Adaptive Codebook Retraining for Data Drift and Dense Compression
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Solution Overview
Problem
The rapid growth of data storage demand, exceeding global manufacturing capacity, and the limitations of data compression and transmission bandwidth pose significant challenges in efficiently storing and transmitting data.
Innovation Solution
A system and method for data storage, transfer, synchronization, and security using automated system efficacy monitoring and model training, where statistical analyses of test datasets determine if the probability distribution of two datasets is within a pre-determined range, allowing for the retraining of encoding and decoding algorithms to produce new data sourceblocks and updated codebooks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If additional physical storage capacity is added to meet growing data storage demand, then storage capacity increases, but manufacturing capacity limitations prevent solving the problem at scale
Solution Approach 1:
The patent applies composite materials by combining multiple compression techniques (lossless compression, lossy compression, and dictionary-based compression) into a hybrid storage system. This composite approach achieves superior space efficiency compared to individual methods, enabling dramatically increased effective storage capacity without requiring proportional increases in physical manufacturing capacity
Solution Approach 2:
The system dynamically adjusts compression parameters and algorithms based on data characteristics, transitioning between different compression modes (lossless/lossy) and updating codebooks adaptively. This allows the storage system to optimize space utilization for different data types and access patterns, maximizing the effective capacity of existing physical storage resources
2Quantity of substance
If data compression is applied to increase storage capacity, then storage efficiency improves, but compression ratios are limited and data degradation occurs with lossy compression
Solution Approach 1:
The patent employs a composite compression strategy that combines lossless compression for critical data portions with lossy compression for less critical portions, achieving much higher overall compression ratios while maintaining acceptable data quality. The system selectively applies different compression levels based on data importance and access requirements
Solution Approach 2:
The compression system is dynamic and adaptive, adjusting compression aggressiveness based on data characteristics, access patterns, and quality requirements. The codebook is continuously updated and refined based on actual usage, allowing the system to optimize the balance between compression ratio and data quality over time rather than using static compression parameters
3Productivity
If data is transmitted across networks, then data access and sharing improve, but transmission bandwidth becomes a bottleneck for large datasets
Solution Approach 1:
The system performs preliminary compression of data before transmission, creating a compact representation that requires significantly less bandwidth. By pre-compressing data using the dictionary-based codebook system, the amount of data that needs to be transmitted across networks is dramatically reduced, eliminating bandwidth bottlenecks while maintaining fast access through efficient decompression
Solution Approach 2:
The patent creates compressed copies of data that can be transmitted and stored efficiently. These compressed representations serve as portable, space-efficient copies that can be rapidly transmitted across networks and only require minimal computational resources to decompress when needed, enabling efficient data sharing without consuming excessive bandwidth
4Reliability
If existing encryption technologies are used to secure data, then data security is maintained, but quantum computing advances threaten to break current encryption methods
Solution Approach 1:
The patent replaces traditional mathematical encryption mechanisms with a physics-based approach using DNA storage and retrieval. By encoding data into DNA sequences and using biological processes for storage and access, the system creates encryption and security mechanisms that are fundamentally different from and resistant to quantum computing attacks, as they rely on biological rather than mathematical complexity
Data Source
AI summary
A system and method for lossy precompression for data compaction using automated model monitoring and training, wherein statistical analyses of test datasets are used to determine if the probability distribution of two datasets are within a pre-determined range, and responsive to that determination new encoding and decoding algorithms may be retrained in order to produce new data sourceblocks, and pre-compression of data prior to processing and statistical analysis allows for the compaction of already compressed data into highly dense formats. The new data sourceblocks may then be processed and assigned new codewords which are compiled into an updated codebook which may be distributed back to encoding and decoding systems and devices.