Comparative Data Compression Using Reference-Based Delta Transmission
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Solution Overview
Problem
Current data transmission methods face challenges with large data sizes, particularly in distributed work environments, where transmitting data over communication mediums can strain systems and exceed receiver limits, even after compression, due to ongoing megabyte-sized data requirements.
Innovation Solution
The method involves comparing data with reference data to separate it into static and dynamic parts, transmitting only the dynamic part, and using lossless compression algorithms for efficient data transfer, allowing for efficient recreation of the data at the destination by merging the dynamic and static parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transmitted over communication medium, then data sharing is enabled, but system traffic increases and receiver data limits are exceeded
Solution Approach 1:
The data is divided into two segments: static data (common with reference) and dynamic data (unique). Only the dynamic segment is transmitted, while the static segment is referenced from the reference data stored at the destination. This segmentation reduces transmission volume while maintaining complete data reconstruction capability.
Solution Approach 2:
Reference data is pre-stored at the destination before transmission occurs. This preliminary action enables the receiver to reconstruct complete data by combining the transmitted dynamic portion with the pre-existing static portion, eliminating the need to transmit redundant common data.
2Productivity
If compression tools are used to reduce data size, then transmission efficiency improves, but compressed data still occupies several megabytes causing transmission issues
Solution Approach 1:
The invention extracts only the essential unique information (dynamic data) from the complete dataset, separating it from the common reference data. This extraction approach transmits minimal necessary data rather than compressing the entire dataset, achieving superior size reduction.
Solution Approach 2:
Instead of transmitting or compressing the complete dataset, only the partial dynamic portion that differs from reference data is transmitted. This partial action approach is more efficient than full compression because it transmits only the necessary changes.
3Adaptability or versatility
If server storage is used to share data, then data accessibility is improved, but performance decreases and operational costs increase
Solution Approach 1:
The destination system maintains local reference data and performs self-service by reconstructing complete data locally through merging transmitted dynamic data with stored reference data. This eliminates the need for remote server access, improving performance and reducing operational costs while maintaining accessibility.
Data Source
AI summary
Data is compared with a reference data, which is accessible at a source. The data is then separated into a static part and a dynamic part, wherein the static part is common with the reference data and the dynamic part is not common with the reference data. The dynamic part is transmitted to a destination. At the destination, the dynamic part is merged with the static part, which is obtained from a copy of the data accessible at destination.