Digital Twin Replica Simulation for Completing Enterprise Data Models
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Solution Overview
Problem
Data management systems, such as Master Data Management (MDM) systems, face challenges in merging incomplete datasets with enterprise data due to technical and business or legal reasons, making it difficult to create a complete dataset for organizational use.
Innovation Solution
The method generates digital twin replicas based on data models to simulate missing data, which are then validated and combined with the original dataset using the relationships defined in the data model, allowing for the creation of a complete dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If digital twin replicas are generated to simulate missing data, then the completeness of the dataset is improved, but the complexity of the data management system increases
Solution Approach 1:
The patent creates digital twin replicas that are digital copies of physical objects or data entities. These replicas simulate the behavior and characteristics of the original entities, allowing the system to generate realistic simulated data without needing access to the actual physical objects or complete original datasets. This copying approach enables data completion while maintaining manageable system complexity through virtual representation.
Solution Approach 2:
The digital twin replicas act as intermediaries between the incomplete enterprise data and the missing information. Rather than directly accessing or inferring missing data from physical objects, the system uses these digital twins as mediating entities that can generate simulated data based on their programmed characteristics and relationships, simplifying the overall data completion process.
2Loss of information
If simulated data is generated and combined with original dataset, then the completeness of the dataset is improved, but the reliability of the data may deteriorate
Solution Approach 1:
The patent implements validation mechanisms that use the data model and relationships between entities to verify the consistency and quality of simulated data. The digital twins generate simulated data that is then checked against the defined data model constraints, relationships, and business rules. This feedback loop ensures that only reliable simulated data that maintains consistency with the overall data structure is combined with the original dataset.
Solution Approach 2:
The system performs preliminary validation and verification of simulated data before combining it with the original dataset. The digital twins are configured with predefined characteristics, relationships, and validation rules that ensure the generated simulated data meets quality standards and maintains consistency with the data model before integration occurs.
Data Source
AI summary
Computer hardware and/or software that perform the following operations: (i) receiving a data model, the data model including nodes representing types of information and edges representing relationships between the types of information; (ii) generating a set of digital twin replicas, where a digital twin replica of the set of digital twin replicas corresponds to a respective node of the data model; (iii) utilizing the set of digital twin replicas to generate simulated data corresponding to the types of information represented by the nodes of the data model; and (iv) combining the simulated data generated by the set of digital twin replicas into a combined set of simulated data based, at least in part, on the edges of the data model.