2020/11/02 by Angira Sharma, Sharma, Angira, Edward Kosasih +8 · 15 citations
Computer Science · Engineering · #Big data #Blockchain Technology Applications and Security #Computer science #Data mining #Data science #Digital Transformation in Industry #Domain (mathematical analysis) #FOS: Computer and information sciences #Field (mathematics) #Implementation #IoT and Edge/Fog Computing #Machine Learning (cs.LG) #Open research #Software Engineering (cs.SE) #Software engineering #State (computer science) #The Internet #World Wide Web #cs.LG #cs.SE
paper · pdf · doi:10.48550/arxiv.2011.02833
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2020/11/02 · arxiv created 2020/12/04 · arxiv updated 2020/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation and forecasting. However, the theoretical framework and practical implementations of digital twins (DT) are still far from this vision. Although successful implementations exist, sufficient implementation details are not publicly available, therefore it is difficult to assess their effectiveness, draw comparisons and jointly advance the DT methodology. This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin. Advancements in machine learning, internet of things and big data have contributed hugely to the improvements in DT with regards to its real-time monitoring and forecasting properties. Despite this progress and individual company-based efforts, certain research gaps exist in the field, which have caused delay in the widespread adoption of this concept. We reviewed relevant works and identified that the major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics. We define the necessary components of a digital twin required for a universal reference framework, which also validate its uniqueness as a concept compared to similar concepts like simulation, autonomous systems, etc. This work further assesses the digital twin applications in different domains and the current state of machine learning and big data in it. It thus answers and identifies novel research questions, both of which will help to better understand and advance the theory and practice of digital twins.