Parameterized Graph Modeling for Network Traffic Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional methods for analyzing network traffic data are inadequate for processing the vast, varied, and dynamic nature of digital communication data, leading to inefficiencies in relationship management and resource allocation.
Innovation Solution
A system for machine learning-based metadata collection and parameterized graph modeling from communication channels, which includes a communication interaction subsystem for analyzing network traffic data and a parameterized graph modeling subsystem for generating data traffic topography maps, along with an interaction assessment subsystem for determining network correlation indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis methods are used to process network traffic data, then the system complexity remains low, but the processing capability and analysis precision are insufficient for vast, varied, and dynamic data
Solution Approach 1:
The system segments the complex network traffic analysis into multiple specialized subsystems: a communication interaction subsystem for data collection, a parameterized graph modeling subsystem for structural analysis, and an interaction assessment subsystem for relationship evaluation. Each subsystem handles specific aspects of the data processing pipeline, enabling high-precision analysis of vast and dynamic network traffic while managing system complexity through functional decomposition.
2Productivity
If machine learning-based metadata collection and parameterized graph modeling are implemented, then the productivity and relationship management efficiency improve, but the device complexity increases
Solution Approach 1:
The system implements dynamic parameterized graph modeling that adapts to varying network traffic patterns and interaction types. The graph structure and analysis parameters are not fixed but dynamically adjusted based on the specific communication channels and interaction characteristics being analyzed, enabling high productivity across diverse scenarios while managing complexity through adaptive rather than static configurations.
Solution Approach 2:
The parameterized graph model serves as an intermediary representation layer between raw network traffic data and relationship management insights. This intermediate graph structure transforms complex, varied, and dynamic communication data into a standardized format that can be efficiently analyzed, thereby improving productivity while containing system complexity through abstraction.
3Loss of information
If comprehensive metadata collection and sentiment analysis are performed on network traffic data, then the information quality and relationship assessment accuracy improve, but the loss of time and processing resources increase
Solution Approach 1:
The system performs preliminary parameterized graph modeling and metadata collection during normal network operations, preparing interaction data and communication patterns in advance. This preliminary action enables rapid relationship assessment and analysis when needed, improving information quality while reducing processing time during actual relationship management tasks by having data pre-processed and structured.
Data Source
AI summary
Systems, computer program products, and methods are described herein for machine learning-based metadata collection and parameterized graph modeling from communication channels. The present disclosure comprises a communication interaction subsystem (CIS) configured to receive requests from a user input device to query network traffic data associated with a plurality of devices. The request comprises a factor set and a correlation criteria. The CIS analyzes the network traffic data based on at least the request and determines a subset of the plurality of devices based on at least the request. The system also comprises a parameterized graph modeling subsystem (PGMS) operatively coupled to the CIS, which is configured to generate a data traffic topography map associated with the subset of the plurality of devices and transmit control signals configured to cause the user input device to display the data traffic topography map.