Document Schema Mapping for Risk Report Aggregation
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Solution Overview
Problem
Existing tools fail to effectively merge and normalize risk assessment reports from different departments within an organization, leading to subjective assessments and loss of data integrity, as these reports often have varying categories and priorities, making it difficult to compare and analyze them accurately.
Innovation Solution
A software application that identifies and maps document schemas across different sources, normalizes values by adjusting weight factors, and formats reports for side-by-side comparison within a graphical user interface, allowing users to customize and track changes while maintaining data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If risk assessment reports from different departments are merged using existing tools, then the aggregation of data is achieved, but data integrity is lost and assessments become subjective
Solution Approach 1:
The system segments the risk assessment reports by identifying and separating different document schemas, categories, and data fields before merging. This allows each report to be processed according to its specific structure while maintaining the integrity of individual data elements during aggregation.
Solution Approach 2:
The system changes parameters by normalizing category names and data field structures across different reports. It adjusts weight factors for different categories and transforms varied data formats into a standardized structure, enabling reliable comparison while preserving the original meaning and integrity of the data.
2Adaptability or versatility
If risk assessment reports with varying categories and priorities are merged, then comprehensive coverage is achieved, but accurate comparison and analysis become difficult
Solution Approach 1:
The system creates a universal framework that can handle multiple document schemas, categories, and data structures from different departments. It establishes common data fields and standardized category names that work across all report types, enabling both comprehensive coverage and accurate comparison simultaneously.
Solution Approach 2:
The system introduces intermediary processing steps including schema identification, category normalization, and weight factor adjustment. These intermediary mechanisms act as mediators between diverse report formats and the final merged output, ensuring accurate comparison while maintaining adaptability to different categories.
3Ease of operation
If traditional programs are used to process unstructured risk assessment reports, then text-heavy data can be handled, but irregularities and ambiguities make understanding difficult
Solution Approach 1:
The system replaces traditional mechanical text processing approaches with intelligent document schema identification and automated mapping mechanisms. It uses computational methods to detect patterns, normalize categories, and resolve ambiguities in unstructured data, making the processing easier while reducing difficulties associated with irregularities.
Data Source
AI summary
Document schemas for a first document from a first data source and a second document from a second source are identified. The document schema includes a set of tags and data elements corresponding to the set of tags. Based on the identified document schema, the set of tags of the first document to the set of tags of the second document are mapped. Portion of the first document is formatted based on the mapped set of tags. The formatted portion of the first document is positioned parallel to corresponding portion of the second document. The formatted first document and the second document are merged then displayed on the computer device.