Dynamic Refresh Session Signature for Data Coherence
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Solution Overview
Problem
Client devices often experience either excessive load on servers due to high refresh frequencies resulting in null updates or stale data due to low refresh frequencies, as the current systems do not effectively link refresh frequencies to actual user activity.
Innovation Solution
A method and system that determine refresh frequencies based on user activity by generating a session signature using detected user interactions, increasing refresh frequency during device usage and decreasing it during inactivity, thereby optimizing data coherence and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the client device requests updates at a high refresh frequency, then data coherence is improved, but server load increases due to null updates
Solution Approach 1:
The refresh frequency is made dynamic by adjusting it based on predicted user activity patterns. The system transitions from static periodic refreshing to dynamic adaptive refreshing, where the refresh interval changes according to the probability of user access at different times, thereby reducing unnecessary updates during low-probability periods while maintaining coherence during high-probability periods
Solution Approach 2:
The system performs preliminary analysis of user activity patterns and generates predictions about future user behavior. By anticipating when users are likely to access the service, the system proactively adjusts refresh timing to occur just before high-probability access periods, ensuring data is fresh without requiring continuous high-frequency updates
2Loss of energy
If the client device requests updates at a low refresh frequency, then server load is reduced, but data becomes stale
Solution Approach 1:
The system performs preliminary analysis of user activity patterns and generates predictions about future user behavior. By anticipating when users are likely to access the service, the system proactively adjusts refresh timing to occur just before high-probability access periods, ensuring data is fresh without requiring continuous high-frequency updates
Solution Approach 2:
The system continuously monitors actual user access patterns and uses this feedback to refine and update the predicted access probability model. This feedback loop allows the system to adapt to changing user behaviors over time, improving the accuracy of refresh timing predictions and optimizing the balance between data freshness and server load
3Device complexity
If the refresh frequency is based on time since last access, then implementation is simplified, but it does not accurately reflect actual user activity
Solution Approach 1:
The system changes the parameter used for determining refresh timing from a simple time-based metric to a probability-based metric that incorporates predicted user activity patterns. This parameter transformation allows the system to maintain simplicity in implementation while significantly improving the accuracy of refresh timing by using probabilistic models that capture actual user behavior patterns
Data Source
AI summary
A method of synchronizing data is provided. An embodiment of the method includes receiving, at a server, information identifying a detected user interaction with a device, determining, at the server, a probability that a user will access a service provided by the server using the device at a time, and generating, at the server, a session signature based on the determined probability, wherein the session signature is used by the device to generate refresh requests associated with the service.