JSON Data Validation via Schema Annotations
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Solution Overview
Problem
Manual validation of JSON data files in Fintech applications is time-consuming and tedious, requiring developers to handle and validate data structures, property names, and values, which is inefficient and prone to errors.
Innovation Solution
A system that validates JSON data files by generating an object model from schema files with injected validation behavior, using annotations to ensure compliance with a requirements contract, allowing for easy configuration and implementation of validation rules without extensive coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual validation logic is implemented by developers, then data validation can be performed, but the process becomes time-consuming and tedious
Solution Approach 1:
The patent applies preliminary action by generating validation logic automatically from schema definitions before runtime. The system pre-processes schema files to create validation rules and object models, so that when data validation is needed, the work has already been done in advance, eliminating the need for manual developer intervention during validation operations.
Solution Approach 2:
The system implements self-service by enabling automatic generation of validation logic from schema files without requiring developer coding. The framework automatically creates validation rules, generates object models, and performs data validation autonomously, making the validation process self-sufficient and removing it from the manual development workflow.
2Reliability
If manual validation logic is developed, then data compliance can be checked, but the complexity of implementation increases
Solution Approach 1:
The patent extracts validation logic generation from the manual development process. By separating the validation rule creation from developer coding and automating it through schema-based generation, the system removes the complex implementation burden from developers while maintaining comprehensive compliance checking capabilities.
Solution Approach 2:
The system introduces an intermediary layer between schema definitions and validation execution. This intermediary automatically generates object models and validation rules from schemas, serving as a mediator that translates high-level schema definitions into executable validation logic without requiring developers to manually bridge the gap.
3Manufacturing precision
If developers manually handle data validation, then specific validation rules can be applied, but productivity decreases
Solution Approach 1:
The validation system performs self-service by automatically generating and executing validation rules from schema definitions. This eliminates the need for developers to manually write validation code for each data structure, significantly improving productivity while maintaining precise validation through the automated rule generation process.
Solution Approach 2:
The system changes the parameter of validation implementation from manual coding to automated generation. By transforming validation rules into generated code from schema parameters, the system maintains precise control over validation behavior while dramatically increasing development productivity and reducing manual effort.
Data Source
AI summary
Implementations generally relate to validating data files. In some implementations, a method includes receiving at least one schema file, where the at least one schema file includes data fields. The method further includes adding annotations to the at least one schema file. The method further includes generating an object model based on the at least one schema file and the annotations. The method further includes receiving at least one data file. The method further includes validating the at least one data file based on the object model to ensure that the at least one data file complies with a requirements contract.