Critical Event Management Software With User-Annotated Predictive Models
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Solution Overview
Problem
Existing critical event management (CEM) software systems lack advanced predictive and prescriptive capabilities to optimize response strategies and minimize disruption, despite improvements in centralization and user interface functionalities.
Innovation Solution
A computing system utilizing predictive models built from user-annotated closed critical events to identify patterns, generate visualizations, and provide predictive and prescriptive analytics through graphical user interfaces (GUIs) for managing critical events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional CEM software systems are used with basic centralization and GUI functionalities, then ease of operation is improved, but predictive capability and response optimization are insufficient
Solution Approach 1:
The system enables users to annotate critical events with attributes, and the system automatically uses these annotations to train predictive models. This self-service approach allows users to contribute to improving predictive capabilities without requiring manual model training, thus maintaining ease of operation while enhancing predictive capability.
Solution Approach 2:
The system implements a feedback loop where users annotate critical events, the system trains predictive models using these annotations, and then uses the models to predict outcomes for new critical events. This feedback mechanism continuously improves predictive accuracy while keeping the user interface simple and easy to operate.
2Reliability
If predictive models are built using user-annotated data, then predictive capability is improved, but system complexity increases
Solution Approach 1:
The system extracts only the necessary annotations from user input and stores them in an analytics table. The predictive models are trained separately from the main CEM software, isolating the complexity of model training and execution from the user-facing functionality. This allows the system to gain predictive capability without making the overall system overly complex for end users.
3Productivity
If automated predictive analytics are implemented, then response efficiency is improved, but loss of time for model training and data processing occurs
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing annotations from closed critical events in an analytics table. This data accumulation happens in the background during normal operations, so when a new critical event occurs, the predictive models are already trained on relevant historical data, enabling immediate prediction without significant time delay.
Solution Approach 2:
The system maintains continuous operation by continuously updating the analytics table with new annotations from closed critical events. The predictive models are trained continuously or periodically on this accumulating data, ensuring that when a new critical event occurs, the models are up-to-date and ready for immediate prediction, thus maintaining continuous improvement without significant interruptions.
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.