Content-Based Data Compression for Mixed Block Encoding
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Solution Overview
Problem
Existing data compression techniques often fail to optimize compression by applying a single method to an entire data set, missing opportunities for more effective reduction in size by using different techniques for varying data portions.
Innovation Solution
A method and system for content-based dynamic data compression that involves a training stage to extract statistical features from data sets, an installation stage to set up the compression parameters, a working stage where individual data blocks are compressed using the most effective technique based on statistical features, and a consumption stage for decompression, allowing for mixed compression techniques across a data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single compression technique is applied to the entire data set, then the compression process is simple and fast, but the compression ratio is suboptimal because it cannot adapt to varying data portions
Solution Approach 1:
The patent divides the data set into multiple data blocks and applies different compression techniques to different blocks based on their characteristics. This segmentation allows the system to optimize compression ratio for each block individually while maintaining manageable complexity through automated technique selection.
Solution Approach 2:
The patent implements dynamic compression by automatically selecting the most appropriate compression technique for each data block based on statistical analysis of the data characteristics. This dynamic approach enables the system to adapt to varying data portions and achieve optimal compression ratios without manual intervention.
2Productivity
If different compression techniques are used for different data blocks, then the compression ratio is optimized, but the complexity of selecting and managing multiple techniques increases
Solution Approach 1:
The patent implements a self-service mechanism where the compression system automatically analyzes each data block's statistical features and selects the most appropriate compression technique without external intervention. This automation eliminates the need for manual technique selection and management, reducing operational complexity while maintaining optimized compression ratios.
Solution Approach 2:
The patent changes the operational parameters of the compression system by using statistical features of each data block as the basis for technique selection. This parameter-driven approach allows the system to automatically adapt to different data characteristics and select optimal compression techniques without requiring manual configuration or management.
3Productivity
If statistical analysis is performed on each data block to select compression techniques, then compression efficiency is maximized, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial statistical analysis to each data block, focusing on the most relevant features for compression technique selection rather than performing exhaustive analysis. This selective approach maintains high compression efficiency by identifying sufficient statistical characteristics without wasting time on unnecessary computations.
Data Source
AI summary
The present disclosure describes methods and systems that provide for content based dynamic data compression. During a training stage a plurality of training sets are compressed using multiple data compression techniques, and each training data set is associated with a most effective compression technique. Statistical features can be extracted from each of the training data sets with consideration of the most effective compression techniques and are saved as a compression parameters file. During a working stage, individual data blocks within a telemetry data set are compressed using a data compression technique selected for the data block in light of the statistical feature presented in the compression parameters file, and the compressed data blocks are written to a compressed data set, along with encoding tags that identify the compression technique used on each data block. During a consumption stage, the compressed data blocks are decompressed using techniques identified by the encoding tags.