Repeating-Bit Data Compression for Low-Latency Scientific Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional data compression methods are inefficient for high-performance applications as they require significant time for statistical analysis and pattern recognition, leading to high latency in data transfer and storage, which is critical in real-time scenarios like network transfer, disk storage, and memory storage.
Innovation Solution
A method that exploits existing repetitive interleaved patterns within scientific data, such as floating point and integer numbers, and image data, to perform fast compression and decompression by identifying and representing these patterns in a space-saving format, reducing the need for extensive analysis and directory creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression methods (dictionary-based or statistical) are used, then data size is reduced, but compression speed decreases due to extensive analysis time
Solution Approach 1:
The patent segments the data processing approach by dividing data into fixed-size blocks and processing each block independently through a series of deterministic transformation steps (bit-reversal, rotation, XOR operations). This eliminates the need for global statistical analysis while maintaining compression effectiveness on localized patterns.
Solution Approach 2:
The patent performs preliminary deterministic transformations on data blocks (bit-reversal, rotation, XOR with key values) before compression encoding. These pre-processing steps organize the data in ways that reveal compressible patterns without requiring time-consuming statistical analysis, enabling faster subsequent compression.
2Quantity of substance
If statistical analysis and pattern recognition are performed to achieve compression, then compression ratio improves, but latency increases
Solution Approach 1:
The patent replaces the mechanical/statistical analysis system with a deterministic transformation system. Instead of performing statistical analysis to identify patterns, the system applies predetermined mathematical transformations (bit-reversal, rotation, XOR) that deterministically reveal compressible structures, eliminating analysis time while maintaining compression effectiveness.
Solution Approach 2:
The patent changes the parameters of data representation through deterministic transformations (reversing bit order, rotating bit positions, XORing with key values). These parameter changes reorganize the data to expose repeating patterns that can be compressed efficiently without requiring statistical analysis of the original data configuration.
3Productivity
If fast compression is implemented without analysis time, then compression speed improves, but compression effectiveness decreases
Solution Approach 1:
The patent maintains continuous useful action by applying a sequence of deterministic transformations (bit-reversal, rotation, XOR operations) that each contribute to revealing compressible patterns. This continuous transformation process ensures that compression effectiveness is maintained throughout the fast processing pipeline without gaps for statistical analysis.
Solution Approach 2:
The patent uses a composite approach combining multiple deterministic transformation techniques (bit-reversal permutation, bit rotation, XOR operations with key values) applied in sequence. This composite transformation strategy achieves compression effectiveness comparable to statistical methods while maintaining the speed required for real-time applications.
Data Source
AI summary
A method and system allows for fast compression and decompressing of data using existing repetitive interleaved patterns within scientific data (floating point, integer, and image). An advantage of the method and system is that it is so fast that it can be used to save time due to a lower amount of data transferred/stored in scenarios like network transfer, disk or memory storage, cache storage or any other real-time applications where time plays a crucial role.