Data Compression Search Modes for Ratio-Speed Tradeoffs
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Solution Overview
Problem
Existing data compression techniques face challenges in balancing compression ratio, processing speed, and computational resource usage, leading to inefficiencies in resource-constrained environments and inconsistent performance across diverse data types.
Innovation Solution
A data compression method that determines a type of search operation, such as near or far search, to generate compressed data blocks, optimizing compression efficiency and reducing overhead by indicating the search operation type in the compressed data blocks, and using an unused bit to identify the last block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high-compression algorithms are used, then compression ratio is improved, but processing speed deteriorates
Solution Approach 1:
The patent segments the search operation into two distinct types: near search and far search. This segmentation allows the system to apply different processing strategies for different data patterns, achieving both high compression ratios and fast processing speeds by matching the appropriate search type to the specific compression scenario.
Solution Approach 2:
The patent introduces dynamic selection between near search and far search operations based on data characteristics. The system can adaptively choose the appropriate search type during compression, allowing processing speed and compression ratio to be optimized dynamically rather than being fixed to a single algorithmic approach.
2Quantity of substance
If computationally intensive algorithms are used, then compression efficiency is improved, but computational resource usage worsens
Solution Approach 1:
The patent applies partial action by implementing near search as a lighter, faster operation for cases where high compression is not critical, while reserving far search for scenarios where maximum compression efficiency is required. This allows the system to use computational resources proportionally to the actual compression needs.
Solution Approach 2:
The patent changes the computational parameter by offering two distinct search operation types with different resource requirements. Near search uses fewer computational resources for faster processing, while far search uses more resources for higher compression efficiency, allowing the system to adjust resource usage based on operational requirements.
3Productivity
If fast compression algorithms are used, then processing speed is improved, but compression ratio deteriorates
Solution Approach 1:
The patent segments the compression algorithm into two operational modes: near search mode for speed-critical applications and far search mode for compression-critical applications. This segmentation resolves the contradiction by allowing the system to select the appropriate mode based on the specific performance requirements of each compression task.
Solution Approach 2:
The patent creates a universal compression system that can perform both near search and far search operations, making it adaptable to different performance requirements. The same compression apparatus can serve both speed-optimized and ratio-optimized scenarios, eliminating the need for separate specialized algorithms.
4Adaptability or versatility
If existing compression techniques are used, then compatibility is maintained, but adaptability to diverse data types worsens
Solution Approach 1:
The patent segments the search operation into near and far types, allowing the system to adapt to different data patterns without requiring completely different algorithms for each data type. This segmentation provides versatility while maintaining manageable system complexity through a unified dual-mode approach.
Solution Approach 2:
The patent introduces dynamic adaptability by allowing the compression system to switch between near search and far search based on data characteristics. This dynamic behavior enables the system to adapt to diverse data types automatically without requiring manual configuration or complex decision logic.
Data Source
AI summary
Provided is a method and apparatus for compressing data. The method according to an embodiment includes receiving a data input string comprising one or more segments, performing a search operation on the data input string, determining a type of the search operation, wherein the type of the search operation comprises a near search operation or a far search operation, and generating one or more compressed data blocks corresponding to the one or more segments of the data input string, based on the determined type of the search operation.