Network Change Impact Analysis With Anomaly-Filtered KPI Averaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for change impact analysis in telecommunications networks are inefficient, labor-intensive, prone to human error, and fail to account for anomalies, leading to inaccurate results and misinterpretation of network performance impacts.
Innovation Solution
A system and method for automated change impact analysis that allows users to select parameters through an interface, uses machine learning to identify and remove anomalies, and generates visualizations and summary reports to enhance data interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual methods are used for change impact analysis, then flexibility in customization is improved, but productivity and time efficiency deteriorate
Solution Approach 1:
The patent replaces manual mechanical data processing with automated computational systems. The impact analysis system automatically retrieves data, performs calculations, and generates reports without manual intervention in data crunching, while preserving user control over parameters and customization options through configured interfaces.
2Ease of operation
If manual data processing is used, then ease of operation is improved, but reliability and accuracy deteriorate due to human errors
Solution Approach 1:
The system performs self-service by automatically retrieving data from network elements, calculating impact metrics, and generating reports without requiring manual data extraction or computation. This eliminates human errors in data processing while maintaining ease of operation through user-friendly parameter selection and report generation interfaces.
3Ease of operation
If average percentage change is used as the primary measure, then ease of operation is improved, but measurement precision deteriorates due to anomaly influence
Solution Approach 1:
The patent introduces multiple impact measurement parameters beyond simple average percentage change. It calculates true average impact by excluding anomalies, and provides detailed breakdowns including anomaly counts and individual cell-level impacts, giving decision-makers more precise and nuanced measurement options while maintaining operational simplicity.
4Productivity
If automated processing is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary impact analysis system that sits between the network elements and decision-makers. This system handles the complexity of automated data retrieval, anomaly detection, and impact calculation internally, while presenting simplified results and parameter selection interfaces to users, thus improving productivity without exposing system complexity.
Data Source
AI summary
The present disclosure provides a system (108) and a method for implementing automated change impact analysis in a network, comprising displaying (302), by an interface (206), a selection menu for enabling a selection of a plurality of parameters by a user (102), presenting (304), based on the selection, a list of key performance indicators (KPIs) corresponding to the plurality of parameters within the interface (206), receiving (306) a user input to select one or more KPIs from said list of KPIs and a time period for processing, determining (308), by a processing engine (208), a true average impact data corresponding to each of the one or more selected KPIs, and displaying (310), by the interface (206), a visual representation of the true average impact data representing a change for each of the one or more selected KPIs for said time period.