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Affiliation

ARDITI - Regional Agency for the Development of Research, Technology, and Innovation, 9020-105, Funchal, Portugal

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2 Papers

Evaluating the Perceived Quality and Functionality of DEMO Models’ Representations in the Health Domain

Topics: Applications and Case-Studies; Enterprise Engineering; Enterprise Ontology; Domain Ontologies

Authors: David Aveiro ^{1

;

2

;

3} , Vítor Freitas ^{1

;

3} , Dulce Pacheco ^{1

;

2

;

4} and Duarte Pinto ¹

Affiliations: ¹ ARDITI - Regional Agency for the Development of Research, Technology and Innovation, 9020-105 Funchal, Portugal , ² NOVA-LINCS, Universidade NOVA de Lisboa, Campus da Caparica, 2829-516 Caparica, Portugal , ³ Faculty of Exact Sciences and Engineering, University of Madeira, Caminho da Penteada 9020-105 Funchal, Portugal , ⁴ School of Technology and Management, University of Madeira, Caminho da Penteada 9020-105 Funchal, Portugal

Digital Object Identifier: 10.5220/0012260400003598

Proceedings of KEOD 2023, ISBN: 978-989-758-671-2

Quick Abstract

Demo’s (Design and Engineering Methodology for Organizations) Way of Modelling encompasses a collection of interconnected models and diagrams designed to depict an organization’s structure and operations in a cohesive and platform-independent manner. Nevertheless, there has been a contention that the syntax and semantics of DEMO models are overly intricate and cluttered, posing challenges for laypeople in terms of interpretation. Our research team has been working on improvements to the DEMO Modelling language for Enterprise Ontology. Previous work had shown challenges in using standard DEMO notations for model communication and validation, prompting the development of new representations. This study evaluates these representations through quality and functionality testing using a health domain case and health professionals with domain knowledge. The results of the conducted tests reveal significant differences in perceived quality and functionality between the new and traditional DEMO representations. These findings indicate a strong preference for the new representations over traditional ones. This study underscores the importance of focusing on users in enhancing the effectiveness of modelling languages like DEMO, particularly in complex domains such as healthcare. The results suggest that these new representations have the potential to improve the perceived quality and functionality of DEMO models in various practical applications, including health-related information systems.

Ontology for a Georeferencing Mobile System for Real Time Detection and Monitoring of Wildfires

Topics: Ontology Engineering; Ontology Matching and Alignment; Ontology Sharing and Reuse; Enterprise Engineering; Enterprise Ontology; Domain Ontologies

Authors: Dulce Pacheco ^{1

;

2

;

3} , David Aveiro ^{1

;

3

;

4} , Valentim Caires ^{1

;

3} and Duarte Pinto ^{1

;

3}

Affiliations: ¹ ARDITI - Regional Agency for the Development of Research, Technology, and Innovation, 9020-105, Funchal, Portugal , ² School of Technology and Management, University of Madeira, Caminho da Penteada, 9020-105, Funchal, Portugal , ³ NOVA-LINCS, Universidade NOVA de Lisboa, Campus da Caparica, 2829-516, Caparica, Portugal , ⁴ Faculty of Exact Sciences and Engineering, University of Madeira, Caminho da Penteada, 9020-105, Funchal, Portugal

Digital Object Identifier: 10.5220/0011592100003335

Proceedings of KEOD 2022/IC3K, ISBN: 978-989-758-614-9

Quick Abstract

This paper presents the Georeferencing Mobile Wildfire Detection System Ontology (GeMoWilDSOn). This ontology served as a base for implementing software for a mobile and georeferencing system for real-time detection and monitoring of wildfires in steep mountainous territories. On average, about 65,000 fires occur in Europe annually, burning approximately half a million hectares of wild land and forest areas. This growing tragedy directly reduces the forest biomass and biodiversity, causing severe damage to the ecosystems. Ontologies help developers speed up the requirements’ analysis in the design of a new system. Our work results in a streamlined ontology focused on fire prevention and fighting with mobile sensors, automatically georeferenced polygon data, and visible and thermal image captures, specially designed for steep mountainous terrain, where firefighting can be complex. Our research fills gaps found in related state-of-the-art and provides innovative contributions such as the concepts of manually drawn areas of fire and shadow, which are of utmost importance regarding this particularity of steep terrain. Our ontology was validated in three real-world tests where experts were delighted with the features, captured information, and its representation in the GUI of the developed system.