Document Versioning via Significance Thresholds
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Solution Overview
Problem
The increasing use of auto-save functions in cloud storage leads to rapid growth of document version histories, resulting in higher storage costs and increased storage demands, as existing technologies lack efficient methods to determine when to create new versions of documents, thereby wasting storage space.
Innovation Solution
An automated system and method that utilizes client and server versioning engines to analyze specific factors such as edit importance, time intervals, and storage availability to determine when to create new versions of electronic documents, reducing unnecessary version creation and optimizing storage usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If auto-save functions are used to automatically save documents at predetermined intervals, then data loss risk is reduced, but storage costs and version payload increase
Solution Approach 1:
The system changes the parameter of version creation from fixed time intervals to dynamic significance-based intervals. Versions are created only when edits reach a certain significance threshold, rather than at every predetermined time interval, thereby reducing the quantity of versions stored while maintaining data reliability.
Solution Approach 2:
The system applies different quality standards to different edits based on their significance. Important edits that meet the significance threshold are preserved as versions, while less significant edits are not stored as separate versions, optimizing storage usage while maintaining critical document history.
2Loss of information
If versions are created for every set of client edits, then complete document history is maintained, but storage costs increase
Solution Approach 1:
The system introduces a significance threshold parameter to filter which edits warrant version creation. This parameter change ensures that only edits meeting the significance criterion are stored as versions, preventing information loss of important changes while reducing storage of redundant versions.
Solution Approach 2:
Instead of copying every edit as a new version, the system selectively copies only those edits that meet the significance threshold. This selective copying approach maintains essential document history while minimizing storage space consumption.
3Productivity
If client versioning engine creates new versions frequently, then latest edits are captured, but memory storage increases
Solution Approach 1:
The system changes the frequency parameter of version creation from high (every edit) to optimized (only significant edits). The significance threshold parameter controls this frequency, ensuring that productivity in capturing important edits is maintained while memory storage is reduced by not capturing every minor edit.
4Loss of information
If server stores all client edits as versions, then complete version history is available, but storage costs increase
Solution Approach 1:
The server applies a significance threshold parameter to filter which client edits should be stored as versions. This parameter change ensures that complete history of significant edits is preserved while reducing storage capacity requirements by excluding less significant edits from version storage.
Data Source
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AI summary
Memory conserving versioning of an electronic document is provided. Client versioning factors are analyzed by a client versioning engine and server versioning factors are analyzed by a server versioning engine for determining when an electronic document should be stored as a new version. Accordingly, new versions of an electronic document are only created when determined to be sufficiently important, thus reducing the amount of memory required for increased version payload.