File Compression With Consensus Pointer Mapping
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Solution Overview
Problem
Existing data compression methods are inefficient in reducing storage and bandwidth requirements, particularly for large files, and often require significant computational resources.
Innovation Solution
A compression engine that utilizes multiple sequence alignment to split files into aligned sequences, generate a consensus sequence, and represent the original file using pointer pairs to the consensus sequence, allowing for efficient compression and decompression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression methods are used, then compression is achieved, but storage and bandwidth requirements are not sufficiently reduced for large files
Solution Approach 1:
The input file is divided into multiple sequences of equal length, which are then aligned vertically to form a compression matrix. This segmentation allows the compression algorithm to process and identify patterns across multiple data streams simultaneously, improving both compression ratio and efficiency for large files.
Solution Approach 2:
The patent transforms the traditional one-dimensional compression approach into a multi-dimensional problem by arranging sequences in a matrix format with rows and columns. This dimensional transformation enables the algorithm to exploit patterns across multiple sequences concurrently, achieving better compression ratios while maintaining productivity.
2Quantity of substance
If compression algorithms process large files, then compression ratio improves, but computational resources increase significantly
Solution Approach 1:
By segmenting the file into equal-length sequences and processing them in a structured matrix format, the algorithm achieves better compression ratios through pattern recognition across sequences while maintaining manageable computational complexity through systematic organization.
Solution Approach 2:
The patent changes the fundamental parameter of data organization from linear to matrix-based, allowing parallel processing of multiple sequences. This parameter change enables the system to achieve higher compression ratios without proportionally increasing computational resource consumption.
3Volume of stationary object
If data is compressed, then storage space is reduced, but decompression complexity increases
Solution Approach 1:
The patent creates a consensus sequence that serves as a compressed representation of the original data. During decompression, the original file can be reconstructed by copying and assembling subsequences from the consensus sequence according to stored pointers, simplifying the decompression process while maintaining high compression ratios.
4Productivity
If multiple sequence alignment is used, then compression efficiency improves, but algorithm complexity increases
Solution Approach 1:
The algorithm segments the input file into equal-length sequences and organizes them in a matrix, creating a structured framework that improves compression efficiency through systematic pattern recognition while keeping the algorithm manageable through clear organizational rules.
Solution Approach 2:
By changing the data organization parameter from linear to matrix-based with specific row and column relationships, the algorithm achieves superior compression efficiency while maintaining controlled complexity through the systematic structure imposed by the matrix arrangement.
Data Source
AI summary
Compressing files is disclosed. An input file to be compressed is first aligned. Aligning the file includes splitting the file into sequences that can be aligned. The result is a compression matrix, where each row of the matrix corresponds to part of the file. A consensus sequence id determined from the compression matrix. Using the consensus sequence, pointer pairs are generated. Each pointer pair identifies a subsequence of the consensus matrix. The compressed file includes the pointer pairs and the consensus sequence.