Clustered File Deduplication for Canonical Content Management
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Solution Overview
Problem
Managing and deduplicating files across different media and platforms is challenging due to file duplications with slight content variations and naming inconsistencies, leading to inefficient storage and retrieval.
Innovation Solution
A system that performs a Reverse Path Crawl to cluster files based on similarity, calculates similarity scores, and selects a canonical file for each cluster using content analysis and user feedback to optimize file management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If files are duplicated across multiple media and platforms for backup, then data redundancy and availability are improved, but storage efficiency and management complexity deteriorate
Solution Approach 1:
The system merges duplicate files across different media and platforms into unified clusters. Files with similar content and naming patterns are grouped together, and a canonical representative is selected for each cluster. This consolidation reduces the number of separate file management operations while maintaining access to all versions, thereby improving management efficiency without sacrificing data availability.
Solution Approach 2:
The patent introduces a file clustering mechanism as an intermediary layer between individual files and the user. This intermediary organizes duplicates into clusters with canonical representatives, providing a simplified interface for file management. Users interact with cluster representatives rather than individual duplicates, reducing management complexity while preserving access to all file versions through the cluster structure.
2Productivity
If simple filename comparison is used to identify duplicates, then processing speed is improved, but identification accuracy deteriorates
Solution Approach 1:
The system segments the file identification process into multiple stages: first comparing filenames for obvious duplicates, then analyzing content similarity for closer matches, and finally evaluating metadata and context for precise identification. This segmented approach maintains speed for obvious duplicates while adding accuracy for borderline cases without requiring all comparisons to be performed simultaneously.
Solution Approach 2:
The patent applies partial content analysis rather than complete file comparison for all files. Content similarity algorithms are applied selectively to files that pass initial filename and metadata filters, providing accurate identification for relevant duplicates while avoiding the computational overhead of analyzing all files exhaustively, thus maintaining processing speed.
3Loss of information
If all file versions are stored and managed individually, then complete content history is preserved, but storage efficiency and retrieval speed deteriorate
Solution Approach 1:
The system merges duplicate file versions into clusters while preserving all individual files. A canonical representative is selected for each cluster to serve as the primary access point, reducing retrieval time. All versions remain accessible through the cluster structure, maintaining complete content history. This merging approach eliminates redundant retrieval operations while preserving full historical information.
Solution Approach 2:
The patent creates a virtual copy structure where cluster representatives serve as proxies for accessing all versions in the cluster. Instead of physically deleting duplicates to improve storage efficiency, the system creates a hierarchical copying structure that allows fast access to canonical representatives while maintaining references to all original versions. This virtual copying approach improves retrieval speed without sacrificing content history preservation.
Data Source
AI summary
Systems and methods for file sorting and de-duplication of files in a filesystem. A reverse path crawl may be performed followed by clustering files into file buckets. The clustering may be followed by one or more steps of combining file buckets based on one or more criteria. The merged content of the files in each file bucket may be analyzed and a similarity score may be calculated for each pair of files in the bucket. Based on the similarity score, the system may split each bucket into one or more dissimilar buckets. The dissimilar buckets may have a canonical file selected for each and a canonical file name created/chosen for said selected canonical file.