Critical Event Attribute Prediction for Faster Incident Response
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Solution Overview
Problem
Existing critical event management (CEM) software systems lack advanced predictive and prescriptive capabilities to efficiently manage and respond to new critical events, such as IT incidents and natural disasters, leading to suboptimal response times and resource allocation.
Innovation Solution
A computing system equipped with predictive models and pattern recognition algorithms that analyze historical critical event data to provide automated suggestions for responding to new events, including service-dependency graphs and user-selectable icons for action display, allowing users to view and implement recommended actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If predictive models and pattern recognition algorithms are implemented to analyze historical critical event data, then response efficiency and effectiveness are improved, but device complexity increases
Solution Approach 1:
The system segments the critical event management process into distinct functional modules: data collection module, pattern recognition module, predictive modeling module, and decision support module. Each module handles specific tasks independently, making the complex system manageable and maintainable while improving response efficiency through specialized processing.
Solution Approach 2:
The system performs preliminary analysis of historical critical event data to build predictive models before actual events occur. By pre-processing and analyzing past events to identify patterns and create response guidelines, the system prepares actionable insights in advance, reducing response time during actual critical events without requiring complex real-time computation.
2Loss of time
If automated predictive algorithms are used to determine suggested actions, then time to resolve critical events is reduced, but measurement precision requirements increase
Solution Approach 1:
The system incorporates feedback mechanisms where the outcomes of critical event responses are fed back into the predictive models. This continuous feedback loop allows the system to learn from actual performance and refine its predictions, improving accuracy over time while maintaining rapid automated response. The feedback mechanism adjusts model parameters based on real-world results, reducing the need for extremely precise initial measurements.
Solution Approach 2:
The system dynamically adjusts the precision requirements and model parameters based on the type and severity of the critical event. For high-stakes events requiring precise predictions, the system demands higher data accuracy, while for routine events it uses more tolerant parameters, thereby optimizing the balance between response speed and measurement precision requirements across different scenarios.
3Reliability
If service-dependency graphs and detailed analytics are displayed to users, then decision-making effectiveness is improved, but ease of operation decreases
Solution Approach 1:
The user interface segments critical information into hierarchical levels: executive summary view for high-level overview, detailed analytics view for in-depth analysis, and service-dependency graph view for relational understanding. Users can navigate between these segments based on their needs, providing comprehensive decision-making support while maintaining ease of operation through structured information presentation.
Solution Approach 2:
The interface dynamically adapts its complexity based on user interactions and event characteristics. For routine events, the system presents simplified summaries with key recommendations, while for complex events it automatically expands to show detailed analytics and service-dependency graphs. This dynamic adjustment maintains ease of operation by presenting only necessary information at appropriate levels of detail.
Data Source
AI summary
Analytics dashboards for critical event management systems that include artificial-intelligence (AI) functionalities, and related software. AI functionalities disclosed include pattern recognition and predictive modelling. One or more pattern-recognition algorithms can be used, for example, to identified patterns or other groupings within stored critical events, which can then be used to improve response performance and/or to inform the generation of predictive models. One or more predictive-modeling algorithms can be used to generate one or more predictive models that can then be used, for example, to make predictions about newly arriving critical events that can then be used, among other things, to provide optimal response performance and allow users to efficiently and effectively manage responses critical events. These and other features are described in detail.