Alert And Incident Clustering for Proactive IT Problem Detection
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Solution Overview
Problem
Conventional problem detection methods in information technology environments are slow and ineffective, often detecting issues too late due to the challenges of processing voluminous and noisy machine-generated alerts, while human-generated incidents are typically low-volume and high-signal but not utilized effectively.
Innovation Solution
A proactive problem detection process that combines machine-generated alerts and user-specified incidents, mapping them to a common vector space, clustering similar elements, and determining relative priorities based on properties like service impact and incident weight to identify underlying problems more accurately and quickly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional problem detection methods are used, then the detection process is simple, but the detection speed is slow and problems are detected too late
Solution Approach 1:
The system performs preliminary actions by continuously analyzing alert data and incident data before problems fully manifest. It proactively identifies potential issues by detecting patterns and anomalies in advance, enabling early intervention before systems fail or performance degrades significantly.
Solution Approach 2:
The system transitions from traditional single-dimension alert monitoring to multi-dimensional analysis by combining alert data, incident data, and pattern recognition across multiple parameters. This dimensional expansion enables more comprehensive and faster problem detection.
2Measurement precision
If machine-generated alerts are processed individually, then the processing method is straightforward, but the data is voluminous and noisy making accurate detection difficult
Solution Approach 1:
The system merges alert data with incident data into a unified analysis framework. By combining these two data sources, the system creates a more comprehensive view that filters noise and highlights genuine problems, improving detection accuracy despite the large volume of data.
Solution Approach 2:
The system extracts meaningful signals from noisy alert data by identifying and isolating relevant patterns and anomalies. It separates significant problems from background noise through sophisticated analysis techniques.
3Quantity of substance
If human-generated incident data is used alone, then the data quality is high, but the volume is low limiting comprehensive problem detection
Solution Approach 1:
The system merges high-quality incident data with high-volume alert data, creating a synergistic effect where incident data provides validation and context while alert data expands coverage. This combination maintains the high signal quality of incident data while achieving comprehensive problem detection across the entire system.
4Loss of time
If traditional problem detection is used, then the implementation is simple, but the response time is delayed reducing effectiveness
Solution Approach 1:
The system performs preliminary analysis continuously in the background, preparing problem detections in advance so that when issues arise, they are already identified and can be addressed immediately, eliminating detection delays.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor system state and adjust detection parameters dynamically. This feedback loop enables rapid adaptation to changing conditions, reducing detection time while maintaining manageable complexity through automated adjustments.
Data Source
AI summary
In various embodiments, a process for proactive problem detection using alert data and incident data includes receiving machine-generated alerts of an information technology environment and receiving user-specified incidents of the information technology environment. The process includes combining the machine-generated alerts and the user-specified incidents into a combined group, clustering elements of the combined group into one or more component clusters, and determining a relative priority between the one or more component clusters based on one or more properties of corresponding elements belonging to the one or more component clusters.