Digital twin: Data exploration, architecture, implementation and future.

A Digital Twin (DT) is a digital copy or virtual representation of an object, process, service, or system in the real world. It was first introduced to the world by the National Aeronautics and Space Administration (NASA) through its Apollo Mission in the '60s. It can successfully design a virtual object from its physical counterpart. However, the main function of a digital twin system is to provide a bidirectional data flow between the physical and the virtual entity so that it can continuously upgrade the physical counterpart. It is a state-of-the-art iterative method for creating an autonomous system. Data is the brain or building block of any digital twin system. The articles that are found online cover an individual field or two at a time regarding data analysis technology. There are no overall studies found regarding this manner online. The purpose of this study is to provide an overview of the data level in the digital twin system, and it involves the data at various phases. This paper will provide a comparative study among all the fields in which digital twins have been applied in recent years. Digital twin works with a vast amount of data, which needs to be organized, stored, linked, and put together, which is also a motive of our study. Data is essential for building virtual models, making cyber-physical connections, and running intelligent operations. The current development status and the challenges present in the different phases of digital twin data analysis have been discussed. This paper also outlines how DT is used in different fields, like manufacturing, urban planning, agriculture, medicine, robotics, and the military/aviation industry, and shows a data structure based on every sector using recent review papers. Finally, we attempted to give a horizontal comparison based on the features of the data across various fields, to extract the commonalities and uniqueness of the data in different sectors, and to shed light on the challenges at the current level as well as the limitations and future of DT from a data standpoint.