Canonical Data Model Dictionary Entry Name Generator
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Solution Overview
Problem
Diverse EDI data models limit interoperability and interchange of documents across different data communication systems, as they lack a universal standard for data element names, leading to heterogeneous representations and difficulties in processing documents across different technology platforms.
Innovation Solution
A microprocessor-implemented method and system that generates dictionary entry names for a canonical data model through linguistics and semantic analysis, counting frequency of occurrence, and validating terms by comparison with reliable entries in a database and search engine results to create consistent and unambiguous representations of data element names.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If diverse EDI data models are used for different data communication systems, then each system can maintain its own data format and structure, but interoperability and document interchange between different systems are limited
Solution Approach 1:
The patent introduces a canonical data model as an intermediary layer between diverse EDI data models. This canonical model serves as a universal representation that enables translation and mapping between different data communication systems, allowing documents to be exchanged without loss of information while maintaining compatibility with various platform-specific formats.
Solution Approach 2:
The canonical data model is designed to be a universal data representation that can serve multiple diverse EDI systems simultaneously. It provides a common framework that accommodates different data formats, structures, and semantics, enabling a single model to fulfill the needs of multiple heterogeneous systems through standardized mapping relationships.
2Reliability
If standardization approaches are used to facilitate interoperability, then document interchange between systems improves, but the semantics of interface and data models must be considered case-by-case in an ad hoc manner
Solution Approach 1:
The patent performs preliminary semantic analysis and learning during the development and deployment phase of the canonical data model. By pre-analyzing and understanding the semantics of diverse data models before interchange operations, the system establishes comprehensive mapping relationships in advance, eliminating the need for complex case-by-case semantic analysis during actual document exchange operations.
3Ease of manufacture
If ad hoc semantic analysis is performed for each interface, then specific document mappings can be achieved, but the process is piecemeal and not systematic
Solution Approach 1:
The canonical data model provides a universal mapping framework that can handle multiple document types and interfaces systematically. Instead of requiring separate ad hoc analysis for each interface, the unified canonical model enables consistent mapping rules to be applied across all data communication systems, reducing overall mapping complexity while maintaining comprehensive document interchange capability.
Data Source
AI summary
A method for building dictionary entry names for data elements of a canonical data model includes identifying candidate terms for the dictionary entry name of a node or equivalence class of the canonical data model. The method includes counting a frequency of occurrence of candidate terms in use and based on the use counts creating a candidate ordering of terms for the complete ordered dictionary entry name of the node or equivalence class. The method further includes validating the candidate ordering of terms for the complete ordered dictionary entry name of the node or equivalence class by comparison of the ordering with reliable dictionary entry name entries in a database and/or by usage counts in search engine results.