University of Southern Denmark
The University of Southern Denmark (Danish: Syddansk Universitet) is with around 30,000 students and 4,000 employees Denmarks third largest university. The university is allocated on six campi with its main campus in Odense.
Within the Maersk Mc-Kinney Moller Institute of the Technical Faculty, Simon Hacks is researching on the conceptualization of EA Debts and EA Smells as well as developing tool support to automatize the analysis.
Recent Publications
Hacks, Simon; Jung, Jürgen
A First Validation of the Enterprise Architecture Debts Concept Inproceedings Forthcoming
In: Bork, Dominik; Proper, Henderik A. (Ed.): Forthcoming.
@inproceedings{nokey,
title = {A First Validation of the Enterprise Architecture Debts Concept},
author = {Simon Hacks and Jürgen Jung},
editor = {Dominik Bork and Henderik A. Proper },
year = {2023},
date = {2023-06-12},
urldate = {2023-06-12},
journal = {International Working Conference on Exploring Modeling Methods for Systems Analysis and Development},
abstract = {The Enterprise Architecture (EA) discipline is now established in many companies. The architectures of these companies changed over time. They resulted from a long creation and maintenance process containing processes and services provided by legacy IT systems (e.g., systems, applications) that were reasonable when they were created but might now hamper the introduction of better solutions. To handle those legacies, we started researching on the notion of EA debts, which widens the scope of technical debts to organizational aspects. However, no studies have yet been conducted to validate if the concept of EA debts has a positive influence. Within this work, we have experimented with students of an EA course. Half of the students were taught the concept of EA debts, while the other half was taught about another topic simultaneously. Afterward, the students performed a modeling task graded by EA experts among the criteria of effectiveness, comprehensibility, minimality, and completeness. The analysis revealed no significant difference between the quality of the created models by the different student groups.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
The Enterprise Architecture (EA) discipline is now established in many companies. The architectures of these companies changed over time. They resulted from a long creation and maintenance process containing processes and services provided by legacy IT systems (e.g., systems, applications) that were reasonable when they were created but might now hamper the introduction of better solutions. To handle those legacies, we started researching on the notion of EA debts, which widens the scope of technical debts to organizational aspects. However, no studies have yet been conducted to validate if the concept of EA debts has a positive influence. Within this work, we have experimented with students of an EA course. Half of the students were taught the concept of EA debts, while the other half was taught about another topic simultaneously. Afterward, the students performed a modeling task graded by EA experts among the criteria of effectiveness, comprehensibility, minimality, and completeness. The analysis revealed no significant difference between the quality of the created models by the different student groups.
Bråtfors, Robin; Hacks, Simon; Bork, Dominik
Historization of Enterprise Architecture Models Via Enterprise Architecture Knowledge Graphs Inproceedings
In: Barn, Balbir S.; Sandkuhl, Kurt (Ed.): The Practice of Enterprise Modeling. PoEM 2022, Springer International Publishing, Cham, 2022.
@inproceedings{nokey,
title = {Historization of Enterprise Architecture Models Via Enterprise Architecture Knowledge Graphs},
author = {Robin Bråtfors and Simon Hacks and Dominik Bork},
editor = {Balbir S. Barn and Kurt Sandkuhl},
doi = {10.1007/978-3-031-21488-2_4},
year = {2022},
date = {2022-11-30},
urldate = {2022-11-30},
booktitle = {The Practice of Enterprise Modeling. PoEM 2022},
volume = {456},
publisher = {Springer International Publishing},
address = {Cham},
series = {Lecture Notes in Business Information Processing},
abstract = {Enterprise Architecture (EA) is the discipline that aims to provide a holistic view of the enterprise by explicating business and IT alignment from the perspectives of high-level corporate strategy down to daily operations and network infrastructures. EAs are consequently complex as they compose and integrate many aspects on different architecture layers. A recent proposal to cope with this complexity and to make EAs amenable to automated and intuitive visual analysis is the transformation of EA models into EA Knowledge Graphs. A remaining limitation of these approaches is that they perceive the EA to be static, i.e., they represent and analyze EAs at a single point in time. In the paper at hand, we introduce a historization concept, a prototypical implementation, and a performance analysis for how EAs can be represented and processed to enable the analysis of their evolution.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Enterprise Architecture (EA) is the discipline that aims to provide a holistic view of the enterprise by explicating business and IT alignment from the perspectives of high-level corporate strategy down to daily operations and network infrastructures. EAs are consequently complex as they compose and integrate many aspects on different architecture layers. A recent proposal to cope with this complexity and to make EAs amenable to automated and intuitive visual analysis is the transformation of EA models into EA Knowledge Graphs. A remaining limitation of these approaches is that they perceive the EA to be static, i.e., they represent and analyze EAs at a single point in time. In the paper at hand, we introduce a historization concept, a prototypical implementation, and a performance analysis for how EAs can be represented and processed to enable the analysis of their evolution.
Hacks, Simon; Smajevic, Muhamed; Bork, Dominik
Using Knowledge Graphs to Detect Enterprise Architecture Smells (Extended Abstract) Inproceedings
In: Leopold, Henrik; Proper, Henderik A. (Ed.): EMISA 2022, Gesellschaft für Informatik e.V. 2022.
@inproceedings{nokey,
title = {Using Knowledge Graphs to Detect Enterprise Architecture Smells (Extended Abstract)},
author = {Simon Hacks and Muhamed Smajevic and Dominik Bork},
editor = {Henrik Leopold and Henderik A. Proper},
url = {https://dl.gi.de/bitstream/handle/20.500.12116/40217/EMISA2022-05.pdf?sequence=1&isAllowed=y},
year = {2022},
date = {2022-06-05},
urldate = {2022-06-05},
booktitle = {EMISA 2022},
number = {5},
organization = {Gesellschaft für Informatik e.V.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Jung, Jürgen; Hacks, Simon; Gooijer, Thijmen; Kinnunen, Matti; Rehring, Kevin
In: 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW), pp. 271-278, 2021.
@inproceedings{9626297,
title = {Revealing Common Enterprise Architecture Debts: Conceptualization and Critical Reflection on a Workshop Format Industry Experience Report},
author = {Jürgen Jung and Simon Hacks and Thijmen Gooijer and Matti Kinnunen and Kevin Rehring},
doi = {10.1109/EDOCW52865.2021.00058},
year = {2021},
date = {2021-12-01},
urldate = {2021-01-01},
booktitle = {2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW)},
pages = {271-278},
abstract = {The Enterprise Architecture (EA) discipline evolved during the past two decades and is now established in a large number of companies. Architectures in these companies changed over time and are now the result of a long creation and maintenance process. Such architectures still contain processes and services provided by legacy IT systems (e.g., systems, applications) that were reasonable during the time they were created but might now hamper the introduction of better solutions. In order to support handling those legacies, research on the notion of EA debts has been started. The concept of EA debts widens the scope of technical debts to cover also organizational aspects offering a mean for managing EA in dynamic environments. The research encompasses the development of methods for managing debts together with a repository of typical EA debts. Identifying EA debts for the repository is challenging as required knowledge is usually not documented. Therefore, a structured approach is needed to externalize this knowledge. The paper presents a workshop format that is used to identify EA debts in organizations. Corresponding workshops are performed in two distinct companies to support them in understanding certain issues they face. First results from those workshops are presented in the second part of the paper.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Enterprise Architecture (EA) discipline evolved during the past two decades and is now established in a large number of companies. Architectures in these companies changed over time and are now the result of a long creation and maintenance process. Such architectures still contain processes and services provided by legacy IT systems (e.g., systems, applications) that were reasonable during the time they were created but might now hamper the introduction of better solutions. In order to support handling those legacies, research on the notion of EA debts has been started. The concept of EA debts widens the scope of technical debts to cover also organizational aspects offering a mean for managing EA in dynamic environments. The research encompasses the development of methods for managing debts together with a repository of typical EA debts. Identifying EA debts for the repository is challenging as required knowledge is usually not documented. Therefore, a structured approach is needed to externalize this knowledge. The paper presents a workshop format that is used to identify EA debts in organizations. Corresponding workshops are performed in two distinct companies to support them in understanding certain issues they face. First results from those workshops are presented in the second part of the paper.
Smajevic, Muhamed; Hacks, Simon; Bork, Dominik
Using Knowledge Graphs to Detect Enterprise Architecture Smells Inproceedings
In: Serral, Estefanía; Stirna, Janis; Ralyté, Jolita; Grabis, Jānis (Ed.): The Practice of Enterprise Modeling, pp. 48–63, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-91279-6.
@inproceedings{10.1007/978-3-030-91279-6_4,
title = {Using Knowledge Graphs to Detect Enterprise Architecture Smells},
author = {Muhamed Smajevic and Simon Hacks and Dominik Bork},
editor = {Estefanía Serral and Janis Stirna and Jolita Ralyté and Jānis Grabis},
doi = {10.1007/978-3-030-91279-6_4},
isbn = {978-3-030-91279-6},
year = {2021},
date = {2021-11-15},
urldate = {2021-11-15},
booktitle = {The Practice of Enterprise Modeling},
pages = {48--63},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {Hitherto, the concept of Enterprise Architecture (EA) Smells has been proposed to assess quality flaws in EAs and their models. Together with this new concept, a catalog of different EA Smells has been published and a first prototype was developed. However, this prototype is limited to ArchiMate and is not able to assess models adhering to other EA modeling languages. Moreover, the prototype is not integrate-able with other EA tools. Therefore, we propose to enhance the extensible Graph-based Enterprise Architecture Analysis (eGEAA) platform that relies on Knowledge Graphs with EA Smell detection capabilities. To align these two approaches, we show in this paper, how ArchiMate models can be transformed into Knowledge Graphs and provide a set of queries on the Knowledge Graph representation that are able to detect EA Smells. This enables enterprise architects to assess EA Smells on all types of EA models as long as there is a Knowledge Graph representation of the model. Finally, we evaluate the Knowledge Graph based EA Smell detection by analyzing a set of 347 EA models.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hitherto, the concept of Enterprise Architecture (EA) Smells has been proposed to assess quality flaws in EAs and their models. Together with this new concept, a catalog of different EA Smells has been published and a first prototype was developed. However, this prototype is limited to ArchiMate and is not able to assess models adhering to other EA modeling languages. Moreover, the prototype is not integrate-able with other EA tools. Therefore, we propose to enhance the extensible Graph-based Enterprise Architecture Analysis (eGEAA) platform that relies on Knowledge Graphs with EA Smell detection capabilities. To align these two approaches, we show in this paper, how ArchiMate models can be transformed into Knowledge Graphs and provide a set of queries on the Knowledge Graph representation that are able to detect EA Smells. This enables enterprise architects to assess EA Smells on all types of EA models as long as there is a Knowledge Graph representation of the model. Finally, we evaluate the Knowledge Graph based EA Smell detection by analyzing a set of 347 EA models.
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