Data Fusion Core of a Digital Twin of the Oil and Gas Industry
Abstract
Competitiveness in the oil and gas industry has required high technological investments for data-centric decisions. One of the trends is the Digital Twins, which make use of virtual spaces and advanced analytical services to monitor and improve physical spaces. A Data Fusion Core (DFC) interrelates these systems. The OSDU data platform is a multi-partner initiative to eliminate data silos in the oil ecosystem and leverage innovation through a data-driven approach. In this work, we analyze to what extent the OSDU data platform can meet the needs of a DFC implementation, with a focus on interoperability, integration, and data lineage.
Keywords:
Digital Twin, Data Fusion, Data Integration, Data Interoperability
References
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Omitola et al., T. (2012). Capturing interactive data transformation operations using provenance workflows. In Extended Semantic Web Conference, pages 29–42. Springer.
Sawadogo, P. and Darmont, J. (2021). On data lake architectures and metadata management. J. Intell. Inf. Syst., 56(1):97–120.
Schneider, T. and Simkus, M. (2020). Ontologies and Data Management: A Brief Survey. KI-Kunstliche Intelligenz, 34(3):329–353.
Simmhan, Y. L., Plale, B., and Gannon, D. (2005). A survey of data provenance in escience. SIGMOD Rec., 34(3):31–36.
Studer, R., Benjamins, V. R., and Fensel, D. (1998). Knowledge engineering: principles and methods. Data & knowledge engineering, 25(1-2):161–197.
The Open Group (2020). The open group guide osdu™ system concept. Technical report.
Wanasinghe et al., T. (2020). Digital twin for the oil and gas industry: Overview, research trends, opportunities, and challenges. IEEE Access, 8:104175–104197.
Xiao et al., G. (2018). Ontology-based data access: A survey. In Proc. of the Twenty-Seventh Intl. Conf. on Artificial Intelligence, IJCAI-18, pages 5511–5519
Published
2021-10-04
How to Cite
CORREIA, Jaqueline B.; ABEL, Mara; BECKER, Karin.
Data Fusion Core of a Digital Twin of the Oil and Gas Industry. In: BRAZILIAN SYMPOSIUM ON DATABASES (SBBD), 36. , 2021, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 343-348.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2021.17896.
