Hybrid Data Compression with Predictor-Based Block Selection
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Solution Overview
Problem
Current data compression and decompression methods in computer systems and communication networks face inefficiencies due to reliance on single compression schemes, which do not guarantee robust compressibility across various data types and value locality properties, leading to increased latency and complexity.
Innovation Solution
The implementation of hybrid data compression and decompression systems that combine multiple compression methods, using a predictor to select the best suited compression scheme based on the dominating data type of a block, allowing for dynamic adjustment at runtime if necessary, to optimize compressibility and reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single compression scheme is used, then the system complexity is reduced, but the compressibility across various data types deteriorates
Solution Approach 1:
The system dynamically selects the most appropriate compression scheme based on the characteristics of the input data block. A predictor mechanism analyzes data type patterns and value locality properties to determine whether to apply statistical encoding, delta encoding, or other compression methods, allowing the system to adapt to different data types rather than using a fixed single scheme
Solution Approach 2:
The system changes the compression parameter (selection of compression scheme) based on data characteristics. By monitoring data type distributions and value locality metrics, the system adjusts which compression algorithm is applied to maximize compressibility for the specific data being processed
2Adaptability or versatility
If multiple compression schemes are combined, then the compressibility across various data types is improved, but the device complexity increases
Solution Approach 1:
The system segments the compression task by dividing it into distinct processing paths for different data types. Each compression scheme is applied to specific segments of data that match its optimal characteristics, rather than attempting to apply all schemes to all data. This segmentation reduces the effective complexity by activating only the necessary compression path for each data block
Solution Approach 2:
A predictor mechanism serves as an intermediary between the data input and the multiple compression schemes. This intermediary analyzes data characteristics and routes the data to the most appropriate compression scheme, managing the complexity of having multiple schemes by providing a centralized decision-making layer that simplifies the overall system architecture
3Adaptability or versatility
If a predictor mechanism is added to select compression schemes, then the compressibility is enhanced, but the processing latency increases
Solution Approach 1:
The predictor mechanism performs preliminary analysis of data characteristics before the actual compression process. By examining data type patterns and value locality properties in advance, the system determines the optimal compression scheme ahead of time, allowing the compression operation itself to proceed efficiently without repeated decision-making during the compression process
Solution Approach 2:
The predictor mechanism is integrated into the compression system and uses the data itself to make selection decisions. The data's own characteristics (data types, value locality) serve as the basis for prediction, eliminating the need for external or complex analysis tools, thereby reducing the overhead time added by the prediction process
Data Source
AI summary
Methods, devices and systems enhance compression and decompression of data blocks of data values by selecting the best suited compression method and device among two or a plurality of compression methods and devices, which are combined together and which said compression methods and devices compress effectively data values of particular data types; said best suited compression method and device is selected using as main selection criterion the dominating data type in a data block by predicting the data types within said data block.