Automated Model Element Documentation From JSON Metadata
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Solution Overview
Problem
Data modeling applications lack the ability to provide information about the specific element properties of generated data models to consuming applications, leading to incorrect assumptions and erroneous conclusions.
Innovation Solution
A data modeling system that automatically generates a unified document with element properties, including data filters, aggregations, and calculated measures, using a parser and integrator service to parse metadata from JSON files, and integrates generative artificial intelligence (GenAI) for contextually relevant insights, enabling structured documentation and export in various formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data models are shared with consuming applications without element property documentation, then data access and model sharing are simplified, but incorrect assumptions about data meaning and usage occur leading to erroneous conclusions
Solution Approach 1:
The patent introduces an intermediary documentation system that bridges the gap between data model creators and consumers. This intermediary captures element property information (definitions, data types, relationships, constraints) and makes it available to consuming applications without complicating the core data sharing mechanism. The documentation acts as a mediator that preserves information while maintaining simplicity in data access.
2Reliability
If comprehensive documentation of element properties is provided, then user understanding and correct data usage improve, but system complexity and documentation generation overhead increase
Solution Approach 1:
The system performs preliminary action by automatically capturing and documenting element properties at the time of data model creation, rather than requiring manual documentation later. The documentation is generated upfront from metadata extracted from JSON files representing the data model, ensuring information accuracy while reducing future overhead.
Solution Approach 2:
The documentation system is self-service in that it automatically extracts element property information from the data model metadata without requiring manual intervention. The system uses parsers to read JSON files and generate documentation autonomously, reducing the burden on users while maintaining comprehensive information.
3Loss of information
If manual documentation of data model properties is performed, then information accuracy can be maintained, but time and resources spent on documentation increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of documentation creation with an automated computational system. Parsers and integrator services automatically extract element property information from JSON metadata files and generate documentation, substituting human manual effort with machine-based automation that is both faster and more consistent.
Solution Approach 2:
The system creates copies of element property information from the source JSON metadata files. Rather than manually recreating documentation from scratch, the system copies and transforms the existing structured data into documentation format, preserving accuracy while dramatically reducing time investment.
4Measurement precision
If detailed element property information is made available to consuming applications, then data analysis accuracy improves, but information security and data exposure risks increase
Solution Approach 1:
The patent applies local quality by providing different levels of information detail to different users or applications based on their needs and authorization. The documentation system can selectively expose element properties, showing comprehensive information to authorized users while limiting exposure for others, thus balancing analytical precision with security requirements.
Data Source
AI summary
Complex data models integrate information from diverse sources in data modeling server. The parser and integrator service in data modeling server accesses metadata describing data model and uses template for creating a document. Based on the template and the metadata, the parser and integrator service automatically generate a document that shows element properties of data models. This document serves as an abstract representation, visually illustrating the properties of elements within data models. The system facilitates interactive user input, enabling users to input prompts directed to a generative AI component. This AI processes the prompts, generating results seamlessly integrated into the automatically generated document. In essence, this scenario encapsulates a sophisticated approach to data modeling, where automated processes, guided by metadata and templates, generate insightful documents representing the properties of complex data models. User interaction with generative AI adds a dynamic layer to the process, enhancing the document with tailored insights.