Data Recovery Management via Metadata Classification
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Solution Overview
Problem
Current data recovery systems face challenges in efficiently managing large volumes of data items with multiple versions, as manual recovery operations are time-consuming and resource-intensive, especially when dealing with hundreds of thousands or millions of data items, and storing and transmitting large data sets can be costly and time-consuming.
Innovation Solution
A recovery management system that automatically classifies data items based on metadata regarding versioning and storage operations, using a target recovery time to determine recovery actions, which may involve modifying metadata rather than the underlying data, and employs a bidirectional doubly linked list for efficient data structure processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recovery operations are performed on large volumes of data items, then recovery accuracy can be maintained, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent segments the large volume of data items into groups based on their metadata characteristics and versioning patterns. By classifying data items into different categories (e.g., frequently accessed, rarely accessed, critical, non-critical), the system can apply different recovery strategies to each segment, enabling automated processing while maintaining accuracy for critical items and reducing time for less critical items.
Solution Approach 2:
The patent changes the parameter of recovery operation from uniform manual processing to differentiated automated processing based on metadata parameters. By analyzing metadata such as access patterns, data importance, and version history, the system dynamically adjusts recovery parameters (e.g., priority, method, resources allocated) to optimize the balance between accuracy and time consumption.
2Loss of information
If all data items are recovered with full detail, then data completeness is ensured, but resource consumption and processing overhead increase
Solution Approach 1:
The patent applies local quality by differentiating the level of recovery detail based on the specific characteristics of each data item or group. Instead of uniformly recovering all data items with the same level of detail, the system applies higher recovery fidelity to critical data items and accepts lower fidelity for less critical items, thereby reducing overall processing resources while maintaining data completeness for essential information.
Solution Approach 2:
The patent implements partial action by recovering only the necessary portions of data items based on their importance and access patterns. For frequently accessed or critical data, full recovery is performed, while for less critical data, selective recovery of key metadata or summary information suffices, reducing processing overhead while ensuring data completeness where it matters most.
3Reliability
If extensive data copying and transmission are performed during recovery, then data availability is improved, but time and cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-processing and pre-positioning data items during normal operation. Metadata is continuously updated and organized, and data items are staged in advance in appropriate locations based on predicted recovery needs. When recovery is initiated, the system can quickly retrieve pre-positioned data without extensive real-time copying and transmission, thereby improving data availability while reducing transmission time and cost.
4Productivity
If classification based on target recovery time is implemented, then recovery efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamics by making the classification system adaptive rather than static. The classification criteria and categories are dynamically adjusted based on changing data characteristics, access patterns, and recovery priorities. The system can automatically refine classification rules over time based on observed recovery outcomes, improving efficiency without requiring complex manual configuration or rigid hierarchical structures.
Data Source
AI summary
Systems and methods are provided for management of data item recovery operations based on classification of data items with respect to a target recovery time. Data items may be classified using metadata regarding versioning and other data storage operations performed on the data items, and the timing of those storage operations with respect to the target recovery time (TRT). Each class may be associated with one or more recovery actions. The recovery actions may involve modifying the metadata rather than the underlying data items.