Schema-Based Data Validation Across Formats With Audit Reporting
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Solution Overview
Problem
Existing data validation systems lack standardization, flexibility, and comprehensive auditing capabilities, leading to inconsistent validation, format restrictions, extensive coding efforts for rule updates, and difficulty in debugging and integrating across different platforms.
Innovation Solution
A system for automatic schema-based data validation that supports multiple data formats, allows dynamic configuration of conformity standards, performs comprehensive checks, and generates detailed logs and assessment reports for historical auditing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If custom validation logic is created for each data standard, then validation can be performed according to specific rules, but maintenance overhead increases and the system becomes platform-specific
Solution Approach 1:
The patent implements a universal validation framework that can validate multiple data standards (XML, JSON, CSV, etc.) using a common validation engine and schema language. This eliminates the need for separate custom validation logic for each standard, reducing maintenance overhead while maintaining validation accuracy across different platforms and data formats.
Solution Approach 2:
The patent introduces an intermediary schema language that acts as a mediator between data standards and validation logic. Instead of writing custom validation code for each standard, validators define rules in the schema language, which the validation engine automatically translates into executable validation logic, reducing both complexity and platform dependency.
2Ease of operation
If custom validation code is written for each application component, then validation can be performed locally, but code duplication occurs across the system
Solution Approach 1:
The patent creates a universal validation library that can be reused across all application components. Instead of each component having its own custom validation code, they all use the same validation engine with configurable schemas, eliminating code duplication while maintaining the ability to perform local validation.
Solution Approach 2:
The patent uses schema files as templates that can be copied and reused across different validation scenarios. Rather than copying validation logic code, the system copies schema definitions, which are then instantiated by the validation engine, reducing duplication while preserving local validation capability.
3Difficulty of detecting and measuring
If manual investigation is performed on files with large numbers of records, then root causes can be identified, but the process becomes infeasible and time-consuming
Solution Approach 1:
The patent implements automated feedback mechanisms that provide detailed validation results, error locations, and root cause analysis without manual investigation. The validation engine automatically generates comprehensive reports identifying specific records and fields that fail validation, along with suggestions for correction, making large-scale data validation feasible and time-efficient.
Solution Approach 2:
The patent replaces manual mechanical investigation with automated computational analysis. Instead of manually examining each record in large files, the validation engine automatically processes and analyzes all records, using algorithms to identify patterns, root causes, and anomalies, dramatically reducing investigation time while improving accuracy.
4Adaptability or versatility
If data schema changes are made, then the system can adapt to new requirements, but corresponding updates in custom code are required
Solution Approach 1:
The patent implements a dynamic validation system where schemas can be modified without changing the underlying validation engine or code. Schema files are stored in external formats that can be edited and reloaded, allowing the system to adapt to new requirements dynamically. The validation engine automatically picks up schema changes without requiring code updates or recompilation.
Data Source
AI summary
A method and a system for performing schema-based data validation are disclosed. The method includes receiving a first dataset that comprises a plurality of entries, the first dataset is received from at least one data source. Next, the method includes receiving a second dataset that comprises a predefined set of instructions corresponding to a set of conformity standards for validation of the first dataset. Next, the method includes applying the predefined set of instructions to the received first dataset. Next, the method includes validating each of the plurality of entries of the first dataset based on the applying of the predefined set of instructions. Thereafter, the method includes generating a consolidated assessment report based on the validation, and displaying, using a display unit, the generated consolidated assessment report.