Automated XML Schema Generation from Relational Models
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Solution Overview
Problem
Existing methods for generating electronic document schemas from relational models are inefficient and lack automation, leading to inconsistencies and difficulties in ensuring repeatability and compliance with logical data models.
Innovation Solution
The process involves accessing complex type information from a logical relational data model, scrubbing it to remove foreign keys, and translating it into a hierarchical data model to generate an Enterprise Common Format (ECF) schema, which includes defining parent-child relationships and encapsulating common attributes, using a meta-language to automate the schema generation and ensure compliance with XML schema standards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to generate electronic document schemas from relational models, then flexibility and adaptability are maintained, but productivity is low and consistency is poor
Solution Approach 1:
The system enables self-service automation where the schema generation process operates autonomously by automatically accessing complex type information from the relational model, scrubbing it to remove foreign keys, translating it into hierarchical data model, and generating the final XML schema without requiring manual intervention at each step
Solution Approach 2:
The patent replaces manual mechanical operations with an automated computational system that uses software components to perform schema generation tasks, substituting human operators with an automated process that handles data extraction, transformation, and schema creation
2Reliability
If foreign keys are retained in child entities during schema generation, then relational integrity is maintained, but XML schema compliance and data integrity are compromised
Solution Approach 1:
The system extracts and removes foreign keys from child entities during the scrubbing process, separating the foreign key information from the child entity structure. This extraction eliminates redundancy while maintaining data integrity through the hierarchical parent-child relationship structure in the XML schema
Solution Approach 2:
The patent changes the structural parameters of the schema by transforming the relational model's foreign key references into hierarchical nesting structures. This parameter transformation converts flat relational references into nested hierarchical elements, optimizing the XML schema structure while preserving referential integrity
3Manufacturing precision
If automated scrubbing process is implemented to remove foreign keys, then schema compliance is improved, but processing time increases
Solution Approach 1:
The system performs preliminary scrubbing actions automatically during the schema generation process, pre-processing the complex type information to remove foreign keys before translation. This preliminary action ensures compliance is built-in from the start rather than added as a separate validation step
Solution Approach 2:
The patent implements continuous automated processing where the scrubbing, translation, and schema generation actions occur in an uninterrupted sequential flow. The system maintains continuous useful action by automatically progressing through each transformation step without idle time or manual intervention gaps
Data Source
AI summary
Methods and apparatus for generating a schema for data asset information are disclosed. In one example, complex type information corresponding to a logical relational data model that defines an organization of the data asset information is accessed. The logical relational data model includes a parent entity and child entities corresponding to the parent entity. Treating the complex type information produces scrubbed complex type information. The treatment of the complex type information includes, at least, the removal of foreign keys from child entities. The scrubbed complex type information is then translated to produce a hierarchical data model corresponding to the logical relational data model. A schema is then generated for the data asset information based upon the hierarchical data model.