Symbol-Based Data Compression for Sparse Data Bandwidth Reduction
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Solution Overview
Problem
Current data compression methods are inefficient in handling sparse or non-uniform data, leading to increased bandwidth utilization and storage requirements, especially when transmitting or storing data across networks or within computing systems.
Innovation Solution
The implementation of the Multipass Embedded Residual Group Encoding (MERGE) data compression method, which generates symbol string codes indicating positions, skips, or wraps around to efficiently compress data by identifying and encoding patterns in data sets, including sparse or non-uniform distributions, using techniques like run length coding and adaptive coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional data compression methods are used, then data transmission and storage are performed, but bandwidth utilization increases and compression efficiency decreases for sparse or non-uniform data
Solution Approach 1:
The patent segments the data into groups of symbols and processes each group independently by identifying symbol strings within the group. This segmentation allows the compression algorithm to efficiently handle sparse or non-uniform data by focusing on local patterns rather than processing the entire data set as a whole, thereby improving compression efficiency while reducing bandwidth utilization.
Solution Approach 2:
The patent employs adaptive coding that dynamically adjusts the coding strategy based on the characteristics of the data being compressed. The algorithm adapts to sparse or non-uniform data distributions by modifying its approach to identify and encode symbol strings, allowing it to maintain high compression efficiency across varying data types and distributions while minimizing bandwidth requirements.
2Quantity of substance
If data is compressed using traditional methods, then storage space is reduced, but the complexity of handling sparse or non-uniform data increases
Solution Approach 1:
The patent implements a self-service mechanism where the compression algorithm automatically identifies symbol strings and determines the most efficient coding approach without requiring external intervention or complex preprocessing. The algorithm serves itself by adapting to the data characteristics and making autonomous decisions about how to compress sparse or non-uniform data, thereby reducing handling complexity while achieving effective data size reduction.
Solution Approach 2:
The patent changes the parameters of the coding process dynamically based on the data being compressed. By adjusting coding parameters according to the specific characteristics of sparse or non-uniform data, the algorithm simplifies the handling process while maintaining effective compression ratios, thus reducing both data size and handling complexity simultaneously.
3Ease of operation
If compression is applied to all data types uniformly, then processing is simplified, but compression efficiency decreases for specific data distributions
Solution Approach 1:
The patent creates a universal compression framework that can handle multiple data types and distributions through a single unified approach. The algorithm identifies symbol strings and applies adaptive coding that works effectively across different data characteristics, providing both processing simplicity and high compression efficiency without requiring separate specialized algorithms for different data types.
Solution Approach 2:
The patent employs dynamic adaptation within a unified processing framework. The algorithm automatically adjusts its behavior based on the specific data distribution it encounters, maintaining ease of operation through a single interface while achieving high compression efficiency for various data types including sparse and non-uniform distributions through its adaptive nature.
Data Source
AI summary
Briefly, within a computer or digital data processing system, embodiments describe a method, article and apparatus for compressing data is described, which may be used for communicating between two or more components connected to an interconnection medium (e.g., a bus) within a single computer or digital data processing system, and/or for communicating between two or more computers or digital data processing systems via an interconnection medium such as a network.