Mapper Automates Data Model Mapping with Semantic Distance
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Solution Overview
Problem
Existing systems face challenges in accurately interpreting and mapping non-compatible data to standard data models, leading to ambiguity and manual effort, with existing solutions like OPC-UA and AAS requiring manual interpretation and potentially inaccurate mappings due to lack of mapping information.
Innovation Solution
An information processing apparatus with a mapper that identifies matching or partially matching classes or properties in a standard data model, using a UI unit, extraction unit, mapping unit, and distance calculation unit to determine mapping candidates and automate the mapping process, even for data that does not conform to the standard model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation and mapping is used for non-compatible data, then mapping flexibility is maintained, but user burden increases and mapping accuracy decreases
Solution Approach 1:
The mapping unit automatically performs data mapping by detecting classes and properties from input data and matching them with the standard data model without requiring manual user intervention. The system self-services the mapping process by using the distance calculation unit to autonomously determine the best matches based on semantic similarity.
Solution Approach 2:
The patent replaces the manual mechanical process of interpretation and mapping with an automated information processing system. The mapper uses computational methods including distance calculation and semantic analysis to substitute human manual work in the mapping process.
2Productivity
If existing data is mapped without mapping information, then processing speed is maintained, but mapping accuracy deteriorates
Solution Approach 1:
The distance calculation unit pre-calculates and stores semantic distances between data elements and the standard data model before actual mapping occurs. This preliminary preparation enables the mapping unit to quickly retrieve and compare pre-computed distances, maintaining high processing speed while ensuring accurate mapping decisions.
3Ease of operation
If automated mapping is implemented, then user burden is reduced, but mapping accuracy may worsen without proper semantic analysis
Solution Approach 1:
The distance calculation unit provides feedback information in the form of semantic distance measurements to the mapping unit. This quantitative feedback allows the mapping unit to objectively evaluate and select the best mapping candidates, ensuring high mapping accuracy while maintaining automated operation without manual intervention.
Solution Approach 2:
The system transforms the qualitative assessment of data compatibility into a quantitative parameter (semantic distance). By changing the parameter from subjective judgment to objective numerical measurement, the system achieves both automation and high mapping accuracy through computable metrics.
Data Source
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AI summary
An information processing apparatus includes a mapper that determines a mapping candidate corresponding to a description included in the input data from among hierarchically structured classes or properties included in a standard data model. The mapper detects a class or a property that exactly matches or partially matches, with respect to a schema included in the input data and a content thereof, the content from a class hierarchical structure of the standard data model and a set of properties defined for a class, and determines a first mapping candidate based on the detected class or property.