Variable-Segment Index Code for Text Message Compression
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Solution Overview
Problem
Existing communication devices face high resource demands due to inefficient text message compression methods, which do not effectively adapt to changing keyword frequencies and temporal usage patterns.
Innovation Solution
A data processing system that generates and manages an index code by associating keywords with indices, logically dividing it into variable-sized segments, and updating based on natural and usage keyword frequency distributions, as well as temporal changes, to compress and decompress short text messages efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If frequent updates of the index code are performed to adapt to changing keyword frequencies, then the compression efficiency is improved, but the resource consumption and device complexity increase
Solution Approach 1:
The index code structure is made dynamically adaptable through logical segmentation into variable-sized segments. Each segment can be independently updated based on usage patterns, allowing the system to adapt to changing keyword frequencies without requiring complete reupdates of the entire index code, thus reducing resource consumption while maintaining compression efficiency
Solution Approach 2:
The index code is divided into multiple logically separate segments of variable sizes. This segmentation allows selective updating of only those segments that contain frequently changing keywords, rather than updating the entire index code structure. This reduces the computational resources and energy required for updates while preserving the ability to adapt to temporal changes in language usage
2Adaptability or versatility
If the index code is frequently updated to reflect temporal changes in keyword usage, then the adaptability is improved, but the device complexity and processing overhead increase
Solution Approach 1:
By dividing the index code into logically separate segments, the system can track and update only the segments that contain keywords exhibiting temporal usage patterns. This reduces the processing overhead compared to updating a monolithic index code structure, while still achieving high adaptability to changing language usage
Solution Approach 2:
The system monitors changes in keyword frequency parameters over time and selectively updates index code segments based on these parameter changes. This allows the system to adapt to temporal patterns in keyword usage without requiring complex processing for every possible change, as updates are triggered only when significant parameter changes are detected
3Productivity
If variable-sized segments are introduced to optimize index code structure, then the compression efficiency is improved, but the device complexity increases
Solution Approach 1:
The index code is organized into variable-sized segments that can be independently managed. This segmentation allows the system to optimize compression efficiency by allocating appropriate segment sizes based on keyword frequency distributions, while the modular structure actually simplifies management compared to a monolithic structure, as each segment can be processed and updated independently
Solution Approach 2:
Different segments of the index code can have different properties and optimization strategies tailored to their specific content characteristics. High-frequency keywords can be placed in segments optimized for quick access, while less frequent keywords can be in segments with different optimization characteristics. This local optimization improves overall compression efficiency without requiring complex global management
Data Source
AI summary
A method of compressing short text messages, comprising: generating an index code comprising an association of keywords in the text messages with indices, the index code is logically divided into segments of variable size, each segment comprising at least one bucket, being a constant range of indices; adjusting the index code according to a natural keyword frequency distribution and to statistical analysis of the text messages; associating short indices with frequent keywords in the text messages; converting the text messages into compressed text messages in which at least some of the keywords are replaced by the associated indices; and updating the association between the indices and the keywords, updating the segments, and updating the updating frequency in respect to a usage keyword frequency distribution and temporal changes thereof.