Network Data Compression Using Recurring Session Patterns
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Solution Overview
Problem
Conventional digital data compression techniques are inefficient for handling real-time data from dynamic networks, requiring substantial processing overhead and being ill-suited for transient data, especially in decompression and handling recurring patterns.
Innovation Solution
A method that compresses and decompresses network data by partitioning webpages into segments, using mappings like hash functions to identify and encode recurring data patterns, reducing storage space and processing time, and adapting to changes in data patterns through statistical methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional compression techniques (Huffman, LZW, Run Length Encoding) are used to compress digital data, then data storage efficiency is improved, but processing overhead and decompression time increase substantially
Solution Approach 1:
The patent segments network data into distinct components: static recurring patterns and dynamic variable portions. By separating these segments, the system applies different compression strategies to each, avoiding the need to process entire data sets through complex statistical encoding. This segmentation reduces processing overhead while maintaining compression efficiency.
Solution Approach 2:
The system performs preliminary identification and extraction of recurring static patterns from network data before compression. By pre-processing the data to isolate these patterns, the system avoids performing complex statistical analysis during compression and decompression operations, thereby reducing processing time while maintaining storage efficiency.
2Quantity of substance
If conventional compression techniques are applied to network data, then some compression is achieved, but flexibility and adaptability to dynamic network data patterns are insufficient
Solution Approach 1:
The patent implements a dynamic compression system that adapts to changing network data patterns. The system continuously identifies recurring patterns in real-time network traffic and adjusts its compression approach accordingly. This dynamic adaptation allows the system to maintain high compression ratios while remaining flexible to varying data patterns, unlike static conventional methods.
Solution Approach 2:
The system changes its compression parameters based on the identified recurring patterns in network data. By dynamically adjusting compression parameters according to the specific characteristics of the data being compressed, the system achieves both high compression ratios and adaptability to different data patterns, resolving the contradiction between compression efficiency and flexibility.
3Quantity of substance
If statistical encoding processes are used for compression, then compression efficiency is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts and isolates recurring static patterns from network data, separating them from the dynamic variable portions. By taking out these recurring patterns and handling them separately, the system avoids applying complex statistical encoding to the entire data set, thereby improving processing speed while maintaining compression efficiency for the extracted patterns.
Data Source
AI summary
A method of storing data is provided. The method includes receiving a first set of data provided over a network session, and compressing the first set of data to form a second set of data. As further provided, the second set of data includes a number of bytes smaller than the first set of data. Further, the second set of data includes a portion of compressed data that is common to other network sessions. The method further includes compressing further the portion of the compressed data common to other network sessions to obtain a third set of data, and storing the third set of data.