Model Driven Engineering and Ontology Development

Devedić, Vladan; Djurić, Dragan; Gaevic, Dragan

doi:10.1007/978-3-642-00282-3

Cited by 15 publications

(6 citation statements)

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“…One possible knowledge base concept -amongst others -for representing knowledge are ontologies which consist of classes, relations, and axioms, and which provide " ... the basic structure or armature around which a knowledge base can be built' [33] and have useful characteristics as e.g. different fonus of representation, possibilities for knowledge sharing, or reasoning [34].…”

Section: A Knowledge Engineeringmentioning

confidence: 99%

Notice of Retraction: Role-based knowledge structures in Complex Adaptive Logistics Systems

Hülsmann

Schwientek

Cordes

et al. 2010

2010 IEEE International Conference on Advanced Management Science(ICAMS 2010)

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The goal of this paper is to provide a fundamental methodical approach for identifying actors in Complex Adaptive Logistics Systems to deduce knowledge that can be implemented into the decision structures of intelligent logistics objects. This serves as a basis for further simulation-based studies regarding the efficiency of the usage of autonomous cooperation-based technologies in logistics services. For this purpose, role theory is applied and connected to knowledge engineering, and the resulting methodology is critically discussed regarding its contributions and limitations.

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Section: A Knowledge Engineeringmentioning

confidence: 99%

Notice of Retraction: Role-based knowledge structures in Complex Adaptive Logistics Systems

Hülsmann

Schwientek

Cordes

et al. 2010

2010 IEEE International Conference on Advanced Management Science(ICAMS 2010)

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“…DevOps practices, model is still expected to hold an important role in a business and software-oriented engineering process (Babar et al, 2015). The use of the metamodel might be minimized in certain areas, but its usage pattern suggests the existence of communication and collaboration space where the model will persist, or even take a central role, such as envisioned by the modeldriven engineering approach (Gasevic et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…Since the adoption of flowchart (Gelinas, Sutton and Fedorowicz, 2004), the modelling approach has shifted on several occasions, from the structured programming paradigm (DeMarco, 1979), to object-oriented (Rumbaugh et al, 1991), and more recently towards service-oriented approach (Papazoglou and Georgakopoulos, 2003). The importance of modelling in software engineering is further strengthened by the emergence of model-driven engineering (Gasevic et al, 2009), in which model becomes the core element of an engineering.…”

Section: Introductionmentioning

confidence: 99%

A unifying structure of metamodel landscape

Yustianto

Doss

Suhardi

2019

Jnl of Modelling in Management

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Purpose -The modelling landscape experiences a rich proliferation of modelling language, or metamodel.The emergence of cross-disciplinary disciplines, such as enterprise engineering and service engineering, necessitates a multi-perspective approach to traverse the component from strategic level to technological aspect. This paper aims to find a unifying structure of metamodels introduced by academics and industries.Design/methodology/approach -A grounded approach is taken to define the structure by collating the metamodels to form an emerging structure. Metamodels were collected from a literature survey from several interrelated disciplines: software engineering, system engineering, enterprise architecture, service engineering, business process management and financial accounting.Findings -The result suggests seven stereotypes of metamodel, characterized by its label: goal, enterprise, business model, service, process, software and system. The aspect of "process" holds a central role in connecting all other aspect in the modelling continuum. Service engineering can be viewed as an alternative abstraction of enterprise engineering in containing the concepts of "business model", "capability", "value", "interaction", "process" and "software".Research limitations/implications -Metamodel collection was performed to emphasize on representativeness rather than comprehensiveness, in which old and unpopular metamodel were disregarded unless it offer unique characteristic not yet represented in the collection. Owing to its bottom-up approach, the paper is not intended to identify a gap in metamodel offering.Originality/value -This paper produces a structure of metamodel landscape in a graphical format to illustrate correlation between metamodels in which evolutive patterns of metamodel proliferation can be observed. The produced structure can serve as map in metamodel continuum.

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“…Ontology can be defined as "an explicit specification of a concept definition", which means that the concepts in a domain and the existing relationships among them are formalized in ontologies (Gruber, 1995). Thus, the common understanding about a domain is formalized in ontologies, mostly for the purpose of applying, sharing and exchanging information (Gašević, Djuric and Devedžic, 2009). Ontologies also enable integration of knowledge by unifying Databases, Data Warehouses, and knowledge bases vocabularies (Djellali, 2013).…”

Section: Ontologies and Semantic Reasoningmentioning

confidence: 99%

Applying process mining and semantic reasoning for process model customisation in healthcare

Detro

Santos

Panetto

et al. 2019

Enterprise Information Systems

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Process flexibility plays a key role in high variability environments, such as healthcare. In this type of environment, the process model needs to change some elements to adjust to specific sets of requirements. Thus, this paper proposes a process model customizing method based on ontology and process mining. The method proposed is applied in customizing process models for acute ischemic stroke treatment. During process model customization, the method provides decision-making support for users, thereby ensuring a structurally correct process customization and enabling improves patient treatment by means of recommendations.

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Model Driven Engineering and Ontology Development

Abstract: The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Cited by 15 publications

References 0 publications

Notice of Retraction: Role-based knowledge structures in Complex Adaptive Logistics Systems

Notice of Retraction: Role-based knowledge structures in Complex Adaptive Logistics Systems

A unifying structure of metamodel landscape

Applying process mining and semantic reasoning for process model customisation in healthcare

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