Abstract: The Internet of Things (IoT), personal and wearable devices, and continuous advances in data-gathering techniques have significantly increased the amount of relevant data that can be leveraged for innovative real-time, data-driven applications. Digital Twins (DTs) are virtual representations of physical objects which are fully integrated and in which the automatic data exchange occurs in a bidirectional way. DTs and big data are mutually reinforcing technologies since huge volumes of data representing the physical/virtual worlds are collected, transformed, and generated through models to aggregate value to the business. Modern DTs follow a five-component architecture, which includes a Data Management (DM) component that bridges a physical system, a mirrored virtual one, and services components. However, there is no clarity on the functionality required for the DM component. This work presents a Systematic Literature Review on DM issues and proposed solutions in the DT context. We analyzed DM under the big data value chain activities, highlighting key issues to be addressed: data heterogeneity, interoperability, integration, data search/discovery, and quality. In addition to surveying existing solutions for handling these issues, we contextualized them in the domain and function for which the DT was proposed, the type of data dealt with, and the technical infrastructure. The compilation of these solutions sheds light on the functionality of the DM component in a DT, trends, and opportunities.

Keywords: Data management, Digital twin, Big Data, Systematic Literature Review

Journal/Conference: Knowledge and Information Systems (KAIS)

    Authors: Jaqueline Bitencourt Correia, Mara Abel and Karin Becker

    Publication: https://link.springer.com/article/10.1007/s10115-023-01870-1

Thesis/Dissertation: PPGC, UFRGS, Porto Alegre. Masters.

    Author: Jaqueline Bitencourt Correia

    Advisor: Prof. Dr. Karin Becker 

    Publication: https://lume.ufrgs.br/handle/10183/255587