IT Anomaly Detection via Document Clustering and ML
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Solution Overview
Problem
Current anomaly detection in IT systems relies on manual monitoring, which is often inadequate and fails to accurately identify issues due to limited administrator knowledge and incomplete performance metrics, leading to undetected errors and potential system failures.
Innovation Solution
A method that generates vectors from documents about IT systems to represent words, clusters these vectors, and uses machine learning to identify features, training a model to automatically detect anomalies based on these features during system operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring is used for anomaly detection in IT systems, then device complexity is reduced, but reliability and measurement precision deteriorate due to limited administrator knowledge and incomplete performance metrics
Solution Approach 1:
The system automatically generates performance metrics and clusters documents without requiring administrator intervention. The machine learning model autonomously identifies anomalies by analyzing clustered document features, enabling the system to self-monitor and self-diagnose IT system issues without human expertise.
Solution Approach 2:
The patent replaces manual monitoring processes with automated machine learning-based anomaly detection. Instead of relying on human administrators to monitor system performance, the system uses computational algorithms to process documents, generate metrics, and identify anomalies automatically.
2Productivity
If manual monitoring is used, then ease of operation is improved, but productivity deteriorates due to inadequate detection capabilities and potential system failures going undetected
Solution Approach 1:
The system performs preliminary processing by automatically generating performance metrics and clustering documents before anomaly detection. This pre-processing organizes information in advance, enabling efficient and accurate anomaly identification without requiring complex real-time analysis during operation.
Solution Approach 2:
The patent introduces an intermediary layer of automated processing between data collection and anomaly detection. The machine learning model acts as a mediator that processes raw documents and metrics, transforming them into actionable anomaly insights, thereby bridging the gap between simple data collection and sophisticated analysis.
3Measurement precision
If automated machine learning-based monitoring is implemented, then reliability and measurement precision are improved, but device complexity and initial setup requirements increase
Solution Approach 1:
The system segments the monitoring process into distinct functional modules: document processing, performance metric generation, document clustering, and anomaly detection. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by dividing complex tasks into manageable units.
Solution Approach 2:
The machine learning model serves multiple functions within the system: it processes documents, generates performance metrics, clusters related information, and identifies anomalies. This multi-functionality reduces the need for separate specialized components, thereby reducing overall system complexity while maintaining high measurement precision.
Data Source
AI summary
A method includes generating a plurality of vectors representing words in a plurality of documents about an information technology (IT) system and clustering the plurality of vectors to produce a plurality of clusters. The method also includes identifying a cluster of the plurality of clusters that contains a plurality of clustered vectors, generating a feature based on a plurality of words represented by the plurality of clustered vectors, and training a machine learning model to identify an anomaly in the IT system based on the feature.