Modeling Probabilistic Data with Fuzzy Probability Measures in UML Class Diagrams

  • Jie Sheng
  • Li YanEmail author
  • Zongmin Ma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1000)


Being a standard of the Object Management Group (OMG), the Unified Modeling Language (UML) has been applied to diverse domains. UML class diagram model is a conceptual data model and has been widely used for database design and information modeling. Information in real-world applications is often uncertain. To model and deal with uncertain data, various uncertain databases are pro-posed, including fuzzy ones and probabilistic ones. Also, there are few efforts in modeling fuzzy and probabilistic data in databases. But few efforts have been made on modeling uncertainty in conceptual data models. In this paper, we concentrate on modeling probabilistic data with fuzzy probability measures in the UML class diagram model. We introduce the semantics of fuzzy and probabilistic information into the UML class diagram model and extend several major constructs of UML class diagrams accordingly. We present the corresponding graph-ical representations of the extended UML class diagram model in the paper.



The work was supported in part by the National Natural Science Foundation of China (61772269 and 61370075).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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