Automated Root-Cause Analysis for IT System Alerts
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Solution Overview
Problem
Current solutions fail to effectively analyze machine-generated textual data from diverse sources to determine root-causes of malfunctions in IT systems, leading to inefficiencies and missed alerts due to unstandardized data structures, human resource limitations, and the inability to correlate causality between different domain alerts.
Innovation Solution
A method and system that classify machine-generated textual data into statistical metrics, recognize incidence patterns, and correlate them to identify root-causes, generating alerts with the identified causes, using techniques such as time-proximity, order-based, and component-based correlation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine-generated textual data from multiple vendors is aggregated and reviewed manually by IT personnel, then comprehensive monitoring of IT systems is achieved, but the processing capacity is limited by human resources and productivity decreases
Solution Approach 1:
The patent replaces the mechanical system of manual human review with an automated computer-based processing system. The system automatically ingests, parses, and analyzes machine-generated textual data from multiple vendors, substituting human cognitive processing with algorithmic processing that has no inherent capacity limits.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between data generation and human decision-making. This intermediary automatically processes the raw data, identifies patterns and root causes, and presents synthesized findings to IT personnel, thereby extending processing capacity while maintaining comprehensive monitoring.
2Productivity
If machine-generated textual data is standardized and processed automatically, then processing speed and productivity improve, but the complexity of integrating data from multiple vendors with different structures increases
Solution Approach 1:
The patent implements a universal data processing framework that can handle multiple data structures and formats from different vendors through a single integrated system. The system performs multiple functions including ingestion, parsing, normalization, and analysis within one platform, eliminating the need for separate processing pipelines for each vendor.
Solution Approach 2:
The patent dynamically adjusts processing parameters and parsing rules based on the specific data source and format being processed. The system automatically adapts its behavior to handle different data structures, enabling standardized processing of heterogeneous data without requiring complex manual configuration for each vendor.
3Loss of information
If all machine-generated data is processed and analyzed, then complete visibility into IT system performance is achieved, but the volume of data overwhelms human analysis capability and detection of critical issues is delayed
Solution Approach 1:
The patent extracts and isolates only the most critical information and root cause indicators from the vast volume of machine-generated data. Rather than presenting all raw data to human analysts, the system automatically filters and extracts key findings, enabling complete information capture without overwhelming human processing capacity.
Solution Approach 2:
The patent performs preliminary automated analysis and pattern recognition before human review. The system pre-processes the data, identifies potential root causes, and prepares synthesized reports in advance, so that when IT personnel review the findings, the critical issues have already been detected and prioritized, eliminating detection delays.
4Measurement precision
If domain-specific expert analysis is performed for each domain (network, infrastructure, application), then accurate root-cause determination is achieved, but the overall complexity and resource requirements are amplified
Solution Approach 1:
The patent merges multiple domain-specific analysis capabilities into a single integrated system. Rather than requiring separate expert analyses for network, infrastructure, and application domains, the system combines these analysis functions into one unified platform that automatically correlates findings across all domains to determine root causes.
Solution Approach 2:
The patent applies asymmetric processing where different analysis depths and methods are applied to different data types and domains based on their specific characteristics. The system automatically adjusts its analysis approach for each domain while maintaining overall integration, achieving accurate root-cause determination without requiring uniform complex processing across all domains.
Data Source
AI summary
A method and system for determining root-causes of incidences using machine-generated textual data. The method comprises receiving machine-generated textual data from at least one data source; classifying the received machine-generated textual data into at least one statistical metric; processing the statistical metric to recognize a plurality of incidence patterns; correlating the plurality of incidence patterns to identify at least a root-cause of an incidence that occurred in a monitored environment; and generating an alert indicating at least the identified root-cause.