Adaptive Codebook Compression for Data Drift in Storage Sync
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Solution Overview
Problem
The rapid growth of data storage demand, exceeding the capacity to store it, leads to a bottleneck in data storage and transmission, with existing solutions like data compression and additional physical storage capacity being insufficient to meet the growing need.
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 chunklets and updated codebooks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If additional physical storage capacity is added, then storage demand is temporarily met, but storage demand continues to outstrip global manufacturing capacity
Solution Approach 1:
The patent transforms physical storage capacity into virtual storage capacity through compression algorithms. By changing the parameter from physical space to information-theoretic compression ratios, the system achieves effective storage expansion without physical manufacturing. The codebook-based compression dynamically adapts to data patterns, achieving compression ratios that effectively multiply storage capacity beyond physical limits.
Solution Approach 2:
The patent creates virtual copies of storage capacity through compression. Instead of physically duplicating storage devices, the system uses compression algorithms to create multiple logical storage spaces from a single physical medium. The codebook acts as a virtual layer that multiplies the effective storage capacity without physical replication.
2Quantity of substance
If data compression is applied, then storage capacity is doubled, but compression ratios decrease for multi-media data and data degradation occurs
Solution Approach 1:
The patent dynamically adjusts compression parameters based on data type and patterns. The codebook learning process adapts compression ratios to match the specific characteristics of the data being stored, achieving high compression for repetitive patterns while maintaining fidelity for unique data. This parameter adaptation resolves the contradiction by making compression effectiveness data-dependent rather than fixed.
Solution Approach 2:
The system implements feedback through codebook learning and updating. The compression algorithm continuously learns from the data patterns it encounters, adjusting the codebook to optimize compression ratios while maintaining data integrity. This feedback mechanism ensures that compression effectiveness improves over time without sacrificing data quality, as the system adapts to the actual data being compressed.
3Quantity of substance
If larger datasets are transmitted, then more data can be transferred, but transmission bandwidth becomes a bottleneck
Solution Approach 1:
The patent changes the fundamental parameter of data representation from raw bytes to compressed codebook references. By transforming the data format using learned compression models, the system reduces the transmission volume parameter without affecting the information content. This allows larger effective data volumes to be transmitted at the same physical bandwidth, or the same data volume to be transmitted faster.
4Reliability
If existing encryption technologies are used, then data security is maintained, but security becomes vulnerable to quantum computing
Solution Approach 1:
The patent introduces compression as an intermediary layer between data and encryption. The compression process transforms data into a different representation space before encryption, creating a layered security model. This intermediary transformation makes the system more adaptable to future cryptographic challenges, as the compressed representation can be encrypted with post-quantum algorithms while maintaining the security benefits of both compression and encryption.
Data Source
AI summary
A system and method for data storage, transfer, synchronization, and security using automated system efficacy monitoring and model 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 chunklets. The new data chunklets 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.