A Metamodel for a DSL to Design Decision Support Systems in Smart Tourism
The development of Decision Support Systems (DSS) for several companies operating in sectors such as tourism, healthcare, or others, presents significant challenges due to the nature of their multi-component architecture. A typical DSS consists of a data component, a model component incorporating machine learning and deep learning techniques, and a user interface component, each one requires diverse technologies. This complexity increases development time and effort, hindering the efficient design of DSS tailored to specific domains like tourism, where real-time decision-making is essential. One promising approach to overcome these challenges is the use of Domain-Specific Languages (DSLs). DSLs allow developers to model and generate code more efficiently by providing high-level abstractions suited to the domain. By leveraging DSLs, it is possible to simplify the design process and automate the generation of code or parts of the DSS, thereby reducing the overall complexity and time required for development. This paper proposes a metamodel for designing DSS, specifically for the Smart Tourism sector. The metamodel serves as a foundation for the creation of DSL. The goal is to provide a solution to facilitate the development of DSS for the demands of tourism. The paper outlines the key concepts, approach, and results, presenting the first element of the proposed DSL which is the metamodel capturing the essential components and relationships for DSS in Smart Tourism.
Authors
- Adnane Souha (ORCID: https://orcid.org/0009-0007-4379-4575)
- Abdeslam Jakimi (ORCID: https://orcid.org/0000-0003-1425-3474)
- El Mahi Bouziane
- Lahbib Naimi (ORCID: https://orcid.org/0000-0003-2880-0432)
- Charaf Ouaddi
- Lamya Benaddi (ORCID: https://orcid.org/0009-0008-8928-6303)
Institutions
- Université Moulay Ismail de Meknes (MA)
Publication Details
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1109/dasa63652.2024.10836366
- Citations
- 6
- Primary Topic
- Semantic Web and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 2.08
Funders
- Ministry of Higher Education and Scientific Research