Ontological Network Service Group Identification
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Solution Overview
Problem
Existing network traffic analysis methods struggle to dynamically identify and manage Business Process Application Service Groups within enterprise networks, which are constantly changing due to node reconfigurations and migrations, leading to inefficiencies in network management and performance monitoring.
Innovation Solution
A system utilizing an ontological structure and data collection agents to automatically create and update a network description, allowing for real-time analysis of connections and resource usage, and an administration console with a knowledge base and inference engine to identify and categorize Business Process Application Service Groups, even across different operating systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional network management methods are used to manually track and manage enterprise nodes, then network management can be performed with simple tools, but the ability to dynamically identify and manage Business Process Application Service Groups deteriorates as networks change
Solution Approach 1:
The system enables automatic self-identification of Business Process Application Service Groups through data collection agents that autonomously gather network data and inference engines that automatically analyze relationships. The system serves itself by continuously monitoring and adapting to network changes without requiring manual reconfiguration or intervention, thus achieving dynamic adaptability while maintaining manageable complexity through automation.
Solution Approach 2:
The patent replaces manual mechanical network management methods with automated electronic systems including data collection agents, knowledge bases, and inference engines. This substitution transforms the mechanical process of manually tracking nodes into an automated electronic analysis system that dynamically identifies service groups based on observed network relationships, significantly improving adaptability while the automation actually reduces operational complexity.
2Productivity
If manual methods are used to track node reconfigurations and migrations, then implementation can be simple, but network management efficiency and performance monitoring deteriorate
Solution Approach 1:
The system implements continuous feedback loops where data collection agents monitor network node relationships in real-time, feed this information to knowledge bases and inference engines, which then automatically update service group identifications. This feedback mechanism enables the system to automatically adapt to node reconfigurations and migrations, dramatically improving network management efficiency and performance monitoring while the structured feedback process actually simplifies complexity through systematic automation.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and analyzing network data before changes impact service delivery. Data collection agents proactively monitor node relationships, and inference engines pre-identify potential service group changes, allowing the system to adaptively reconfigure management views before actual network changes occur, thereby improving efficiency while maintaining manageable complexity through advance preparation.
3Measurement precision
If automated analysis systems are implemented to identify service groups, then dynamic identification capability improves, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments the complex analysis task into distinct functional components: data collection agents that gather raw network data, knowledge bases that store and organize information, and inference engines that perform relationship analysis. This segmentation allows each component to specialize in a specific function, improving the overall precision of service group identification while managing system complexity through modular architecture where each segment can be independently developed and maintained.
Solution Approach 2:
The system introduces intermediary elements including knowledge bases that mediate between raw network data and analysis results, and inference engines that act as intermediaries between data collection and service group identification. These intermediaries structure and process information in standardized ways, improving measurement precision by ensuring consistent analysis while actually reducing complexity by providing clear interfaces and abstraction layers between system components.
Data Source
AI summary
One or more business process application service groups may be categorized. An ontological definition of an enterprise network can then be analyzed to identify one or more structures within the enterprise network that correlate to the one or more categorized business process application service groups.