Consensus Sequence Alignment for Compressed File Updates
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Solution Overview
Problem
Existing data compression methods face inefficiencies when dealing with large files and additional content, leading to high computational complexity and suboptimal compression ratios, especially when updating compressed files with new data.
Innovation Solution
A compression engine that utilizes multiple sequence alignment to split files into aligned sequences, generating a consensus sequence and pointer pairs, allowing efficient compression and decompression of files, including updates with reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional compression methods are used on large files, then compression is achieved, but computational complexity increases significantly when updating with new content
Solution Approach 1:
The patent divides the compressed file into multiple sequences (e.g., sequence1, sequence2, sequence3) that can be independently aligned and updated. When new content is added, only the affected sequences need to be reprocessed rather than the entire file, significantly reducing computational complexity for updates while maintaining the ability to handle large files effectively
Solution Approach 2:
The patent pre-aligns the file content into multiple sequences and creates a consensus sequence in advance. This preliminary structuring allows for efficient updates by simply aligning new content with the existing consensus sequence and pointer pairs, avoiding the need for complete re-compression and reducing computational overhead when adding new data
2Productivity
If traditional compression methods are used, then files are compressed, but compression ratios become suboptimal when dealing with large files and updates
Solution Approach 1:
The patent transitions from traditional linear compression to a multi-dimensional approach by creating multiple sequences and a consensus sequence structure. This allows the system to maintain optimal compression ratios for large files by distributing information across multiple aligned sequences, enabling both efficient compression and scalable updates without sacrificing compression effectiveness
3Adaptability or versatility
If compressed files are updated with new content, then new data is added, but computational resources and time increase significantly
Solution Approach 1:
By segmenting the compressed file into independent sequences with pointer pairs referencing a consensus sequence, the patent enables selective updating. When new content is added, only the specific sequences containing or related to the new data need to be re-aligned and re-compressed, dramatically reducing the time required for updates compared to reprocessing the entire file
Solution Approach 2:
The patent uses pointer pairs that reference the consensus sequence, creating a form of indirect copying. When updating, the system can reference existing consensus sequences and pointer structures rather than recreating the entire compressed representation, reducing computational time and resources needed for updates while maintaining data integrity
Data Source
AI summary
Compressing files is disclosed. An input, which is associated with an original file and new content, is to be compressed. The input includes a consensus sequence of the original file and the new content. The new content is aligned based using the consensus sequence of the original file in order to generate a new consensus sequence that reflects both the original content and the new content. The compression engine generates a new compression matrix and a new consensus sequence. Using the new consensus sequence, pointer pairs are generated. Each pointer pair identifies a subsequence of the consensus matrix. The new compressed file includes the pointer pairs and the new consensus sequence.