Heuristic File Selection for Backup Systems
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Solution Overview
Problem
Conventional data backup methods are inefficient and costly for modern computer systems that store large files, as they require significant resources and user expertise, and often fail to prioritize the most valuable files.
Innovation Solution
A data backup system that uses a file type database with location exclusion and file type tables to simplify setup and selection rules, allowing for incremental backups and file clustering to optimize resource usage and prioritize high-value files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional backup methods create a copy of every file, then data protection is ensured, but resource consumption and cost increase significantly
Solution Approach 1:
The patent segments the backup process by dividing files into different categories based on their importance and characteristics. It uses file type databases and clustering to group files, allowing selective backup of only necessary portions rather than copying every file, thus reducing resource consumption while maintaining data protection for critical files
Solution Approach 2:
The patent applies local quality by assigning different backup priorities and strategies to different files based on their specific characteristics. High-value files receive enhanced protection and backup frequency, while less critical files use standard or reduced backup approaches, optimizing resource allocation according to local file importance
2Reliability
If conventional backup methods copy every file, then complete data recovery is possible, but backup time and operational complexity increase
Solution Approach 1:
The patent performs preliminary actions by pre-categorizing files into clusters based on their importance, file type, and location before the actual backup process. File type databases are pre-populated with classification rules, allowing the backup system to quickly identify and prioritize critical files, thereby reducing backup time while ensuring complete recovery capability for high-value data
Solution Approach 2:
The patent applies partial action by performing comprehensive backup operations only on high-value file clusters that require complete protection, while using incremental or selective backup approaches for other files. This partial focus on critical data reduces overall backup time while maintaining adequate recovery capabilities
3Reliability
If conventional backup methods backup all files, then no data is left unprotected, but system complexity and user expertise requirements increase
Solution Approach 1:
The patent implements self-service by enabling the backup system to automatically classify, prioritize, and select files for backup using pre-configured file type databases and clustering algorithms. The system autonomously determines which files require protection and applies appropriate backup strategies without requiring users to manually configure complex backup policies, thereby maintaining comprehensive protection while reducing system complexity from the user perspective
4Use of energy by moving object
If selective backup of high-value files is implemented, then resource usage is optimized, but risk of missing critical files increases
Solution Approach 1:
The patent performs preliminary classification of all files into structured clusters based on file type, location, and importance criteria before selective backup. File type databases are pre-populated with comprehensive classification rules that ensure no critical files are overlooked, allowing the system to confidently select only necessary files for backup while maintaining protection completeness
Solution Approach 2:
The patent incorporates feedback mechanisms where the backup system continuously monitors and updates file classifications based on changing data patterns and user behavior. This feedback loop ensures that the selective backup process adapts to new critical files or changing importance levels, maintaining comprehensive protection coverage while optimizing resource usage
Data Source
AI summary
A method, article of manufacture, and apparatus for backing up data. In some embodiments, this includes loading a file type database, wherein the file type database includes a location exclusion table and a file type table, analyzing the file type database, updating a file backup list based on the analysis, and storing the updated file backup list in a storage device. In some embodiments, a file may be scanned prior to loading the database.