Dynamic Hard Threshold Adjustment via User Feedback
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Solution Overview
Problem
Hard thresholds in cloud computing environments for monitoring time-series data are static, leading to increased likelihood of false positive and false negative alerts due to changes in data-generating entities over time.
Innovation Solution
Data-agnostic computational methods and systems that adjust hard thresholds based on user feedback, using feedback statistics to determine the quality and significance of alerts and iteratively adjust the thresholds to better represent user perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hard thresholds are used to monitor time-series data, then alerts can be generated to identify anomalies, but the likelihood of false positive and false negative alerts increases due to static thresholds not adapting to changes in data-generating entities
Solution Approach 1:
The patent implements dynamic threshold adjustment by continuously learning from user feedback. The system transitions from static hard thresholds to adaptive thresholds that evolve over time based on user interactions, allowing the monitoring system to adapt to changing patterns in data-generating entities while maintaining reliable anomaly detection
Solution Approach 2:
The system incorporates user feedback loops where user responses to alerts (confirming false positives or false negatives) are fed back into the threshold adjustment mechanism. This feedback drives continuous refinement of thresholds, improving alert accuracy by aligning them with user expectations and actual system behavior patterns
2Ease of manufacture
If static hard thresholds are used for monitoring, then the system is simple to implement, but the system cannot adapt to changes in data-generating entities over time
Solution Approach 1:
The system implements self-service through automated threshold adjustment based on user feedback. Instead of requiring manual threshold configuration and updates, the system automatically learns and adapts thresholds by processing user responses to alerts, reducing implementation complexity while maintaining high adaptability to changing conditions
3Measurement precision
If user feedback collection is implemented to improve alert quality, then threshold accuracy improves, but system complexity increases
Solution Approach 1:
The system uses feedback from user responses to alerts to continuously refine threshold precision. User confirmations of false positives and false negatives provide targeted information that directly improves measurement accuracy of anomaly detection, with the feedback mechanism integrated seamlessly into the existing alert workflow
Solution Approach 2:
The system adjusts threshold parameters dynamically based on accumulated user feedback. By changing threshold values in response to feedback patterns, the system achieves higher measurement precision for anomaly detection while managing complexity through parameter optimization rather than structural complexity
Data Source
AI summary
This disclosure is directed to data-agnostic computational methods and systems for adjusting hard thresholds based on user feedback. Hard thresholds are used to monitor time-series data generated by a data-generating entity. The time-series data may be metric data that represents usage of the data-generating entity over time. The data is compared with a hard threshold associated with usage of the resource or process and when the data violates the threshold, an alert is typically generated and presented to a user. Methods and systems collect user feedback after a number of alerts to determine the quality and significance of the alerts. Based on the user feedback, methods and systems automatically adjust the hard thresholds to better represent how the user perceives the alerts.