Industrial Semantic Model Mapping for Siloed Legacy Data Access
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Solution Overview
Problem
In industrial infrastructures, service engineers face inefficiencies due to the need to manually investigate numerous siloed databases to address issues in complex legacy systems, leading to time-consuming diagnostic processes and requiring significant manpower and technical expertise.
Innovation Solution
An instantiated industrial semantic model is created by deriving data types from received data, mapping them to context models, and instantiating knowledge graphs, which automates data access and integration across heterogeneous data sources, using predefined knowledge models and similarity metrics to connect unrelated data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If service engineers manually investigate siloed databases to diagnose issues in legacy systems, then comprehensive diagnostic results can be obtained, but the diagnostic process becomes extremely time-consuming and requires significant manpower
Solution Approach 1:
The patent introduces an intermediary layer (semantic model and knowledge graph) between the service engineer and the siloed databases. This intermediary automatically queries, integrates, and processes data from multiple databases using predefined context models and relationships, delivering comprehensive diagnostic results without requiring manual investigation of each database.
Solution Approach 2:
The system enables self-service diagnostics by automatically performing data gathering, integration, and analysis without human intervention. The semantic model and knowledge graph autonomously query relevant databases, join data based on contextual relationships, and present diagnostic results, freeing service engineers from manual data collection tasks.
2Reliability
If customized queries are formulated for each legacy system to achieve comprehensive diagnostic results, then accurate diagnostics are obtained, but the complexity of the system increases significantly
Solution Approach 1:
The patent creates a universal semantic model and knowledge graph framework that can handle multiple legacy systems and database types through a single integrated approach. Instead of requiring customized queries for each system, the universal model uses standardized context models and relationships to automatically adapt to different legacy systems, reducing system complexity while maintaining diagnostic accuracy.
Solution Approach 2:
The system changes the parameters of data representation by transforming raw database data into semantic data with explicit relationships and contexts. This transformation allows the same diagnostic framework to work across different legacy systems by changing only the semantic model parameters rather than the entire query structure, thereby reducing complexity.
3Quantity of substance
If manual data gathering is performed across multiple siloed databases, then comprehensive data coverage is achieved, but the ease of operation deteriorates due to the complexity of navigating multiple databases
Solution Approach 1:
The semantic model and knowledge graph serve as an intermediary that automatically handles the complexity of navigating multiple siloed databases. Service engineers interact only with the unified semantic interface, while the intermediary performs all necessary data gathering, joining, and integration across databases, maintaining comprehensive data coverage while dramatically improving ease of operation.
Solution Approach 2:
The system creates a copied and simplified representation of the complex database structure through the semantic model and knowledge graph. Instead of requiring engineers to navigate the actual complex database structure, they interact with the copied semantic representation that presents data in an easily accessible, unified format while maintaining comprehensive coverage.
Data Source
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AI summary
Apparatus and method for providing an instantiated industrial semantic model, iISM, for an industrial infrastructure having data generation components, the apparatus (5) comprising: an interface unit (5A) adapted to receive from an industrial infrastructure (1) data generated by data generation components (3) provided within the industrial infrastructure (1), a context management unit (5B) adapted to derive types of data generation components (3) provided within the industrial infrastructure (1) depending on characteristics of the received data and adapted to map each derived type of data generation component (3) to an associated context model (CM) specified in the industrial semantic model, ISM, stored in a repository, and an instantiating unit (5C) adapted to instantiate the stored industrial semantic model, ISM, with predefined industrial instance models, IIMs, of data generation components (3) on the basis of the mapped context models to generate the instantiated industrial semantic model, iISM, of the industrial infrastructure (1).