Knowledge Graph for Network Data Visualization
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Solution Overview
Problem
Obtaining network information from massive data in communication networks is inefficient, requiring manual querying of command lines.
Innovation Solution
A data processing method that generates a knowledge graph from network data, allowing users to visualize network entities and relationships, and identifies abnormal entities and root-cause faults, thereby improving efficiency and automating the inference of network fault root causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual querying of command line is used to obtain network information, then the process can be performed with simple tools, but the efficiency of obtaining network information is low
Solution Approach 1:
The patent creates a knowledge graph that copies and structures network data from command line outputs into a visual, queryable format. The management device extracts network data from command line queries and generates a knowledge graph that replicates the information in an accessible visual form, eliminating the need for manual command line querying while preserving all necessary network information.
Solution Approach 2:
The knowledge graph acts as an intermediary between the network data and the user. Instead of users directly querying command lines, the management device generates a knowledge graph that mediates the information retrieval process, allowing users to obtain network information through visual inspection or automated queries rather than manual command line operations.
2Productivity
If knowledge graph is generated to visualize network data, then the efficiency of obtaining network information is improved, but the device complexity increases
Solution Approach 1:
The management device is divided into distinct functional modules: a data processing module that extracts network data from command line outputs, and a knowledge graph generation module that constructs the visual representation. This segmentation allows each module to perform its specific function independently, making the overall system more manageable and maintainable despite the increased complexity.
Solution Approach 2:
The knowledge graph generation capability serves multiple functions: it visualizes network topology, displays network entities and relationships, enables automated fault detection, and provides a basis for various network analysis tasks. This multi-functionality justifies the added device complexity by providing a unified platform that replaces multiple separate tools and processes.
3Measurement precision
If automated fault detection is implemented using knowledge graph, then the precision of fault identification is improved, but the device complexity increases
Solution Approach 1:
The system implements automated feedback mechanisms where the knowledge graph continuously monitors network entities and their relationships. When changes or anomalies are detected in the network data, the system automatically updates the knowledge graph and identifies potential faults by analyzing deviations from expected states, providing continuous precision feedback without requiring manual intervention.
Solution Approach 2:
The knowledge graph is built and maintained in advance with complete network topology and entity relationship information. This preliminary structuring of data enables rapid and precise fault identification when issues occur, as the system already has the framework in place to immediately analyze and detect anomalies without needing to process raw data during the fault detection event.
Data Source
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AI summary
A data processing method and apparatus, and a computer storage medium are provided, and belong to the field of network technologies. A management device obtains network data of a target network (201), where the network data includes a network topology of the target network and device information of a plurality of network devices in the target network, and the device information includes one or more of interface configuration information, protocol configuration information, and service configuration information. The management device extracts a plurality of knowledge graph triplets from the network data (202), where each knowledge graph triplet includes two network entities and a relationship between the two network entities, and a type of the network entity is a network device, an interface, a protocol, or a service. The management device generates a knowledge graph of the target network based on the plurality of knowledge graph triplets (203). The knowledge graph of the target network can be used to visualize the network data. Therefore, efficiency of obtaining network information by a user can be improved.