Network Data Model Semantic Mapping via Label Computation
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Solution Overview
Problem
The network industry faces challenges in managing and operating networks due to data model diversity, with vendors having proprietary models and standard bodies maintaining common models, leading to a labor-intensive and error-prone process, as existing manual mapping methods are inefficient and prone to errors.
Innovation Solution
The implementation of an automatic mapping method using semantic matching, which involves computing labels and contexts for network data models, utilizing label computation algorithms and a lexical database to identify semantic relationships and reduce contexts, enabling dynamic mapping with minimal design-time cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual mapping methods are used for network data models, then mapping can be performed, but the process becomes labor-intensive and error-prone
Solution Approach 1:
The patent replaces manual mechanical mapping processes with an automated semantic matching system that uses computational algorithms to automatically identify and map relationships between network data models, eliminating the need for manual intervention in the mapping process
Solution Approach 2:
The system performs self-service by automatically discovering and establishing mappings between data models through semantic analysis, where the mapping system itself conducts the analysis and generates mappings without requiring external manual input or intervention
2Adaptability or versatility
If multiple vendor proprietary models and standard body models are used, then flexibility and adaptability are improved, but data model diversity creates complexity and difficulty in management
Solution Approach 1:
The patent introduces a semantic matching system as an intermediary layer between different vendor proprietary data models and standard body models, which automatically translates and maps between these diverse models, allowing the system to handle multiple data model formats without requiring manual intervention or complex manual mapping configurations
3Productivity
If semantic matching is performed to automatically map data models, then mapping efficiency is improved, but computational resources and processing complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-processing data models to extract and prepare semantic information before the actual mapping operation, including computing labels, determining contexts, and preparing metadata, which reduces the computational burden during the main mapping process and enables more efficient automatic mapping
Data Source
AI summary
In one embodiment, a method includes processing network data models at a network device operating in a network comprising a plurality of network components, each of the network components associated with one of the network data models, performing semantic matching at the network device for at least two of the network data models, the semantic matching comprising computing labels for elements of the network data models utilizing label computation algorithms configured for notational conventions used in the network data models, computing contexts for the elements based on a hierarchy of each of the network data models, removing one or more of the labels used to form the contexts to create reduced contexts, and computing a semantic relationship for the reduced contexts of the network data models. The network data models are mapped at the network device based on the semantic matching for use in a network application. An apparatus and logic are also disclosed herein.