Tree Delta Encoding for Software Version Directory Changes
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Solution Overview
Problem
Existing file delta encoding methods are inadequate for tracking changes between different versions of software, which contains multiple files and directories, as they cannot simply be applied to determine differences between data trees.
Innovation Solution
A system for tree delta encoding that accesses and compares two data trees, classifying similarity between nodes based on file names, paths, values, and directory structures to generate a delta as a sequence of edit operations, using discrete similarity levels such as no match, weak, fair, exact, and strong matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If file delta encoding is applied to software versions, then change tracking between files is improved, but it cannot determine changes between different versions of software containing multiple files and directories
Solution Approach 1:
The patent segments the software version comparison problem into hierarchical components: data trees representing directory structures, nodes representing files or directories, and similarity levels for different types of nodes. This segmentation allows the application of delta encoding to complex software versions by breaking them down into manageable hierarchical units that can be individually compared and encoded.
2Ease of manufacture
If simple file delta encoding is used, then encoding process is simple, but it cannot handle complex data tree structures with directories and nested files
Solution Approach 1:
The patent implements nesting by representing directory structures as hierarchical data trees where directories contain nested files and sub-directories. Each level of the hierarchy can be independently encoded while maintaining the nested structure, allowing simple file delta encoding techniques to be applied recursively at each level of the nested data tree structure.
Solution Approach 2:
The patent adds hierarchical dimensionality to the encoding process by organizing files and directories into multi-level data trees with parent-child relationships. This transforms the flat file comparison problem into a multi-dimensional hierarchical comparison, enabling the handling of complex directory structures while maintaining encoding simplicity through recursive application at each hierarchical level.
3Measurement precision
If discrete similarity levels are used for node classification, then matching precision is improved, but classification complexity increases
Solution Approach 1:
The patent applies local quality by defining different similarity level criteria for different types of nodes (file nodes versus directory nodes). File nodes use similarity levels based on file names and contents, while directory nodes use similarity levels based on directory names and structural relationships. This localized differentiation improves matching precision for each node type without requiring a single complex classification system for all nodes.
Data Source
AI summary
A first data tree may include a first set of directory nodes and a first set of file nodes. A second data tree may include a second set of directory nodes and a second set of file nodes. Similarity between the first set of file nodes and the second set of file nodes may be classified based on file names, file paths, and file values. Similarity between the first set of directory nodes and the second set of directory nodes may be classified based on directory names, directory paths, nested folders, and included files. A delta between the first data tree and the second data tree may be generated based on the classification of similarity between the first set of file nodes and the second set of file nodes and the classification of similarity between the first set of directory nodes and the second set of directory nodes.