Predictive Causal Analysis for Service Incident Root Cause Identification
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Solution Overview
Problem
In large federated service management platforms, determining the cause contributors of service incidents is challenging due to the complexity and interdependencies of services, leading to manual intensive investigations that do not scale well with the rapid growth of new applications and database structures, overwhelming traditional manual investigation processes.
Innovation Solution
A predictive causal analysis system using a machine learning model to generate a predictive causal probability score data object, identifying likely cause contributors among service changes and upstream service changes by analyzing service incident data, including time and risk assessment values, and providing a causal change analysis interface for decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual investigation processes are used to determine cause contributors of service incidents, then investigation accuracy can be maintained through human analysis, but the process does not scale well with rapid growth of new applications and database structures
Solution Approach 1:
The patent replaces manual mechanical investigation processes with an automated machine learning-based system. The predictive causal analysis system uses trained models to automatically identify cause contributors of service incidents, substituting human manual analysis with computational algorithms that can scale efficiently without increasing operational complexity.
Solution Approach 2:
The system enables self-service by automatically analyzing service incident data and generating predictive causal probability scores without requiring manual intervention. The machine learning model autonomously processes complex service network data, identifies patterns, and provides causal analysis results, allowing the system to serve itself rather than requiring continuous human investigation.
2Measurement precision
If manual investigation processes are used to identify cause contributors, then detailed analysis can be performed, but the time required increases significantly in complex federated service networks
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical service incident data before actual incidents occur. The models learn patterns and relationships from past data, enabling rapid prediction and identification of cause contributors when new incidents happen, eliminating the need for time-consuming manual analysis during the incident response.
Solution Approach 2:
The patent substitutes manual time-consuming analysis with automated machine learning inference. The trained models rapidly process service incident data and generate causal probability scores in seconds, replacing the slow manual investigation process while maintaining or improving identification precision through computational pattern recognition.
3Ease of operation
If traditional manual investigation processes are used, then human judgment can be applied to complex scenarios, but the process becomes overwhelming as service networks grow
Solution Approach 1:
The patent introduces an intermediary layer in the form of a machine learning model that mediates between the complex service network data and the analysis process. The model processes complex dependency relationships and service interactions, transforming them into simplified predictive causal probability scores that are easy to interpret and act upon, shielding operators from the underlying complexity.
Solution Approach 2:
The system replaces manual human analysis with automated machine learning mechanisms that handle complex service dependencies. The trained models automatically navigate through complex service graphs and dependency relationships, generating causal analysis results without overwhelming human operators with complex data structures.
Data Source
AI summary
Methods, apparatuses, or computer program products provide for generating a predictive causal probability score data object. A complex federated service network may be monitored to identify a service incident data object associated with a service incident. A predictive causal machine learning model may generate a predictive causal probability score data object based at least in part on a service incident time associated with the service incident data object. The predictive causal probability score data object may be output.