CloudIncidentForecaster is a state-of-the-art incident management solution crafted to predict, categorize, and manage incidents within cloud-based systems. Drawing insights from historical incident data, the project not only forecasts the probable locations of future incidents but also estimates their resolution duration, enabling more efficient and proactive incident management.
Data Source: The event log was sourced from the audit system of a ServiceNow platform instance utilized by an IT company and was enriched with data from a relational database.
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Goals:
- Predict the probable locations of upcoming incidents.
- Estimate the time required to resolve these incidents.
- Categorize incidents into meaningful groups to reveal patterns and streamline processes.
- Use Key Performance Indicators (KPIs) to measure and refine the system's predictive capabilities.
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Features:
- Real-time incident analytics.
- Predictive modeling using machine learning techniques.
- Interactive dashboard for monitoring, visualization, and insights.
- Alerting system for real-time incident notifications.
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Technologies Used:
- Cloud Providers: (TBD).
- Incident Analytics Tools: (TBD)
(TBD: Detailed steps on setting up the cloud environment, installing required tools, and libraries.)
(TBD: Guidelines on how to use the system, initiate forecasts, and interpret findings.)
We invite contributions from the community. Please consult the contributing guidelines for further details.
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