Dynamic User Profile TTL Management at Edge Database
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Solution Overview
Problem
Conventional user profile management systems schedule user profiles for deletion based on a user profile-independent time interval, leading to inefficiencies such as retaining profiles unnecessarily or prematurely deleting them before they can be used.
Innovation Solution
A user profile management system that determines an optimal time-to-live (TTL) value for user profiles at an edge database by analyzing historical request data, allowing for dynamic adjustment of the TTL based on usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a user profile-independent time interval is used for deletion scheduling, then the system operation is simplified, but the database operational efficiency deteriorates due to unnecessary retention or premature deletion
Solution Approach 1:
The patent applies dynamics by transitioning from a static, fixed time interval for user profile deletion to a dynamic, adaptive TTL value that adjusts based on actual user activity patterns. The system continuously monitors user actions and recalculates the TTL accordingly, making the deletion schedule flexible and responsive to real-time usage rather than following a rigid predetermined timeline.
Solution Approach 2:
The patent implements feedback by using user activity data to inform and adjust the TTL calculation. The system monitors user actions, analyzes patterns, and feeds this information back into the TTL determination process. This closed-loop approach ensures that the deletion schedule continuously adapts based on actual usage, preventing both unnecessary retention and premature deletion.
2Device complexity
If a fixed time interval is used for user profile retention, then the system complexity is reduced, but storage space efficiency deteriorates due to unnecessary data retention
Solution Approach 1:
The system replaces the static fixed interval approach with a dynamic TTL mechanism that adapts to user behavior. By continuously adjusting the retention period based on actual user activity patterns, the system optimizes storage utilization without requiring complex manual intervention, achieving better space efficiency through automated adaptive management.
Solution Approach 2:
The system implements self-service by automatically monitoring user actions and adjusting TTL values without requiring external intervention. The profile management system autonomously analyzes usage patterns and makes intelligent decisions about data retention, eliminating the need for complex manual scheduling while improving storage efficiency.
3Productivity
If historical request data analysis is implemented for TTL determination, then the database operational efficiency is improved, but the system complexity increases
Solution Approach 1:
The system uses feedback from historical request data to continuously optimize TTL values. By analyzing user action patterns and feeding this information back into the TTL calculation, the system achieves high operational efficiency through data-driven decision-making, with the complexity managed through automated algorithms rather than manual processes.
Solution Approach 2:
The system performs self-service by automatically analyzing historical data and adjusting TTL values without external intervention. The automated analysis of user patterns and intelligent recalibration of retention periods achieves optimal database efficiency while keeping operational complexity manageable through algorithmic automation.
Data Source
AI summary
Systems and methods for dynamic user profile management are provided. One aspect of the systems and methods includes receiving, by a lookup component, a request for a user profile; computing, by a profile component, a time-to-live (TTL) refresh value for the user profile based on a lookup history of the user profile; updating, by the profile component, a TTL value of the user profile based on the request and the TTL refresh value; storing, by the profile component, the user profile and the updated TTL value in the edge database; and removing, by the edge database, the user profile from the edge database based on the updated TTL value.