Grammar-Aware Data Compression for Lossless GDSII Files
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Solution Overview
Problem
Existing data compression methods lack efficiency in handling specific grammar rules and structures, leading to suboptimal compression and decompression of data files, particularly in formats like GDSII, which restricts the effectiveness of tools like gzip and bzip2.
Innovation Solution
A method utilizing a transliteration procedure to fragment and reassemble data files, combined with an abbreviation procedure and referencing technique, to achieve lossless compression and decompression, specifically tailored for data formats like GDSII, by maintaining an adaptive library for efficient data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If generic compression tools (gzip, bzip2) are used, then lossless compression is achieved, but compression efficiency is limited due to inability to recognize specific data characteristics and grammar rules
Solution Approach 1:
The patent applies local quality by treating different sections of the data file differently based on their specific characteristics. The compression tool identifies and applies specialized compression algorithms to specific data sections (such as header, body, footer) while using generic compression for other sections, thereby optimizing compression efficiency for each local characteristic.
Solution Approach 2:
The patent changes parameters by dynamically adjusting compression settings based on detected data characteristics. When specific grammar rules or patterns are identified in data sections, the tool switches between different compression algorithms and parameter sets to maximize compression efficiency while maintaining adaptability to various data formats.
2Productivity
If data is fragmented into modules for selective compression, then storage and transfer efficiency improve, but system complexity increases due to need for module identification and reassembly
Solution Approach 1:
The patent applies segmentation by dividing the data file into distinct modules (header, body, footer) that can be independently compressed and managed. Each module is identified by specific markers or delimiters, allowing selective compression while maintaining a relatively simple reassembly process through the use of clear module boundaries and identification schemes.
Data Source
AI summary
The present invention provides a method and system for lossless manipulation of a data file size. The method comprises receiving the data file in an original format (205) and performing an action (210) of compression (215) or decompression (220) on the data file. The data file can be compressed or decompressed using a transliteration procedure. The present invention also provides a method for compressing or decompressing a data file using an abbreviation procedure, as well as a referencing procedure. A method for compressing the data file using any combination of the transliteration procedure, the abbreviation procedure and the referencing procedure is provided. The present invention further provides a system for performing compression or decompression on a data file based on the transliteration procedure, the abbreviation procedure and the referencing procedure.