| 1. |
- Hussein, Ahmed, et al.
(author)
-
Towards a Multi-Level Model of Enterprise Architecture Modeling Notations
- 2023
-
In: Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 2, ICEIS 2023. - : INSTICC. - 9789897586484 ; , s. 466-473
-
Conference paper (peer-reviewed)abstract
- Over the past few decades, the field of enterprise architecture (EA) has grown, and many EA modeling notations have been proposed. In order to support the different needs, the different notations vary in the element types that they provide in their metamodel. This abundance of elements makes it difficult for the end-user to differentiate between the various elements and complicates the model transformation between different EA model notations. Therefore, this research analyzes existing EA frameworks and their modeling notations and extracts common properties. First, we performed a literature review to identify common EA frameworks and their modeling notations. Second, based on the found notations’ concepts, we create a taxonomy based on their similarities that leads to a multi-level model of EA notations. Our results showed that The Open Group Architecture Framework, ArchiMate, Department of Defense Architecture Framework, and Integrated Architecture Framework are the most used EA frameworks. Those frameworks served as input for a multi-level model comprising the common concepts of the different modeling notations.
|
|
| 2. |
- Namgay, Phub, 1988-, et al.
(author)
-
FAIR Data by Design : A Case of the DiVA Portal
- 2023
-
In: Proceedings of the 25th International Conference on Enterprise Information Systems. - Prague, Czech Republic : SciTePress. - 9789897586484 ; , s. 160-167
-
Conference paper (peer-reviewed)abstract
- FAIR Data Principles is a guideline for making data and other digital objects findable, accessible, interoperable, and reusable. Thus far, the traction and uptake of the principle are primarily in the domain of bio and natural sciences. The knowledge gap is the application of the FAIR Data Principles in designing data repositories for FAIR data in the academic data ecosystem. This paper provides a critical insight into how the principle can be utilised as a paradigm to design data that embodies the tenets of FAIR Data Principles. We conducted a case study of the DiVA portal, an information repository and finding tool in Sweden, to explicate FAIR data by design. The portal scored high in a qualitative assessment against the 15 facets of FAIR DataPrinciples, as illustrated by the high density of green cells in the traffic light rating matrix (see Table 1). It indicates the robustness of data in the portal that is easy to share, find, and reuse. This study suggests practitioners operationalise FAIR Data Principles in their data repositories by design through systems and policies underpinned by the principle. It would enrich data governance and management for the back office and data experiences for end users. The study also advances the knowledge base on data management through a granular exposition of FAIR data by design.
|
|
| 3. |
- Raavikanti, Sashikanth, et al.
(author)
-
A Recommender Plug-in for Enterprise Architecture Models
- 2023
-
In: Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 2, ICEIS 2023. - : INSTICC. - 9789897586484 ; , s. 474-480
-
Conference paper (peer-reviewed)abstract
- IT has evolved over the decades, where its role and impact have transitioned from being a tactical tool to a more strategic one for driving business strategies to transform organizations. The right alignment between IT strategy and business has become a compelling factor for Chief Information Officers and Enterprise Architecture (EA) in practice is one of the approaches where this alignment can be achieved. Enterprise Modeling complements EA with models that are composed of enterprise components and relationships, that are stored in a repository. Over time, the repository grows which opens up research avenues to provide data intelligence. Recommender Systems is a field that can take different forms in the modeling domain and each form of recommendation can be enhanced with sophisticated models over time. Within this work, we focus on the latter problem by providing a recommender architecture framework eases the integration of different Recommender Systems. Thus, researchers can easily compare the performance of different recommender systems for EA models. The framework is developed as a distributed plugin for Archi, a widely used modeling tool to create EA models in the ArchiMate notation.
|
|
| 4. |
- Reiz, Achim, et al.
(author)
-
A proposal for an ontology metrics selection process
- 2023
-
In: Proceedings of the 25th International Conference on Enterprise Information Systems - (Volume 1). - : SciTePress. - 9789897586484 ; , s. 583-590
-
Conference paper (peer-reviewed)abstract
- Ontologies are the glue for the semantic web, knowledge graphs, and rule-based intelligence in general. They build on description logic, and their development is a non-trivial task. The underlying complexity emphasizes the need for quality control, and one way to measure ontologies is through ontology metrics. For a long time, the calculation of ontology metrics was merely a theoretical proposal: While there was no shortage of proposed ontology metrics, actual applications were mostly missing. That changed with the creation of NEOntometrics, a tool that implemented the majority of ontology metrics proposed in the literature. While it is now possible to calculate large amounts of ontology metrics, it also revealed that the calculation alone does not make the metrics useful (yet). In NEOntometrics alone, there are over 160 ontology metrics - a careful selection for the given use case is crucial. This position paper argues for a selection process for ontology metrics. It first presents core questions for identifying the underlying ontology requirements and then guides users to identify the correct attributes and their associated measures.
|
|
