Adaptive Genomic Data Encoding With Dynamic Sourceblock Sizing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The rapid growth of data storage demand, particularly in genomic data science, has outpaced the capacity to store and transmit data efficiently, leading to bandwidth limitations and security concerns, especially with the advent of quantum computing and the need for adaptive compression methods that maintain data integrity and privacy.
Innovation Solution
A system and method for bandwidth-efficient data encoding using a sequence analyzer to deconstruct genomic data into sourceblocks, assign reference codes, and dynamically adjust sourceblock sizes based on dataset characteristics, incorporating machine learning for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional data compression methods are used, then storage capacity is doubled, but compression ratio decreases substantially for multi-media data and results in data degradation
Solution Approach 1:
The patent segments genomic data into fixed-size blocks and processes each block independently through cryptographic transformation. This segmentation allows the system to achieve high compression ratios for structured genomic data while maintaining data integrity through reversible encryption operations on each segment.
Solution Approach 2:
The system dynamically adjusts block size parameters based on the specific characteristics of genomic datasets. By optimizing block size and cryptographic operation parameters for genomic data patterns, the system achieves superior compression ratios compared to traditional methods while preserving complete data fidelity through lossless encryption.
2Quantity of substance
If physical storage capacity is increased, then storage demand is met temporarily, but storage demand continues to outpace manufacturing capacity
Solution Approach 1:
The patent extracts and eliminates redundant information from genomic datasets by identifying and removing repetitive sequences through cryptographic hashing. This extraction process reduces the volume of data that needs to be stored and transmitted while preserving all essential genetic information, directly addressing the mismatch between storage demand and manufacturing capacity.
Solution Approach 2:
The system creates compact cryptographic representations (hashes) of genomic data blocks that serve as efficient copies for storage and transmission. These cryptographic copies maintain full data fidelity while occupying minimal storage space, enabling the system to handle exponentially growing data volumes without proportionally increasing physical storage requirements.
3Reliability
If data is encrypted for security, then data privacy is protected, but transmission bandwidth requirements increase
Solution Approach 1:
The patent segments genomic data into fixed-size blocks before applying cryptographic transformations. This segmentation enables the system to process and encrypt only the essential data elements, generating compact cryptographic representations that maintain security while reducing overall data size and transmission bandwidth requirements.
Solution Approach 2:
The system optimizes cryptographic operation parameters specifically for genomic data characteristics, using variable block sizes and hashing strategies adapted to genomic patterns. This parameter optimization achieves robust data security through encryption while minimizing the size of encrypted output, thereby reducing transmission bandwidth requirements.
4Device complexity
If fixed-size blocks are used for cryptographic operations, then processing is simplified, but adaptability to different genomic data types is reduced
Solution Approach 1:
The patent implements dynamic block size selection that adapts to the specific characteristics of different genomic datasets. The system can adjust block sizes based on sequence complexity, alphabet size, and frequency distribution, allowing optimal compression efficiency across diverse genomic data types while maintaining manageable processing complexity through structured algorithms.
Solution Approach 2:
The system dynamically changes cryptographic operation parameters including block size, hashing strategy, and processing depth based on genomic data characteristics. This parameter adaptability enables the system to achieve optimal compression efficiency for different genomic data types (whole genomes, gene families, mixed regions) while keeping processing complexity controlled through systematic parameter adjustment.
Data Source
AI summary
A system and methods for adaptive bandwidth-efficient data encoding comprising: a sequence analyzer configured to analyze a received sequence dataset, maintain a count of unique characters, and identify positions where the unique character count increases by a power of two; an adaptive sourceblock optimizer that determines and dynamically adjusts optimal sourceblock sizes based on dataset characteristics; and a data deconstruction engine that deconstructs the dataset into sourceblocks and creates codewords for storage or transmission. The system analyzes sequence complexity, alphabet size, and character frequency distribution to optimize sourceblock sizes, and uses machine learning to improve decision-making over time. This adaptive approach enhances compression efficiency across varied genomic data types, including genome graphs, while maintaining data integrity and security. The system efficiently encodes, stores, and transmits complex genomic and bioinformatic datasets, addressing the growing challenges in data storage and bandwidth limitations.