Neural Sourceblock Sizing for Adaptive Data Compaction
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Solution Overview
Problem
The rapid growth of data storage demand exceeds the capacity to store it, leading to bottlenecks in data transmission and security concerns as existing encryption technologies are challenged by quantum computing, with data compression methods failing to provide sufficient space savings or degrading data quality.
Innovation Solution
A system and method using artificial intelligence, specifically a neural network, to predict optimal block sizes for data encoding, allowing real-time adaptability and efficient data compaction by dynamically adjusting block sizes based on data patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is used to increase storage capacity, then storage efficiency is improved, but data quality is degraded or compression ratio is insufficient
Solution Approach 1:
The patent applies dynamics by making the sourceblock size variable rather than fixed. The system dynamically adjusts the sourceblock size based on the characteristics of the data being processed, allowing optimal compression for different data types and patterns while maintaining data integrity. This is achieved through a feedback mechanism that analyzes data properties and adapts the encoding parameters in real-time.
Solution Approach 2:
The patent changes the parameter of sourceblock size from a static value to a dynamic variable. By modifying this parameter based on data characteristics (such as redundancy levels, pattern recognition results), the system achieves better compression ratios for different data types without degrading quality. The parameter adjustment is guided by performance metrics and data analysis.
2Ease of manufacture
If fixed sourceblock size is used for data encoding, then processing simplicity is improved, but storage efficiency and adaptability are reduced
Solution Approach 1:
The system transitions from static to dynamic sourceblock sizing while maintaining processing efficiency. The sourceblock size is adjusted dynamically based on data characteristics, allowing the system to adapt to different compression opportunities without significantly increasing processing complexity. The adaptation is driven by automated analysis of data patterns.
Solution Approach 2:
The encoding system performs self-optimization by automatically analyzing its own performance and the characteristics of incoming data. The system monitors compression effectiveness and adjusts sourceblock sizes autonomously based on observed data patterns, eliminating the need for manual configuration while maintaining high storage efficiency.
3Quantity of substance
If larger sourceblock size is used, then compression ratio is improved, but processing time and memory usage increase
Solution Approach 1:
The system dynamically adjusts sourceblock size to balance compression ratio and processing time. Rather than using consistently large blocks, the system selects optimal block sizes based on data characteristics, achieving good compression while avoiding excessive processing delays. The block size varies adaptively to match the local structure of the data.
Solution Approach 2:
The system applies compression selectively rather than uniformly across all data. By identifying regions with high redundancy or compressibility, the system applies larger sourceblocks only where beneficial, while using smaller blocks in regions where compression opportunities are limited, thus optimizing the trade-off between compression ratio and processing time.
4Speed
If data transmission bandwidth is increased to handle large data sets, then transmission speed is improved, but system cost and energy consumption increase
Solution Approach 1:
The system extracts and removes redundant information from data before transmission. By identifying and eliminating duplicate or unnecessary data elements through intelligent encoding, the system reduces the amount of data that needs to be transmitted, thereby reducing bandwidth requirements while maintaining transmission speed.
Solution Approach 2:
The system changes the parameter of data representation by encoding information more efficiently. Through optimized encoding schemes that adapt to data characteristics, the system transmits the same information using fewer bits, reducing bandwidth consumption without sacrificing transmission speed or data fidelity.
Data Source
AI summary
A system and method for data compaction optimization which leverages a neural network to predict optimal block sizes for data encoding, enhancing efficiency and adaptability in various applications. It begins with data preprocessing, extracting features, and creating labeled datasets for training. The neural network architecture is carefully designed, allowing it to learn complex relationships between data characteristics and optimal block sizes. During training, the network is fine-tuned and optimized using appropriate loss functions and regularization techniques. Once deployed, it continuously monitors incoming data streams for shifts in data patterns and adapts predictions accordingly. By predicting multiple block sizes, the system accommodates diverse compression needs. This versatile system offers real-time adaptability, ensuring optimal encoding performance as data patterns evolve over time.