Scalable Data Platform Canvas for Reusable SDN Code Generation
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Solution Overview
Problem
Telecommunication networks, particularly 5G NR cellular networks, face challenges in managing vast amounts of data across distributed nodes, leading to inefficient data management, lack of code reusability among developers, and difficulties in ensuring data quality and accessibility, which hampers efficient data processing and decision-making.
Innovation Solution
A data platform with a scalable infrastructure provides a framework comprising a toolbox, policy, and canvas sections, enabling self-service data management, automation, and collaboration among developers to create, reuse, and improve data solutions, incorporating AI and ML capabilities for data processing and quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If developers create custom utilities and applications to solve specific problems, then problem-solving capability is improved, but code reusability and developer efficiency deteriorate
Solution Approach 1:
The patent implements a standardized framework with common utilities, data types, and processing functions that can be reused across multiple applications. The framework provides a universal set of building blocks (data connectors, processing functions, visualization components) that developers can combine to solve various problems without creating custom code from scratch, thereby improving both adaptability and productivity.
2Speed
If data is stored and processed at distributed network nodes, then data accessibility and processing speed are improved, but data management complexity and quality control deteriorate
Solution Approach 1:
The patent segments data management into standardized modular components distributed across network nodes. Each node manages its own local data using standardized protocols and interfaces, while the framework provides centralized coordination for data quality control and consistency. This segmentation enables fast local processing while maintaining manageable complexity through standardization.
Solution Approach 2:
The framework standardizes data parameters and formats across all nodes, transforming diverse data into uniform representations. By establishing common data types, validation rules, and processing parameters, the system maintains data quality and consistency across distributed nodes without requiring complex custom management at each node.
3Quantity of substance
If unstructured data is collected from diverse sources, then data volume and information richness are improved, but data parsing and analysis difficulty deteriorate
Solution Approach 1:
The patent introduces standardized data formats and processing functions as intermediaries between diverse data sources and analysis applications. The framework provides data connectors and transformers that automatically convert unstructured data from various sources into standardized internal representations, eliminating the need for custom parsing logic at each consumer and reducing overall complexity.
Data Source
AI summary
This disclosure relates to generating executable code using a data platform. One method includes presenting a graphical user interface (GUI) of a data platform associated with a software-defined network (SDN). The GUI includes a canvas, a toolbox area with one or more graphical objects each representing executable code to perform one or more functions in the SDN, and a policy area with one or more graphical objects each representing a set of one or more policy rules for governing how data is at least one of handled, stored, accessed, or protected. The method receives user input that causes graphical objects in the toolbox area and the policy area to move to the canvas. The method receives third user input that causes the data platform to generate output executable code based on the graphical objects in the canvas. The method outputs the output executable code.