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International Fuzzy Systems Association World Congress

IFSA/NAFIPS 2019 2019: Fuzzy Techniques: Theory and Applications pp 589–600Cite as

Modeling Probabilistic Data with Fuzzy Probability Measures in UML Class Diagrams

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1000))

Abstract

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.

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Acknowledgment

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

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Jie Sheng, Li Yan & Zongmin Ma

Authors
  1. Jie Sheng
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  2. Li Yan
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  3. Zongmin Ma
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Corresponding author

Correspondence to Li Yan .

Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Louisiana at Lafayette, Lafayette, LA, USA

    Ralph Baker Kearfott

  2. Center for Computing Research (CIC), Instituto Politecnico Nacional, Ciudad de Mexico, Mexico

    Ildar Batyrshin

  3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Marek Reformat

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  5. Department of Computer Science, University of Texas at El Paso , El Paso, TX, USA

    Vladik Kreinovich

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Sheng, J., Yan, L., Ma, Z. (2019). Modeling Probabilistic Data with Fuzzy Probability Measures in UML Class Diagrams. In: Kearfott, R., Batyrshin, I., Reformat, M., Ceberio, M., Kreinovich, V. (eds) Fuzzy Techniques: Theory and Applications. IFSA/NAFIPS 2019 2019. Advances in Intelligent Systems and Computing, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-21920-8_52

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