Automated Correlated Anomaly Tagging for Real-Time Network Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing network monitoring systems struggle to efficiently process large amounts of real-time data for anomaly detection and management, particularly in wireless telecommunications networks, lacking effective automation and user-friendly tools for operators.
Innovation Solution
A method and system for automatized monitoring of network anomalies involving real-time data processing, tagging of correlated anomalies, and automatic assignment of tags with conditional or non-conditional identifiers, using machine learning for prediction and reconfiguration of anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of real-time network data are processed for anomaly detection, then detection capability is improved, but processing complexity and resource consumption increase
Solution Approach 1:
The patent segments the complex anomaly detection process into distinct functional modules: data collection module, data processing module, anomaly detection module, and reporting module. Each module handles specific tasks independently, reducing overall processing complexity while maintaining comprehensive anomaly detection capability across the network
Solution Approach 2:
The patent introduces an intermediary processing layer that aggregates and pre-processes raw network data before feeding it to the anomaly detection algorithm. This intermediary layer filters and structures data, reducing the computational burden on the detection system while preserving critical anomaly information
2Ease of operation
If automated anomaly detection tools are implemented, then operator workload is reduced, but system complexity increases
Solution Approach 1:
The system implements self-service automation where the anomaly detection system automatically collects data, processes information, detects anomalies, and generates reports without requiring manual operator intervention. The system serves itself by autonomously performing monitoring and analysis tasks, reducing operator workload while the modular design keeps system complexity manageable
Solution Approach 2:
The patent creates a universal monitoring platform that handles multiple network elements, data types, and anomaly scenarios through a single integrated system. This multi-functional approach consolidates various monitoring tasks into one system, reducing the need for multiple separate tools and simplifying operator interaction while maintaining comprehensive coverage
3Measurement precision
If comprehensive network monitoring is implemented, then anomaly detection accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent applies local quality by configuring monitoring parameters and detection sensitivity differently for various network elements and locations. Critical network components receive more intensive monitoring with higher detection accuracy, while less critical elements use standard monitoring levels, optimizing resource consumption across the entire network while maintaining high detection accuracy where needed
Solution Approach 2:
The system dynamically adjusts monitoring parameters such as sampling rates, detection thresholds, and analysis depth based on network conditions and anomaly risk levels. When anomalies are detected, the system increases monitoring intensity locally; during normal operation, it reduces resource consumption by using lower-intensity monitoring modes, thereby maintaining detection accuracy while optimizing resource usage
Data Source
AI summary
There is provided a method and system for automatized monitoring of anomalies during operation of a network, the method comprising receiving real-time network data describing operation of the network, processing the received real-time network data to detect anomalies, and post-processing anomalies to compute groups of correlated anomalies, configuring tags relating to anomalies, each tag belonging to a tag category and having an associated identifier, an associated label, a condition field allowing to define one or several associated condition(s), assigning tags among the previously configured tags to groups of correlated anomalies, comprising automatic assigning of conditional tags if the associated condition or conditions is validated for said groups of correlated anomalies. Optionally, for each group of correlated anomalies tagged with a tag having at least one associated action, the at least one action is applied.