Data management for developing digital twin ontology model

Abstract

Digital Twin (DT) is the imitation of the real world product, process or system. Digital Twin is the ideal solution for data-driven optimisations in different phases of the product lifecycle. With the rapid growth in DT research, data management for digital twin is a challenging field for both industries and academia. The challenges for DT data management are analysed in this article are data variety, big data & data mining and DT dynamics. The current research proposes a novel concept of DT ontology model and methodology to address these data management challenges. The DT ontology model captures and models the conceptual knowledge of the DT domain. Using the proposed methodology, such domain knowledge is transformed into a minimum data model structure to map, query and manage databases for DT applications. The proposed research is further validated using a case study based on Condition-Based Monitoring (CBM) DT application. The query formulation around minimum data model structure further shows the effectiveness of the current approach by returning accurate results, along with maintaining semantics and conceptual relationships along DT lifecycle. The method not only provides flexibility to retain knowledge along DT lifecycle but also helps users and developers to design, maintain and query databases effectively for DT applications and systems of different scale and complexities.

Digital twins and hybrid modelling for simulation of physiological variables and stroke risk

“…If the digital twin models should become a ready-to-use eHealth technology, additional work needs to be done, for example concerning health informatics and end-usage design. There is some, but not much, previous work that focus on data management concerning digital twins (36). Some challenges mentioned include data heterogeneity, the potentially large size of data, the fact that the data is dynamic, i.e.…”

“…Some challenges mentioned include data heterogeneity, the potentially large size of data, the fact that the data is dynamic, i.e. changes over time, and the related data mining problems that these challenges give rise to (36).…”

A blockchain-based cooperative modeling method for digital twin ontology model of the mechanical product

“…In order to better express the multidisciplinary and multi-domain coupling and interaction of mechanical products, ontology as a general knowledge representation method, its application in digital twin modeling has become one of the current research directions. For example, Singh et al [6] transformed domain knowledge into a minimal data structure through ontology, thereby mapping, querying and managing digital twin databases. Bao et al [7] proposed a new concept of product information unit based on ontology, and studied the modeling method of multisource heterogeneous data.…”

Platology: A Digital Twin Ontology Suite for the Complete Lifecycle of Infrastructure

“…DTs are essentially nourished by bidirectional interaction between the physical and virtual spheres and therefore establish new possibilities. Data management is decisive for the development and maintenance of any information system, DT being one such concept requires data management to address challenges such as data variety, data mining, and DT dynamics between the virtual and physical spaces (Singh et al, 2021). Ontologies as the basis for the application of semantic web and linked data technologies are a well-established approach for leveraging data and information sources (Zhong et al, 2015).…”