Abstract
This paper presents a novel approach to the analysis of data with uncertain information. The classic approach of bivariate logic does not allow for the modeling of intermediate states, which are particularly important when studying phenomena characterized by uncertain information. The proposed methodology, based on assumptions of Łukasiewicz's logic, introduces a transformation of logical values to the set {-1, 0, 1}, which allows for an intuitive interpretation of truth, falsity, and lack of information. An unusual aspect of the approach is the use of a contiguity matrix, determined from the implication operator, to assess the degree of dependence between variables. The method was applied in the analysis of real-world data. The results confirm its effectiveness and efficiency in analyzing dependencies while accounting for uncertainty due to missing or ambiguous data, with a linear time complexity.
Paper Type
Full Paper
DOI
10.62036/ISD.2025.69
Multi-Valued Dependency Analysis Methodology. A Novel Approach to Modeling Uncertainty in Data
This paper presents a novel approach to the analysis of data with uncertain information. The classic approach of bivariate logic does not allow for the modeling of intermediate states, which are particularly important when studying phenomena characterized by uncertain information. The proposed methodology, based on assumptions of Łukasiewicz's logic, introduces a transformation of logical values to the set {-1, 0, 1}, which allows for an intuitive interpretation of truth, falsity, and lack of information. An unusual aspect of the approach is the use of a contiguity matrix, determined from the implication operator, to assess the degree of dependence between variables. The method was applied in the analysis of real-world data. The results confirm its effectiveness and efficiency in analyzing dependencies while accounting for uncertainty due to missing or ambiguous data, with a linear time complexity.
Recommended Citation
Mroczek, T., Matusiewicz, Z. & Skica, T. (2025). Multi-Valued Dependency Analysis Methodology. A Novel Approach to Modeling Uncertainty in DataIn I. Luković, S. Bjeladinović, B. Delibašić, D. Barać, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Empowering the Interdisciplinary Role of ISD in Addressing Contemporary Issues in Digital Transformation: How Data Science and Generative AI Contributes to ISD (ISD2025 Proceedings). Belgrade, Serbia: University of Gdańsk, Department of Business Informatics & University of Belgrade, Faculty of Organizational Sciences. ISBN: 978-83-972632-1-5. https://doi.org/10.62036/ISD.2025.69