MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information
There exist areas, such as the disease prevention or inclement weather protocols, in which the analysis of the information based on strict protocols require a high level of rigor and security. In this situation, it would be desirable to apply formal methodologies that provide these features. In this...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Graz University of Technology
2022-05-01
|
| Series: | Journal of Universal Computer Science |
| Subjects: | |
| Online Access: | https://lib.jucs.org/article/76030/download/pdf/ |
| _version_ | 1818250690271641600 |
|---|---|
| author | María Castañeda Mercedes G. Merayo Juan Boubeta-Puig Iván Calvo |
| author_facet | María Castañeda Mercedes G. Merayo Juan Boubeta-Puig Iván Calvo |
| author_sort | María Castañeda |
| collection | DOAJ |
| description | There exist areas, such as the disease prevention or inclement weather protocols, in which the analysis of the information based on strict protocols require a high level of rigor and security. In this situation, it would be desirable to apply formal methodologies that provide these features. In this scope, recently, it has been proposed a formalism, fuzzy automaton, that captures two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the models should be designed by domain experts, who have the required knowledge for the design of the processes, but do not have the necessary technical knowledge. To address this limitation, this paper proposes MODELFY, a novel model-driven solution for designing a decision-making process based on fuzzy automata that allows users to abstract from technical complexities. With this goal in mind, we have developed a framework for fuzzy automaton model design based on a Domain- Specific Modeling Language (DSML) and a graphical editor. To improve the interoperability and functionality of this framework, it also includes a model-to-text transformation that translates the models designed by using the graphical editor into a format that can be used by a tool for data anal- ysis. The practical value of this proposal is also evaluated through a non-trivial medical protocol for detecting potential heart problems. The results confirm that MODELFY is useful for defining such a protocol in a user-friendly and rigorous manner, bringing fuzzy automata closer to domain experts. |
| first_indexed | 2024-12-12T15:56:24Z |
| format | Article |
| id | doaj.art-c7ed36966e17448cbbc5d7500c0ecdf8 |
| institution | Directory Open Access Journal |
| issn | 0948-6968 |
| language | English |
| last_indexed | 2024-12-12T15:56:24Z |
| publishDate | 2022-05-01 |
| publisher | Graz University of Technology |
| record_format | Article |
| series | Journal of Universal Computer Science |
| spelling | doaj.art-c7ed36966e17448cbbc5d7500c0ecdf82022-12-22T00:19:29ZengGraz University of TechnologyJournal of Universal Computer Science0948-69682022-05-0128544547410.3897/jucs.7603076030MODELFY: A Model-driven Solution for Decision Making based on Fuzzy InformationMaría Castañeda0Mercedes G. Merayo1Juan Boubeta-Puig2Iván Calvo3Universidad Complutense de MadridUniversidad Complutense de MadridUniversity of CádizUniversidad Complutense de MadridThere exist areas, such as the disease prevention or inclement weather protocols, in which the analysis of the information based on strict protocols require a high level of rigor and security. In this situation, it would be desirable to apply formal methodologies that provide these features. In this scope, recently, it has been proposed a formalism, fuzzy automaton, that captures two relevant aspects for fuzzy information analysis: imprecision and uncertainty. However, the models should be designed by domain experts, who have the required knowledge for the design of the processes, but do not have the necessary technical knowledge. To address this limitation, this paper proposes MODELFY, a novel model-driven solution for designing a decision-making process based on fuzzy automata that allows users to abstract from technical complexities. With this goal in mind, we have developed a framework for fuzzy automaton model design based on a Domain- Specific Modeling Language (DSML) and a graphical editor. To improve the interoperability and functionality of this framework, it also includes a model-to-text transformation that translates the models designed by using the graphical editor into a format that can be used by a tool for data anal- ysis. The practical value of this proposal is also evaluated through a non-trivial medical protocol for detecting potential heart problems. The results confirm that MODELFY is useful for defining such a protocol in a user-friendly and rigorous manner, bringing fuzzy automata closer to domain experts.https://lib.jucs.org/article/76030/download/pdf/Model-driven engineeringFuzzy automataDSMLGr |
| spellingShingle | María Castañeda Mercedes G. Merayo Juan Boubeta-Puig Iván Calvo MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information Journal of Universal Computer Science Model-driven engineering Fuzzy automata DSML Gr |
| title | MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information |
| title_full | MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information |
| title_fullStr | MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information |
| title_full_unstemmed | MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information |
| title_short | MODELFY: A Model-driven Solution for Decision Making based on Fuzzy Information |
| title_sort | modelfy a model driven solution for decision making based on fuzzy information |
| topic | Model-driven engineering Fuzzy automata DSML Gr |
| url | https://lib.jucs.org/article/76030/download/pdf/ |
| work_keys_str_mv | AT mariacastaneda modelfyamodeldrivensolutionfordecisionmakingbasedonfuzzyinformation AT mercedesgmerayo modelfyamodeldrivensolutionfordecisionmakingbasedonfuzzyinformation AT juanboubetapuig modelfyamodeldrivensolutionfordecisionmakingbasedonfuzzyinformation AT ivancalvo modelfyamodeldrivensolutionfordecisionmakingbasedonfuzzyinformation |