CDS Performance Annotations Using ML-Based Name Generation
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Solution Overview
Problem
Existing systems require manual effort and lack consistency in generating performance annotations for Core Data Services (CDS) views and field names, which are crucial for ensuring compliance with guidelines and optimizing database performance.
Innovation Solution
An intelligent CDS development framework automates the generation of performance annotations and names for CDS views and fields, using machine learning algorithms to analyze database characteristics and user interactions, ensuring compliance with CDS guidelines and reducing manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual annotation and name generation are used for Core Data Services, then flexibility and adaptability to specific business needs are maintained, but time consumption and lack of consistency increase
Solution Approach 1:
The system enables self-service by automatically generating performance annotations and names for Core Data Services views and fields. The machine learning model analyzes database characteristics and user interactions to autonomously create compliant annotations without requiring manual intervention, thus resolving the contradiction between productivity and precision.
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated machine learning-based system. The neural network model processes database metadata and interaction patterns to generate annotations automatically, substituting human effort with an intelligent system that maintains both speed and consistency.
2Loss of time
If automated name generation is implemented, then time and effort requirements are reduced, but system complexity increases
Solution Approach 1:
The framework achieves multi-functionality by using a single machine learning model to handle multiple tasks: generating view names, field names, and performance annotations simultaneously. This universal approach reduces overall system complexity compared to having separate specialized systems, while still minimizing time loss.
Solution Approach 2:
The system performs preliminary actions by pre-training the machine learning model on historical data and patterns before actual annotation generation is needed. This pre-processing reduces the computational burden during runtime, making the automated system more efficient and less complex in practice.
3Manufacturing precision
If machine learning algorithms are used for name generation, then consistency and compliance with CDS guidelines are improved, but data processing requirements increase
Solution Approach 1:
The system applies local quality by tailoring the machine learning model to specific organizational requirements and data patterns. The model is fine-tuned on locally relevant data and guidelines rather than using generic training data, reducing the quantity of data needed while maintaining high compliance precision.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the machine learning model's training parameters and thresholds to optimize performance. By tuning hyperparameters and adaptation rates, the system achieves high guideline compliance with reduced data processing requirements, balancing precision and data volume.
Data Source
AI summary
A method, a system, and a computer program product for generating name recommendations in a core data services computing environment. A dataset for training a name data model is received. The name data model is configured for determination of a recommendation for one or more names in a plurality of names associated with one or more artifacts in a plurality of artifacts of a database management system. The name data model is trained using the received dataset and applied to generate one or more names associated with the one or more artifacts.