Attribute-Based Alert Monitoring for Time Series Anomalies
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Solution Overview
Problem
Setting up monitoring for alerts in large datasets is challenging and time-consuming, especially when determining which attribute values should be used to filter and monitor time series data for anomalies.
Innovation Solution
Anomaly detection systems recommend and automatically configure alert monitoring based on one or more attribute values, facilitating the selection and configuration of alert monitors for time series data using graph-based processes and trained models to detect anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual configuration of alert monitoring is performed, then monitoring accuracy can be ensured, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system automatically performs anomaly detection and configures alert monitoring without requiring manual user intervention. The anomaly detection system autonomously analyzes time series data, identifies anomalies using trained models, and sets up alert monitors based on detected anomalies, thereby eliminating the time-consuming manual configuration process while maintaining detection accuracy
Solution Approach 2:
The system pre-configures alert monitoring by automatically analyzing historical time series data and identifying potential anomalies before they occur. By performing preliminary anomaly detection and setting up monitoring based on predicted anomalies, the system prepares the monitoring framework in advance, reducing the time required for configuration when actual anomalies need to be monitored
2Reliability
If comprehensive monitoring of all attribute values is implemented, then anomaly detection coverage is improved, but system complexity and computational resources increase
Solution Approach 1:
The system extracts and focuses monitoring efforts on specific attribute values that are most relevant to detected anomalies. Instead of monitoring all attribute values comprehensively, the anomaly detection system identifies and extracts only the critical attributes associated with anomalies, thereby reducing configuration complexity and computational overhead while maintaining reliable anomaly detection coverage
Solution Approach 2:
The system applies different monitoring strategies to different attribute values based on their relevance to detected anomalies. Rather than applying uniform comprehensive monitoring to all attributes, the system tailors monitoring intensity and configuration to local characteristics of each attribute, optimizing resource allocation while ensuring reliable detection of anomalies in critical areas
3Productivity
If automatic anomaly detection is implemented, then productivity is improved, but the complexity of the detection system increases
Solution Approach 1:
The anomaly detection system is designed as a universal multi-functional platform that automatically performs multiple tasks including data analysis, anomaly detection, alert configuration, and monitoring. By consolidating these functions into a single automated system, the platform improves productivity by eliminating manual processes while managing complexity through integrated design rather than separate specialized components
Data Source
AI summary
Various embodiments described herein support or provide for alert monitoring of data (e.g., metric data) based on one or more recommended attribute values (e.g. dimension values), which can facilitate generation of alerts for the data based on detected anomalies. In particular, an embodiment can determine one or more recommended attribute values associated with data being analyzed for anomalies (e.g., metric data), select (or facilitate selection of) one or more of the recommended attribute values, configure alert monitoring for the data based on one or more selected attribute values, and enable the alert monitoring. Once enabled, the alert monitoring can trigger an alert in response to detecting one or more anomalies in the metric data associated with the one or more selected attribute values.