Automated Object Grouping in Data Management Systems
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Solution Overview
Problem
In data management systems, manual object grouping by users is inefficient and prone to inconsistencies, especially in distributed environments where objects are managed separately, leading to unsatisfactory data protection and recovery outcomes.
Innovation Solution
A method that automatically detects operation parameters of objects, determines their correlation rate, and groups objects based on a predetermined threshold, enabling automated logical grouping and enhancing data consistency without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual object grouping is used, then users can control the grouping process, but the process is inefficient and prone to inconsistencies
Solution Approach 1:
The system performs automatic object grouping without requiring manual user intervention. The grouping mechanism autonomously detects operation parameters, calculates correlation rates, and determines groupings based on predefined thresholds, eliminating the need for users to manually perform the grouping task while maintaining consistency and efficiency
2Ease of operation
If separate backups are performed for objects, then individual object management is simplified, but data consistency between objects deteriorates
Solution Approach 1:
The system merges multiple objects into groups based on their operational correlation. By identifying objects that frequently access shared resources or exhibit similar operational patterns, the system creates unified groups that can be managed together, ensuring data consistency across grouped objects while maintaining the simplicity of individual object management through automated correlation detection
3Productivity
If automatic grouping is implemented, then grouping efficiency is improved, but system complexity increases
Solution Approach 1:
The system transforms complex object relationships into measurable operation parameters such as access frequency, resource sharing patterns, and temporal correlations. By converting qualitative object relationships into quantitative parameters that can be automatically processed, the system achieves efficient automatic grouping while keeping the underlying mechanism transparent and manageable through parameter-based decision making
Data Source
AI summary
Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for grouping objects in a data management system. The method includes: detecting operation parameters of at least two of a plurality of objects in a data management system, and determining a rate of correlation between the at least two objects based on the detected operation parameters, wherein the rate of correlation indicates a degree of correlation between the at least two objects. The method further includes: comparing the determined rate of correlation with a predetermined threshold, and determining, based on the comparison of the determined rate of correlation with the predetermined threshold, grouping of the at least two objects. With this method, objects with a high degree of correlation are logically grouped together, so that a user can manage objects in batches in an efficient manner during object management, thus improving the system performance. In particular, data consistency can be maximized in the process of data protection and restoration.