Treffer: Designing a pattern language to enhance model composability and reusability: An example with component-based probabilistic models.

Title:
Designing a pattern language to enhance model composability and reusability: An example with component-based probabilistic models.
Authors:
Aly, Ebrahim1 (AUTHOR) e.aly@student.adfa.edu.au, Elsawah, Sondoss1,2 (AUTHOR), Turan, Hasan H.1,2 (AUTHOR), Ryan, Michael J.3 (AUTHOR)
Source:
Environmental Modelling & Software. Nov2023, Vol. 169, pN.PAG-N.PAG. 1p.
Database:
GreenFILE

Weitere Informationen

This paper presents a pattern language for developing Object-Oriented Bayesian Networks (OOBN), as a member of the component-based probabilistic models family, to tackle complex problems. The proposed pattern language integrates knowledge from various domains, such as modeling, software engineering, and Bayesian networks, to provide a comprehensive solution for developing OOBNs. The paper also provides a validation framework to evaluate the pattern language. As a practical application for the OOBN pattern language, a case study of using it to develop an OOBN is presented. The model in the case study aims to represent the complex interconnections among the Sustainable Development Goals (SDG), long-term sustainability and resilience. The results of the case study validate the effectiveness of the pattern language and highlight its potential for future applications. The proposed OOBN pattern language provides a systematic approach to the development of OOBN, reducing the complexity and increasing the efficiency of their modeling process • New pattern language for Object-Oriented Bayesian Networks. • The pattern language Integrates multidisciplinary knowledge to handle large networks. • Validation framework for language evaluation. • Case study models SDG, sustainability, and nation resilience. [ABSTRACT FROM AUTHOR]

Copyright of Environmental Modelling & Software is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)