Dominik Bork


Image
Associate Prof. Dipl.-Wirtsch.Inf.Univ.
Dr.rer.pol.

Dominik Bork

  • About:

    Dominik Bork is working as an Assistant Professor for Business Systems Engineering at TU Wien since July 2020. Prior to moving to TU Wien, he worked as a Postdoc at the University of Vienna. He received his Diploma in Information Science and his PhD (Dr. rer. pol.) from the University of Bamberg where he primarily worked on multi-view enterprise modeling and metamodeling.

    During his academic career, he was visiting researcher at and is up to date active collaborator with the University of Technology Sydney, the Instituto Tecnologico Autonomo de Mexico, the University of Pretoria, Stockholm University, and the Ecolé de Mines d’Albi.

    Dominik Bork is elected domain expert of the Special Interest Group on Modelling Business Information Systems of the German Informatics Society (GI).

  • Orcid: 0000-0001-8259-2297
  • Keywords: Conceptual Modelling, UML, Model Engineering, Artificial intelligence, object oriented software design, Enterprise Architecture, Process Engineering
  • Roles: Associate Professor

Publications

CM2KGcloud – An open web-based platform to transform conceptual models into knowledge graphs
Muhamed SmajevicSyed Juned AliDominik Bork

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Handle: 20.500.12708/191770; Year: 2024; Issued On: 2024-01-01; Type: Publication; Subtype: Article; Peer Reviewed:

Keywords: Artificial intelligence, Cloud platform, Conceptual modeling, Knowledge graph, Model transformation, Model-driven engineering
Astract: Semantic processing of conceptual models is a focus of research for several years, bridging the disciplines of knowledge-based systems, conceptual modeling, and model-driven software engineering. With Knowledge Graphs, this research area gained momentum. In this paper, we introduce CM2KGcloud, a generic and extensible Web-based platform for transforming conceptual models into Knowledge Graphs. The platform can work on models created by state-of-the-art metamodeling platforms (e.g., EMF, Papyrus, ADOxx) and transforms models created with them into standardized Knowledge Graph representations like GraphML, RDF, and OWL. CM2KGcloud can be used as a service and can be integrated into software systems by its exposed API.

Smajevic, M., Ali, S. J., & Bork, D. (2024). CM2KGcloud – An open web-based platform to transform conceptual models into knowledge graphs. Science of Computer Programming, 231, Article 103007. https://doi.org/10.1016/j.scico.2023.103007
Combining Textual and Graphical Modeling with Next Generation Frameworks
Adam LencsesDominik Bork

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Handle: 20.500.12708/197412; DOI: 10.34726/hss.2024.115082; Year: 2024; Issued On: 2024-01-01; Type: Thesis; Subtype: Diploma Thesis;

Keywords: blended modeling, GLSP, Langium, model server
Astract: Combining textual and graphical modeling i.e., representing textual models in the form of diagrams, has been a popular topic ever since in the field of model engineering. Most often modeling tools only provide users the possibility to create models either in textual form or in the form of a diagram, and the users have to decide upon initial creation of the model whether they would like to use a textual or a graphical model editor. So far, blended modeling tools combining both approaches have only been developed based on traditional frameworks e.g., Xtext and EMF. The next generation frameworks Langium and the Graphical Language Server Platform (GLSP) promote new opportunities such as increased modularity in architecture and deployment options, more flexibility in user interface design, web-based and cloud-friendly development possibilities, while eliminating the dependency to Java.This thesis aims to revisit and explore the topic of combining textual and graphical modeling with the next-generation frameworks Langium and GLSP. A concept for blended textual-graphical modeling based on these frameworks is developed, which utilizes a model service to jointly manage the textual and graphical editor’s underlying modification model. The concept considers that the graphical and textual editor must operate on the same model, simultaneous updates must be possible between the two editors and non-semantic information of the model must be maintained during updates of the model. The concept is realized as an artifact based on the Workflow language. An existing GLSP framework for the Workflow language providing graphical modeling is extended by a Langium language server providing textual modeling, and a model server handling model access, provision and updates between the textual and graphical editors.To evaluate the developed concepts and artifacts, the implemented solution concepts are instantiated by two UML use cases of the bigUML modeling tool: the package diagram and the class diagram. These two use cases are evaluated against the conceptualized requirements of blended textual-graphical modeling.

Lencses, A. (2024). Combining Textual and Graphical Modeling with Next Generation Frameworks [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.115082
Frontend-only browser-based modeling tools
David JägerDominik Bork

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Handle: 20.500.12708/198248; DOI: 10.34726/hss.2024.118520; Year: 2024; Issued On: 2024-01-01; Type: Thesis; Subtype: Diploma Thesis;

Keywords: Langium, GLSP, Web Modeling, Model-driven Engineering, Model Management, Metamodeling, Generator
Astract: In recent years, a shift from feature-rich Integrated Development Environments (IDE) to lightweight web clients could be observed. In order to be able to make that shift, for textual editors, the Language Server Protocol (LSP) has been created, while for graphical editors, an enhancement of the LSP has been introduced in the Graphical Language Server Platform (GLSP). However, the model management for graphical editors has still been handled by heavy-weight Java model servers. This includes both the creation of metamodels and the runtime handling of models.This thesis aims to make the next step toward web-based graphical model editors and shift the model management to a TypeScript-only technology stack. For this, the functionalities of the next-generation language framework Langium are explored and extended by a model server API. This enables model-oriented clients to access the Abstract Syntax Tree (AST), which is created by Langium and holds the current state of a model. Furthermore, a new TypeScript native grammar language is conceptualized to provide a TypeScript native solution to define metamodels.To combine the model server API with the TypeScript-based grammar language, a generator is created that sets up the entire model management component. Typically, in Java-based environments, the metamodel for the model management component is created using the EMF Ecore metamodel. Therefore, to ease the transition from Ecore to the TypeScript-based grammar language, a mechanism to create the TypeScript-based grammar definition from the Ecore metamodel is added to the implementation of the generator.The work in this thesis is evaluated in two parts: First, the TypeScript-based grammar language is evaluated by comparing it with the widely used Ecore metamodel. Second, two state-of-the-art modeling tools, which utilize GLSP, are rebuilt to evaluate the generator by creating the metamodel and model management using the generator’s capabilities.

Jäger, D. (2024). Frontend-only browser-based modeling tools [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2024.118520
5th Workshop on Artificial Intelligence and Model-Driven Engineering (MDE 2023)
Lola BurgueñoDominik BorkJessie Galasso-CarbonnelManuel Wimmer

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Handle: 20.500.12708/191915; Year: 2023; Issued On: 2023-12-22; Type: Publication; Subtype: Inproceedings; Peer Reviewed:

Keywords: Model-Driven Engineering
Astract: Model-driven engineering (MDE) and Artificial Intelligence (AI) have gained momentum in recent years, and the fusion of techniques and tools in the two domains paves the way for several applications. Such integrations—which we call MDE Intelligence—are bidirectional, i.e., MDE activities can benefit from the integration of AI ideas and, in return, AI can benefit from the automation and subject-matter-expert integration offered by MDE. The 5th edition of the Workshop on Artificial Intelligence and Model-driven Engineering (MDE Intelligence), held in conjunction with the IEEE/ACM 26th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2023), follows up on the success of the previous four editions, and provides a forum to discuss, study, and explore the opportunities offered and the challenges raised by integrating AI and MDE.

Burgueño, L., Bork, D., Galasso-Carbonnel, J., & Wimmer, M. (2023). 5th Workshop on Artificial Intelligence and Model-Driven Engineering (MDE 2023). In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) (pp. 559–561). IEEE. https://doi.org/10.1109/MODELS-C59198.2023.00093
EA ModelSet – A FAIR Dataset for Machine Learning in Enterprise Modeling
Philipp-Lorenz GlaserEmanuel SallingerDominik BorkJoao Paulo A. AlmeidaMonika Kaczmarek-HeßAgnes KoschmiderHenderik Proper

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Handle: 20.500.12708/191926; Year: 2023; Issued On: 2023-11-25; Type: Publication; Subtype: Inproceedings; Peer Reviewed:

Keywords: Data set, Enterprise architecture, Enterprise modeling, FAIR, Machine learning
Astract: The conceptual modeling community and its subdivisions of enterprise modeling are increasingly investigating the potentials of applying artificial intelligence, in particular machine learning (ML), to tasks like model creation, model analysis, and model processing. A prerequisite—and currently a limiting factor for the community—to conduct research involving ML is the scarcity of openly available models of adequate quality and quantity. With the paper at hand, we aim to tackle this limitation by introducing an EA ModelSet, i.e., a curated and FAIR repository of enterprise architecture models that can be used by the community. We report on our efforts in building this data set and elaborate on the possibilities of conducting ML-based modeling research with it. We hope this paper sparks a community effort toward the development of a FAIR, large model set that enables ML research with conceptual models.

Glaser, P.-L., Sallinger, E., & Bork, D. (2023). EA ModelSet – A FAIR Dataset for Machine Learning in Enterprise Modeling. In J. P. A. Almeida, M. Kaczmarek-Heß, A. Koschmider, & H. Proper (Eds.), The Practice of Enterprise Modeling : 16th IFIP Working Conference, PoEM 2023, Vienna, Austria, November 28 – December 1, 2023, Proceedings (pp. 19–36). Springer. https://doi.org/10.1007/978-3-031-48583-1_2


Teaching

Software Engineering
Semester: 2024W; Nr: 194.020; Type: VU; Hours: 4.0; Language: German; View on TISS


Projects

JSON-basierte, web-natives Modellierungsframework für Model-Diffing
Name: JSONVerse; Title: JSON-basierte, web-natives Modellierungsframework für Model-Diffing; Begins On: 2024-07-01; Ends On: 2025-01-31; Context: Austrian Research Promotion Agency (FFG); View Project Website

Towards Low-Code Business App Development - ER2CDS
Name: ER2CDS; Title: Towards Low-Code Business App Development - ER2CDS; Begins On: 2024-01-01; Ends On: 2024-12-31; Context: valantic Business Technology & Transformatio GmbH; View Project Website

Automatisiertes End-to-End-Testen von Cloud-basierten Modellierungswerkzeugen
Name: InnoScheckEclipsesource23; Title: Automatisiertes End-to-End-Testen von Cloud-basierten Modellierungswerkzeugen; Begins On: 2023-05-01; Ends On: 2024-04-30; Context: Austrian Research Promotion Agency (FFG); View Project Website

Diplomarbeitsbetreuung AI Readiness Assessment
Name: DA-EFS; Title: Diplomarbeitsbetreuung AI Readiness Assessment; Begins On: 2023-01-24; Ends On: 2024-01-23; Context: EFS Unternehmensberatung GesmbH; View Project Website

MFP 4.2 Advanced Analytics for Smart Manufacturing
Name: MFP 4.2; Title: MFP 4.2 Advanced Analytics for Smart Manufacturing; Begins On: 2022-10-01; Ends On: 2023-09-30; Context: CDP Center for Digital Production G; View Project Website

Digital Platform Enterprise
Name: DEMO; Title: Digital Platform Enterprise; Begins On: 2022-01-01; Ends On: 2024-12-31; Context: European Commission; View Project Website

Team

Business Informatics Group, TU Wien

Head


Team member

Henderik Proper

Univ.Prof. PhD

Professors


Team member

Dominik Bork

Associate Prof. Dipl.-Wirtsch.Inf.Univ.
Dr.rer.pol.

Team member

Christian Huemer

Ao.Univ.Prof. Mag.rer.soc.oec.
Dr.rer.soc.oec.

Team member

Gerti Kappel

O.Univ.Prof.in Dipl.-Ing.in
Mag.a Dr.in techn.

Team member

Henderik Proper

Univ.Prof. PhD

Visiting Scientists


Team member

Christiane Floyd

Hon.Prof.in Dr.in phil.

Team member

Johanna Barzen

Dr. phil.

Administration



Researchers


Team member

Syed Juned Ali

Univ.Ass. BSc MSc

Team member

Aleksandar Gavric

Univ.Ass. MEng. B.Eng.

Team member

Marion Murzek

Senior Lecturer Mag.a rer.soc.oec.
Dr.in rer.soc.oec.

Team member

Galina Paskaleva

Projektass.in Dipl.-Ing.in
Dipl.-Ing.in BSc

Team member

Marianne Schnellmann

Univ.Ass.in BSc MSc

Team member

Marion Scholz

Senior Lecturer Dipl.-Ing.in
Mag.a rer.soc.oec.

External Researchers


Team member

Markus Hafner

Gastwissenschaftl. MEng.



Team member

Marco Huymajer

Univ.Ass. Dipl.-Ing.