Data Contextualization via Triplet Federation
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Solution Overview
Problem
Existing control systems and data management solutions struggle to effectively contextualize data from different sources with varying data models, leading to inconsistencies, inaccuracies, and difficulties in integrating data for comprehensive analysis.
Innovation Solution
A system that enables a client application to access data from different data sources and databases using different data models by generating triplets that link generic and custom data objects, allowing for contextualization and integration of data without modifying the client application code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard data models (ISA95, IEC61850) are used for data storage, then data consistency and interoperability are improved, but the system cannot accommodate customer-specific extensions and unique use cases
Solution Approach 1:
The system segments data models into standard data models (ISA95, IEC61850) for foundational consistency and customer-specific extended data models for unique requirements. This segmentation allows each model type to serve its specific purpose while maintaining overall system coherence through the federation mechanism.
Solution Approach 2:
The patent introduces a federation mechanism as an intermediary layer that links standard data models with customer-specific extended data models. This mediator enables data contextualization across different models without requiring modification of the standard models themselves, thus preserving both consistency and adaptability.
2Adaptability or versatility
If customer-specific extended data models are added to meet unique use cases, then system adaptability is improved, but data contextualization becomes difficult due to lack of federation between standard and custom models
Solution Approach 1:
The federation mechanism provides universal data contextualization capability that works across both standard and customer-specific extended data models. This multi-functional approach allows the same federation infrastructure to handle diverse data models without increasing contextualization complexity for each specific case.
3Loss of information
If data is contextualized from multiple sources, then data comprehensiveness and usefulness are improved, but data quality issues such as inconsistencies, missing data, and conflicting values arise
Solution Approach 1:
The system implements feedback mechanisms through the federation that continuously monitor and validate data from multiple sources. The federation compares data across standard and extended models, identifying inconsistencies and conflicts, and enables corrective actions to maintain data quality while achieving comprehensive data contextualization.
Data Source
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AI summary
An apparatus linked to a generic database (GDB) storing generic data relating to generic objects and to a custom database (CDB) storing custom data relating to custom objects, wherein the generic data are accessible from a client application (CA) and the custom data are not accessible from the client application, comprises: a collecting module (COL) configured to access a generic node comprising a generic attribute and an associated generic value included in the generic data relating to a generic object stored in the generic database and to access a custom node comprising a custom attribute and an associated custom value included in the custom data relating to a custom object stored in the custom database, a binding module (BIN) configured to generate a triplet comprising a generic reference of the generic object and a custom reference of the custom object and a relationship between the generic object and the custom object, when a relationship is determined between the generic object and the custom object, the binding module (BIN) configured to store the triplet in a persistent memory of the virtual database (VDB), an access module (ACC) configured to read the triplet in the virtual database and to give access to the client application (CA) to at least part of the generic data and to at least part of the custom data by means of the triplet.