Adaptive Data Compression Screening for Incompressible Blocks
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Solution Overview
Problem
Current data compression methods consume excessive computing resources, particularly CPU, as they either blindly compress all data blocks or skip compression, leading to inefficiencies and misjudgments, especially when dealing with incompressible data blocks.
Innovation Solution
A self-adaptive data compression scheme that selects sample bit strings from a data block to determine statistical characteristics, compressing only if these characteristics match predetermined criteria, thereby optimizing resource use and quickly assessing compressibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data compression is performed on all data blocks, then data storage efficiency is improved, but computing resource consumption increases
Solution Approach 1:
The patent applies preliminary action by performing a quick statistical analysis on sample data from each data block before committing to full compression. This preliminary check examines whether the data contains compressible patterns (repeated characters, low entropy) and only then proceeds with compression if the criteria are met, avoiding wasted computing resources on incompressible data
Solution Approach 2:
The patent changes the parameter of data representation by encoding data in different formats based on detected patterns. When compressible patterns are found, the data is transformed into a more compact representation using run-length encoding or dictionary-based methods, changing the structural parameters of the data to achieve compression
2Use of energy by moving object
If data compression is skipped for all data blocks, then computing resource consumption is reduced, but data storage efficiency deteriorates
Solution Approach 1:
The patent applies local quality by treating different data blocks differently based on their individual characteristics. Each data block is independently evaluated through statistical analysis of its sample data, and compression is applied only to those blocks that exhibit compressible patterns, while incompressible blocks are left unchanged. This localized approach optimizes the balance between storage efficiency and computing resource consumption
3Measurement precision
If statistical analysis is performed on entire data blocks, then compressibility detection accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing each data block into multiple sample segments for statistical analysis. Instead of analyzing the entire data block, the system extracts representative samples from different portions of the block and performs statistical analysis on these smaller segments. This segmented approach maintains adequate detection accuracy while significantly reducing processing time and resource consumption
Data Source
AI summary
A data compression technique involves: selecting, from a data block, a plurality of bit strings as sample data. The technique further involves: determining a set of characters included in the sample data. Each character in the set of characters is represented by at least one bit string among the plurality of bit strings. The technique further involves: compressing the data block if statistical characteristics of the set of characters match predetermined statistical characteristics. Accordingly, incompressible data can be filtered out efficiently and accurately, so as to utilize computing resources more efficiently.