Automated Root Cause Analysis for Telecommunications Networks
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Solution Overview
Problem
Current manual processes for root cause analysis in telecommunications networks are time-consuming and inefficient, requiring skilled engineers to manually analyze large amounts of data, which can take several days and are not consistently systematic, often requiring repetition due to lack of captured learning.
Innovation Solution
An automated system and method that utilizes an analytical workflow with a graphical user interface to guide users through root cause analysis, employing modules like top offender and custom correlation modules, and a self-learning machine to analyze key performance indicators and their components, breaking them down into hierarchical structures for efficient determination of root causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual root cause analysis processes are used by engineers, then analysis can be performed with human judgment and experience, but analysis time is extremely long (several days) and efficiency is low
Solution Approach 1:
An automated analysis system acts as an intermediary between the data warehouse and engineers. The system includes modules for automatically querying the data warehouse, performing correlation analysis on KPIs and their components, and generating root cause analysis results. This intermediary automates the time-consuming manual processes while preserving the analytical rigor needed for accurate root cause identification.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system. Instead of engineers manually querying data and performing correlation analysis, the system uses automated modules to query the data warehouse, analyze KPI relationships, and generate results. This substitution dramatically reduces analysis time from several days to much shorter periods while maintaining analytical accuracy.
2Adaptability or versatility
If manual analysis processes are used, then flexible analysis can be performed, but the process is not consistently systematic and requires repetition due to lack of captured learning
Solution Approach 1:
The system incorporates feedback mechanisms where analysis results and patterns are captured and stored. The automated modules learn from past analyses and use this knowledge to improve future analysis efficiency. The system feeds back learned patterns into the analysis process, reducing the need for repetitive manual analysis while maintaining systematic and consistent approaches.
Solution Approach 2:
The system performs preliminary actions by pre-configuring analysis modules, pre-defining KPI relationships, and pre-establishing correlation analysis frameworks. This preliminary setup enables the system to quickly perform systematic analyses without requiring engineers to re-establish the analytical framework each time, thereby improving consistency and reducing repetition.
3Measurement precision
If experienced engineers perform manual analysis, then high-quality root cause identification can be achieved, but the process is cost inefficient and requires skilled personnel
Solution Approach 1:
The system enables self-service root cause analysis by automating the entire analysis process. Instead of requiring experienced engineers to manually perform analysis, the system autonomously queries the data warehouse, performs correlation analysis on KPIs, and generates root cause identification results. This self-service capability maintains high-quality analysis while eliminating the need for expensive skilled personnel to perform routine analysis tasks.
Solution Approach 2:
The patent replaces the mechanical process of skilled engineers performing manual analysis with an automated computational system. The system uses algorithms and modules to perform correlation analysis and root cause identification, substituting human expertise with automated intelligence. This substitution reduces operational costs while maintaining or improving analysis quality and consistency.
Data Source
AI summary
A system in a telecommunications network includes a database including at least one metric, at least one module configured for analyzing the at least one metric, a graphical user interface configured for displaying the at least one module, and a processor configured for determining a root cause in the network.