Indexing Fragmented User Data Objects
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Solution Overview
Problem
Existing systems face challenges in efficiently backing up and restoring limited-access user data due to restricted access and the need for specialized methods to handle fragmented and duplicative data, which affects computational efficiency and storage capacity.
Innovation Solution
A system and method that utilize indexing data to identify and delete duplicative objects, improving computational efficiency by associating fragmented user data with existing objects in the object repository, and storing user data backups in persistent storage without adding duplicative objects, thereby optimizing storage and restoration processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fragmented user data is backed up without duplication detection, then backup completeness is improved, but storage capacity is wasted due to duplicative objects
Solution Approach 1:
The system uses hashing to transform user data objects into fixed-size hash values, which act as unique identifiers. This allows the system to detect duplicative objects by comparing hash values rather than storing full copies, thereby reducing storage capacity while maintaining backup completeness through accurate duplication detection.
2Quantity of substance
If duplication detection is performed on all fragmented user data, then storage capacity is optimized, but computational efficiency decreases due to processing overhead
Solution Approach 1:
The system performs hashing on a selective basis rather than uniformly processing all fragmented user data. By applying partial action to identify duplicative objects, the system optimizes storage capacity while minimizing computational overhead through targeted processing of data fragments that require duplication detection.
3Quantity of substance
If duplicative objects are identified and excluded from backup, then storage requirements are reduced, but backup complexity increases due to association management
Solution Approach 1:
The system creates and stores hash values as simplified representations of user data objects. These hash copies serve as unique identifiers that enable efficient duplication detection without requiring complex comparison of full data objects, thereby reducing storage requirements while managing backup complexity through the use of these cryptographic hash representations.
4Speed
If indexing data is used to manage user data objects, then restoration efficiency is improved, but system complexity increases due to indexing structure
Solution Approach 1:
The system replaces traditional mechanical indexing methods with cryptographic hash-based indexing. This substitution enables efficient object identification and restoration by using hash values as index keys, thereby improving restoration efficiency while managing system complexity through the adoption of well-established hashing algorithms and data structures.
Data Source
AI summary
A system including persistent storage for storing an object repository and a user data backup. The system also includes a manager that obtains fragmented user data associated with an object that is potentially duplicative of an object stored in the object repository; makes a determination that the object is duplicative based on a copy of the object stored in the object repository; in response to making the determination, adds the fragmented user data to a user data backup without adding the object to the backup; associates the fragmented user data in the user data backup with the copy of the object stored in the object repository; and stores the user data backup in the persistent storage.