Enriching enterprise architecture models with healthcare domain knowledge
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Author (Corporation)
Publication date
2023
Typ of student thesis
Course of study
Collections
Type
04B - Conference paper
Editors
Editor (Corporation)
Supervisor
Parent work
Advanced Information Systems Engineering Workshops. CAiSE 2023 International Workshops
Special issue
DOI of the original publication
Link
Series
Lecture Notes in Business Information Processing
Series number
482
Volume
Issue / Number
Pages / Duration
17-28
Patent number
Publisher / Publishing institution
Springer
Place of publication / Event location
Zaragoza
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
Enterprise architecture (EA) modeling gives an opportunity to have an overview of the enterprise architecture supporting business-IT alignment within the rapidly changing environment. Visual representation of enterprise architecture models is appropriate for interpretation by humans. Machines, however, cannot interpret labels associated with the model element, as well as its domain-specific concepts. To make EA models machine-interpretable, a graphical representation of models shall be connected to domain knowledge. This research demonstrates an approach to enriching the EA model of a medical institution with healthcare domain knowledge. Evaluation of the developed solution proves that a human and a machine could equally understand the ontology-based EA model.
Keywords
Subject (DDC)
Event
CAiSE 2023
Exhibition start date
Exhibition end date
Conference start date
12.06.2023
Conference end date
16.06.2023
Date of the last check
ISBN
978-3-031-34985-0
ISSN
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
Afonina, V., Hinkelmann, K., & Montecchiari, D. (2023). Enriching enterprise architecture models with healthcare domain knowledge. Advanced Information Systems Engineering Workshops. CAiSE 2023 International Workshops, 17–28. https://doi.org/10.1007/978-3-031-34985-0_2