Network Data Abstraction Layer for Unified 5G Service Provisioning
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Solution Overview
Problem
Current telecommunications systems face challenges in managing vast volumes of diverse data from 5G wireless networks, leading to siloed data management and a lack of a unified, integrated view of trusted data in real-time, which complicates data collection and integration for mobile network operators and mobile virtual network operators.
Innovation Solution
A tiered telecommunications system with data abstraction layers that include an infrastructure layer, data abstraction layer, and interface layer, enabling data abstraction, standardization, and real-time access without direct integration to physical sources, utilizing AI/ML models for data management and plug-and-play models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data warehouses, data lakes, and data meshes are used for data management, then data storage capacity increases, but data integration complexity and siloed systems increase
Solution Approach 1:
The patent introduces a data abstraction layer as an intermediary between data sources and consumers. This layer provides standardized interfaces and unified data models that simplify data integration while maintaining the storage capacity benefits of data warehouses, lakes, and meshes. The abstraction layer mediates between the complex underlying storage systems and the simplified access requirements of applications.
Solution Approach 2:
The data abstraction layer implements universal data access interfaces that work across multiple data sources and types. By creating a unified data model and standardized APIs, the system provides multi-functional access to diverse data repositories without requiring separate integration logic for each source, thereby reducing overall integration complexity.
2Speed
If direct integration to physical data sources is implemented, then data access speed increases, but system coupling and integration complexity increase
Solution Approach 1:
The data abstraction layer serves as a mediator that provides fast data access without direct coupling to physical sources. It maintains cached representations and standardized access paths that enable rapid data retrieval while isolating applications from the complexity of underlying data source configurations and physical locations.
Solution Approach 2:
The system segments the data access architecture into distinct layers: physical data sources, data abstraction layer, and application consumers. This segmentation allows each layer to be optimized independently - physical sources for storage, abstraction layer for fast standardized access, and applications for business logic - while reducing cross-layer coupling.
3Adaptability or versatility
If customized data integration solutions are developed for each customer, then service specificity increases, but development time and costs increase
Solution Approach 1:
The data abstraction layer provides universal, standardized data access interfaces and pre-built data models that can be applied across multiple customer scenarios. This universality enables the same core infrastructure to serve diverse customer needs through configuration rather than custom development, significantly reducing development time while maintaining service adaptability.
Solution Approach 2:
The system performs preliminary actions by pre-defining data models, schemas, and access patterns in the abstraction layer. These pre-configured structures are prepared in advance and can be quickly adapted to specific customer requirements through parameterization and configuration, eliminating the need to build integration logic from scratch for each customer.
Data Source
AI summary
Telecommunications systems, computer devices and systems, and methods are provided. One example method, performed by a data abstraction layer of a telecommunications system for a network, includes accessing data elements associated with a network function of the network, performing abstraction on the data elements to generate one or more data products, receiving a customer request for a service on the network, identifying the data product related the service, and provisioning the identified data product to the customer. The method may further include identifying proprietary data and private data of the data elements based on a predefined policy and removing proprietary data and private data before performing abstraction.