Holistic User Profile Generation for Data Protection Resource Management
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Solution Overview
Problem
Current data protection systems face challenges in extracting useful insights from complex and distributed metadata, identifying holistic user profiles, inferring user patterns, and taking proactive actions without resource-intensive efforts, leading to inefficiencies in managing data growth and user satisfaction.
Innovation Solution
A system and method that obtain client metadata from client environment data protection modules, analyze resource utilization, and generate holistic user profiles to identify resource utilization patterns, detect exceeding maximum resource utilization levels, and provide recommendations for modification, thereby automating the process of extracting insights and improving user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual analysis of complex distributed metadata is performed, then useful insights can be extracted, but extensive engineering efforts and resources are required
Solution Approach 1:
The system enables automated self-service by having the data protection system itself perform metadata analysis, user profile identification, and pattern inference without requiring external engineering intervention. The automated generation of holistic user profiles and resource utilization patterns allows the system to extract useful insights from complex distributed metadata independently, eliminating the need for extensive manual engineering efforts while maintaining comprehensive information extraction.
2Measurement precision
If holistic user profiles are identified manually, then user patterns can be inferred, but resource-intensive efforts are required
Solution Approach 1:
The system performs preliminary automated actions by continuously collecting metadata, generating holistic user profiles, and inferring user patterns in the background without requiring manual intervention. This preliminary automated processing enables the system to have user pattern information ready when needed, achieving precise user pattern identification while maintaining high resource efficiency through automated batch processing and incremental updates.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational systems. Instead of human engineers manually analyzing metadata to identify user profiles and patterns, the system uses automated algorithms and data processing mechanisms to perform these tasks, substituting mechanical human effort with efficient computational processes that achieve the same analytical goals with significantly reduced resource consumption.
3Ease of operation
If proactive actions are taken based on metadata insights, then user satisfaction improves, but the system becomes more complex
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring metadata, analyzing user behavior patterns, and automatically adjusting data protection operations based on inferred user preferences and resource utilization patterns. This feedback loop enables proactive actions that improve user satisfaction, such as optimizing backup schedules or resource allocation based on learned patterns, while the automated nature of the feedback processing keeps system complexity manageable through standardized algorithms.
Data Source
AI summary
A method for managing a data protection module includes: obtaining client metadata of a first client environment data protection module (CEDPM) and a second CEDPM; identifying a first resource and a second resource that have been utilized; obtaining a resource utilization value for each identified resource; identifying modifications that have been performed on each identified resource; deriving an average resource utilization value for each identified resource; generating a holistic profile for each CEDPM based on at least the first resource and the second resource, the resource utilization value for each identified resource, the modifications, and the average resource utilization value for each identified resource; determining that the resource utilization value of the first resource exceeds a predetermined maximum resource utilization level; and sending a recommendation and a portion of the holistic profile of the first CEDPM to a user of the first CEDPM that utilizes the first resource.