Sequence Alignment Compression for Multi-Dimensional Files
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Solution Overview
Problem
Existing data compression technologies face challenges in efficiently compressing large files and multi-dimensional data, as they struggle to effectively align and represent diverse patterns across multiple dimensions.
Innovation Solution
The proposed solution involves a compression engine that uses multiple sequence alignment to split files into aligned sequences, generating a consensus sequence and representing the original file as pointer pairs into this sequence, allowing for efficient compression and reconstruction of files across multiple dimensions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing data compression technologies are used on large files and multi-dimensional data, then compression is achieved, but the technologies struggle to effectively align and represent diverse patterns across multiple dimensions
Solution Approach 1:
The patent segments multi-dimensional data into multiple one-dimensional sequences that can be independently processed through multiple sequence alignment. By dividing the complex multi-dimensional compression problem into separate sequential alignments, the system can handle diverse patterns across multiple dimensions while using established 1D alignment algorithms, thus improving adaptability without proportionally increasing complexity.
Solution Approach 2:
The patent transforms multi-dimensional data into a series of one-dimensional sequences for processing. By projecting multi-dimensional patterns into multiple 1D dimensions that can be aligned separately, the system achieves the ability to represent diverse multi-dimensional patterns while avoiding the exponential complexity that would arise from direct multi-dimensional alignment approaches.
2Quantity of substance
If multiple sequence alignment is used to compress files, then storage space is reduced, but computation time increases
Solution Approach 1:
The patent divides the file into multiple sequences that are aligned independently rather than performing a single comprehensive alignment on all data. This segmentation allows parallel processing of multiple alignment operations, reducing overall computation time while achieving significant storage compression through the consolidated consensus sequence representation.
Solution Approach 2:
The patent applies multiple sequence alignment to subsets of data sequences rather than attempting to align all data in a single pass. By processing sequences in manageable groups and building up the compression representation incrementally, the system achieves effective compression while keeping computation time manageable through controlled partial action.
Data Source
AI summary
Compressing files is disclosed. An input file to be compressed is first aligned. When the file has multiple axes or dimensions, the file is aligned along each of the axes. Aligning the file includes splitting the file into sequences that can be aligned along each of the axes or dimensions. Aligning the file generates a compression tensor, where each row or dimensional space of the compression tensor corresponds to part of the file. A consensus tensor is determined from the compression tensor. Using the consensus tensor, pointer lists are generated. Each pointer lists identifies a subsequence or portion of the consensus tensor. The compressed file includes the pointer lists and the consensus tensor.