Resource String Compression for Fast Lookup and Selective Decompression
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Solution Overview
Problem
Existing compression technologies are inefficient for compressing short text strings in resource files, such as those used in software applications, due to the lack of repetitive patterns and the need for per-string decompression, which affects storage and performance.
Innovation Solution
Implementing similarity compression, map-less encoding, and bit-level compression to reduce the size of resource files, along with an enhanced naming process using hash values for efficient string retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression technologies are used on resource files, then file size is reduced, but decompression speed and runtime performance deteriorate due to the need to decompress entire files at once
Solution Approach 1:
The resource file is divided into individual resource string entries, each with its own compression metadata. This allows selective decompression of only the needed strings at runtime rather than decompressing the entire file, resolving the contradiction between file size reduction and decompression speed.
Solution Approach 2:
Compression metadata and lookup structures are prepared in advance during the build process, enabling fast runtime access to compressed resource strings without requiring full file decompression. This preliminary preparation maintains both small file size and fast access performance.
2Productivity
If resource strings are decompressed on a per-string basis, then runtime performance improves, but the complexity of locating and managing individual strings increases
Solution Approach 1:
A hash table or index structure serves as an intermediary between resource string names and their compressed data locations. This intermediary enables fast O(1) lookup of individual strings by name without requiring complex search algorithms, resolving the contradiction between per-string access and management complexity.
3Speed
If numerical identifiers are used for resource strings, then look-up speed improves, but maintainability and ease of use deteriorate across multiple development platforms
Solution Approach 1:
The system uses human-readable string names as the public interface while maintaining numerical or hash-based identifiers internally for fast lookup. This intermediary layer allows developers to use meaningful names for maintenance while the system achieves fast lookup through the internal identifier mapping.
Solution Approach 2:
The system creates and maintains a mapping table that copies the relationship between human-readable names and internal identifiers. This mapping is automatically generated and updated during builds, allowing fast identifier-based lookup while preserving name-based maintainability without manual intervention.
4Quantity of substance
If resource strings are compressed using traditional methods, then file size is reduced, but the ability to selectively decompress individual strings deteriorates
Solution Approach 1:
Each resource string is compressed as an independent unit with its own metadata header, allowing the compression algorithm to work on individual strings rather than the entire file. This segmentation enables selective decompression of specific strings while maintaining overall file size reduction, resolving the contradiction between compression efficiency and selective access capability.
Data Source
AI summary
Technology is disclosed herein for compressing, encoding, and otherwise reducing the size of resource files. In at least one implementation, similarity compression is employed to reduce the size of a resource file. In another implementation, map-less encoding is employed to reduce the number of bytes used to represent a resource string. Bit-level compression is employed in another implementation to reduce the quantity of bits used to encode each character in a string. In addition, implementations are disclosed related to technology for naming strings and accelerated string location and retrieval.