Unified Data Source Model for Heterogeneous Data Integration
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Solution Overview
Problem
Current data modeling tools face challenges in managing and visualizing complex, heterogeneous data sources with different structure types, limiting their ability to handle rapidly growing data model sizes and diverse data sources such as relational databases, NoSQL databases, and cloud services.
Innovation Solution
The method involves defining abstract sub-models without initial data source structures, creating connections between them, and generating a unified data source model that includes these sub-models with their respective data source structures, allowing for separate development and deployment across multiple systems and platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a modeling tool is designed to handle multiple heterogeneous data sources with different structure types, then the adaptability and versatility of the tool is improved, but the device complexity and difficulty of managing the model increases
Solution Approach 1:
The patent divides a large complex data model into multiple sub-models, each representing a specific data source or data domain. Each sub-model can be independently defined, managed, and validated. The modeling tool then integrates these sub-models into a unified data model through defined relationships, allowing the system to handle heterogeneous data sources without overwhelming complexity.
Solution Approach 2:
The patent introduces an intermediary layer that defines relationships between sub-models. This intermediary layer includes relationship definitions, connection rules, and integration logic that mediate between different heterogeneous data sources. This allows the system to manage complexity by providing a standardized interface layer between diverse data sources and the unified model.
2Ease of operation
If the data model is divided into multiple sub-models for separate development, then the ease of operation and parallel development is improved, but the difficulty of integrating and maintaining the unified model increases
Solution Approach 1:
The patent establishes relationship definitions and integration rules between sub-models in advance, before the actual data integration occurs. This preliminary action includes defining relationship types, connection patterns, and validation rules that will govern how sub-models are integrated. By preparing this integration framework beforehand, the patent reduces the complexity of actual integration and maintenance activities.
Solution Approach 2:
The patent implements validation mechanisms that provide feedback during the integration process. The system validates relationships between sub-models, checks for consistency, and provides error messages or warnings that guide the integration process. This feedback mechanism helps maintain data quality and reduces integration errors, making the overall process more manageable despite the complexity of integrating multiple sub-models.
3Adaptability or versatility
If abstract sub-models are defined without initial data source structures, then the adaptability to different data sources is improved, but the measurement precision and data accuracy decreases
Solution Approach 1:
The patent applies local quality by allowing each sub-model to have its own specific data source structure and validation rules tailored to its particular data type and source. While the overall unified model maintains abstract sub-models for adaptability, each local sub-model can enforce precise data accuracy requirements specific to its data domain. This allows the system to maintain high data accuracy locally while preserving overall adaptability.
Solution Approach 2:
The patent enables parameter changes by allowing the transformation of abstract sub-models into concrete data source structures when needed. The system can dynamically adjust the level of abstraction, transforming generic sub-models into specific data structures with precise validation rules when integrating with actual data sources. This parameter change capability allows the system to switch between adaptability mode and precision mode as needed.
Data Source
AI summary
A method of generating a data source model may include defining a first interface for a first abstract sub-model of a first sub-model responsive to user input and defining a second interface for a second abstract sub-model of a second sub-model responsive to user input. A connection may be defined between the first interface and the second interface. First and second data source structure may be respectively defined for the first and second sub-models. After defining the first and second data source structures, a unified data source model may be generated including the first and second sub-models and having the respective first and second interfaces of the first and second abstract sub-models, with the first and second sub-models being coupled through the connection defined between the respective first and second interfaces of the first and second abstract sub-models.