Sequence-Aligned File Compression Using Consensus Pointers
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Solution Overview
Problem
Existing data compression methods are inefficient in reducing storage and bandwidth requirements for large files, particularly in handling similar sequences and structures within data.
Innovation Solution
A compression engine that utilizes multiple sequence alignment to split files into sequences, insert gaps for alignment, and represent the file as a list of pointer pairs to a consensus sequence, allowing for efficient compression and reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional compression methods are used, then compression is simpler to implement, but compression ratio and efficiency are insufficient for large files with similar sequences
Solution Approach 1:
The patent segments the input file into multiple sequences of fixed length, treating each sequence as an independent unit for compression. This segmentation allows the algorithm to identify and compress similar patterns across different parts of the file, improving overall compression efficiency while maintaining manageable complexity through systematic processing of divided data units.
2Quantity of substance
If sequence alignment is performed to compress similar sequences, then storage space and bandwidth are reduced, but computation time and processing complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing the input file into fixed-length sequences and creating an index structure before compression. This preliminary organization of data enables faster alignment and comparison operations during compression, reducing computation time while achieving effective storage space reduction through subsequent sequence matching.
3Productivity
If files are split into multiple sequences for alignment, then compression ratio improves, but reconstruction complexity increases
Solution Approach 1:
The patent creates a consensus sequence that serves as a compressed representation (copy) of the original data patterns. During reconstruction, the original file is regenerated by copying and assembling segments from the consensus sequence according to stored pointers, simplifying the reconstruction process while maintaining high compression ratios through efficient use of the consensus representation.
Data Source
AI summary
Compressing files is disclosed. An input file to be compressed is first aligned. The alignment may be facilitated using the structure of the file. The file may include aspects such as format, rows, fields, or text, that can be used to create a warm start for the alignment operation. 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.