Integration Flow Problem Detection and Auto-Categorization
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Solution Overview
Problem
In distributed computing environments, identifying and understanding the root cause of anomalies in integration flows between systems is inefficient and often requires manual intervention, leading to operational complexity and delayed resolution.
Innovation Solution
A mechanism using a moving time window framework to detect problems by comparing message processing states before and after events, creating problem objects, and employing auto-categorization through clustering and machine learning to assign categories to root causes, thereby reducing operational complexity and improving scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual problem detection and analysis is used in integration flows, then personal expertise can be applied to solve problems, but operational complexity increases and resolution time is delayed
Solution Approach 1:
The system performs preliminary automated analysis of integration flow problems by detecting anomalies, gathering contextual information, and generating root cause hypotheses before human intervention is needed. This preliminary action reduces the time required for human operators to diagnose and resolve issues while maintaining accurate problem detection through structured analysis frameworks.
2Productivity
If automated problem detection is implemented, then resolution time is reduced, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into distinct functional modules including anomaly detection components, information gathering modules, root cause analysis engines, and categorization systems. Each module performs a specific function in the problem detection workflow, which reduces overall system complexity by making each component manageable and independently maintainable while achieving high automated detection efficiency.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the integration flow and human operators. This intermediary automatically detects anomalies, gathers contextual information from multiple sources, and presents structured problem summaries to operators, thereby improving detection efficiency without requiring human operators to directly manage complex monitoring infrastructure.
3Loss of information
If comprehensive problem analysis is performed to identify root causes, then problem understanding is improved, but operational complexity increases
Solution Approach 1:
The system implements a universal problem analysis framework that handles multiple types of integration flow problems through a single standardized process. The root cause analysis engine uses generalizable techniques to analyze diverse anomaly types, gather contextual information from various sources, and generate categorized problem descriptions, thereby improving problem understanding without proportionally increasing system complexity.
Data Source
AI summary
A system and method to determine an event for an executing integration flow defining how messages are passed between a sender system and a receiver system; determine message processing states of messages of the integration flow before an event time; determine message processing states of messages of the integration flow after the event time; determine, based on an evaluation of failed message processing states for the messages before and after the event time, whether the event is associated with a problem; assign, in response to the determination that the event time is associated with a problem, one or more categories to the problem; and store a record of the assignment of the one or more categories to the problem.