Entropy-Sorted Data Chunks for Higher Compression Efficiency
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Solution Overview
Problem
Data compression into random chunkfiles leads to inefficient space utilization, compression, and computational resource waste in data platforms, as existing methods do not effectively utilize entropy metrics to optimize chunkfile creation and management.
Innovation Solution
Applying entropy metrics to determine and reorder data chunks based on their entropy values, allowing for improved compression algorithms to achieve greater compression efficiency by positioning chunks with similar entropy together, thereby reducing storage space and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data chunks are compressed into random chunkfiles without entropy-based reordering, then the chunkfile creation process is simple and fast, but storage space utilization is inefficient and compression ratios are poor
Solution Approach 1:
The patent applies preliminary action by calculating entropy values for each data chunk before compression and reordering chunks based on these pre-calculated entropy values. This pre-processing step groups chunks with similar entropy characteristics together, enabling the compression algorithm to achieve better compression ratios by exploiting patterns within similar-entropy groups, thereby improving storage space utilization without requiring complex real-time decision-making during compression
Solution Approach 2:
The patent changes the organizational parameter of chunkfiles from random ordering to entropy-based ordering. By using entropy value as the sorting criterion instead of random or sequential ordering, the system transforms the chunkfile structure to enable better compression efficiency. This parameter change allows the compression algorithm to encounter more predictable patterns, improving the compression ratio and storage space utilization
2Loss of energy
If data chunks are reordered by entropy values before compression, then compression efficiency and storage reduction are improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies partial action by calculating entropy values only for the purpose of reordering chunks, without performing full compression analysis on each chunk. The entropy calculation serves as a lightweight preprocessing step that provides sufficient information for reordering without the computational overhead of complete compression evaluation, thus balancing the trade-off between compression efficiency improvement and additional processing requirements
Solution Approach 2:
The patent substitutes the traditional mechanical approach of random or sequential chunk ordering with an entropy-based sorting mechanism. This substitution replaces brute-force or simple sequential processing with a more intelligent ordering system that uses entropy metrics to guide the reorganization, achieving better compression efficiency while keeping the additional computational overhead manageable through efficient entropy calculation algorithms
3Productivity
If entropy metrics are calculated and applied to reorganize data chunks, then compression algorithms achieve greater compression rates, but the complexity of the compression process increases
Solution Approach 1:
The patent applies segmentation by dividing the compression process into distinct stages: entropy calculation stage, reordering stage based on entropy values, and compression stage. This segmentation allows each stage to be optimized independently, with the entropy-based reordering serving as a bridge that prepares the data for more effective compression without requiring the entire process to be re-engineered, thus improving compression rates while managing overall process complexity
Data Source
AI summary
Techniques are described for creating more efficient chunkfiles through the use of entropy metrics. In some examples, processing circuitry may determine an entropy value for each of a plurality of data chunks to obtain a corresponding plurality of entropy values. In some examples, processing circuitry may reorganize, based on the corresponding plurality of entropy values, the plurality of data chunks to obtain a reorganized plurality of data chunks. In some examples, processing circuitry may compress the reorganized plurality of data chunks to obtain a compressed chunkfile. In some examples, processing circuitry may store the compressed chunkfile superseding the plurality of data chunks.