AI Incident Impact Dashboard Using Historical Traffic Patterns
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Solution Overview
Problem
Existing systems struggle to accurately determine the impact of incidents on computing components during the incident, including the expected length of impact, missed transactions, and affected components.
Innovation Solution
A computerized method using an AI engine that detects the start of an incident, analyzes transaction traffic patterns, and compares them to historical data to predict a running impact count of missed transactions, which is dynamically displayed on a dashboard.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional incident analysis methods are used, then analysis can be performed after incident resolution, but real-time incident impact determination is not achievable
Solution Approach 1:
The system performs preliminary actions by detecting incident start and identifying transaction traffic patterns in real-time during the incident, rather than waiting for incident resolution. This enables impact determination to occur concurrently with the incident, significantly reducing the time loss while maintaining measurement precision through AI-based pattern comparison with historical data.
2Productivity
If real-time incident impact analysis is implemented, then timely decision-making is enabled, but system complexity increases
Solution Approach 1:
The patent introduces an AI engine as an intermediary component that automatically compares identified transaction traffic patterns with historical data from a data warehouse. This intermediary handles the complex pattern recognition and comparison tasks, enabling real-time incident impact determination without requiring the entire system to become overly complex. The AI engine mediates between raw transaction data and impact analysis results.
3Measurement precision
If historical data comparison is performed, then accurate incident impact prediction is achieved, but data processing requirements increase
Solution Approach 1:
The system extracts only the relevant transaction traffic patterns from the stream of impacted transactions during the incident, rather than processing all transaction data. By identifying and extracting specific patterns that are comparable to historical data, the system achieves accurate incident impact prediction while minimizing computational resource requirements. The extraction focuses on pattern characteristics rather than individual transaction details.
Data Source
AI summary
A computerized method for determining impact of an incident, during the incident, using an AI engine is provided. A stream of transactions is received, and traffic patterns are generated over a predefined time period from the stream of transactions. The traffic patterns are compared with data stored in a historical data warehouse, the data being from a time period that is statistically similar to the current incident time period yet prior to the incident occurring. A running impact count of transactions is determined based on the comparison. The running impact count is dynamically updated as the incident is occurring and displayed in a dashboard. Thus, aspects of the disclosure provide a real-time assessment of the predicted impact of the incident.