ML Forecasting IT Events and KPI Impacts
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Solution Overview
Problem
Current computing tools lack the ability to automatically correlate information technology (IT) events and incidents with their impact on organizational performance indicators (KPIs), preventing IT teams from prioritizing remediations based on organizational performance impacts.
Innovation Solution
A machine learning-based computing tool that trains models on historical IT and organizational process data to forecast IT events and KPI impacts, using correlation graphs to identify affected resources and generate remedial action recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional computing tools are used to monitor IT events and KPIs separately, then monitoring coverage is comprehensive, but the ability to correlate IT events with organizational performance impacts is lacking
Solution Approach 1:
The patent combines separate IT event monitoring and KPI monitoring systems into a unified system that processes both data types together. The event data processor and KPI data processor are integrated to jointly analyze correlations between IT events and organizational performance indicators, enabling comprehensive correlation analysis while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that bridge IT event data and KPI data. These models learn correlations from historical data and serve as mediators to predict KPI impacts from IT events, enabling the system to capture correlation information that would be difficult to obtain through direct observation alone.
2Productivity
If manual analysis of IT event impacts on KPIs is performed, then analysis accuracy can be maintained, but productivity and response time are reduced
Solution Approach 1:
The patent implements preliminary action by training machine learning models on historical IT event and KPI data before actual impact analysis is needed. The models learn correlation patterns in advance, enabling rapid prediction of KPI impacts when new IT events occur, thus achieving both high productivity and maintained accuracy without manual analysis during incident response.
Solution Approach 2:
The patent replaces manual analysis mechanisms with automated machine learning-based prediction systems. The machine learning models automatically process IT event data and generate impact forecasts on KPIs, substituting human analysts while maintaining or improving accuracy through consistent application of learned patterns and enabling much faster response times.
3Measurement precision
If comprehensive historical data is collected for analysis, then forecast accuracy is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data processing by training machine learning models on comprehensive historical data in advance. During actual forecast operations, the pre-trained models can quickly process new data and generate predictions, achieving high forecast accuracy from comprehensive historical data without incurring high processing time delays during critical incident response periods.
Solution Approach 2:
The patent applies partial action by using the trained machine learning models to process only the most relevant features of new IT event data during forecasting, rather than reprocessing all historical data. This approach maintains forecast accuracy by leveraging learned patterns while significantly reducing the computational time and resources required for real-time predictions.
Data Source
AI summary
Mechanisms are provided for forecasting information technology (IT) and environmental impacts on key performance indicators (KPIs). Machine learning (ML) computer model(s) are trained on historical data representing IT events and KPIs of organizational processes (OPs). The ML computer model(s) forecast IT events given KPIs, or KPI impact given IT events. Correlation graph data structure(s) are generated that map at least one of IT events to IT computing resources, or KPI impacts to OPs. The trained ML computer model(s) process input data to generate a forecast output that specifies at least one of a forecasted IT event or a KPI impact. The forecasted output is correlated with at least one of IT computing resource(s) or OP(s), at least by applying the correlation graph data structure(s) to the forecast output to generate a correlation output. A remedial action recommendation is generated based on the forecast output and correlation output.