@misc{21601,
  abstract     = {{The invention describes a distributed merchandise management system, in which the client, retailer and the manufacturer are linked by a network. This is implemented by a cloud storage (105), the cloud storage (105) comprising a means (105 a) for storing data, a means for receiving first data from a first network node (110), the first data being associated with a physical object, a means for receiving request data from a second network node (120), a means for receiving second data from a third network node (130), the second data being associated with the first data and comprising at least one data piece adapted to change the first data depending on the received request data, a means for changing the first data based at least in part on the second data and the request data, and a means for sending a changed portion of the first data from the cloud storage (105) to the first network node (110).}},
  author       = {{Göllner, Thomas and Schwarz, Jan-Hendrik and Gottschalk, Sebastian and Sauer, Stefan}},
  title        = {{{Distributed merchandise management system}}},
  year         = {{2021}},
}

@inproceedings{21639,
  abstract     = {{The development of effective business models is an essential task in highly competitive markets like mobile ecosystems. Existing development methods for these business models do not specifically focus that the development process profoundly depends on the situation (e.g., market size, regulations) of the mobile app developer. Here, a mismatch between method and situation can lead to poor resource management and longer development cycles. In software engineering, situational method engineering is used for software projects to configure a development method out of a method repository based on the project situation. Analogously, we support creating situation-specific business model development methods with a method base and new user roles. Here, the method engineer obtains the knowledge of the domain expert and stores it in the method base as elements, building blocks, and patterns. The expert knowledge is derived from a grey literature review on mobile development processes. After this, the method engineer constructs the development method based on the described situation of the business developer. We provide an open-source tool and evaluate it by constructing a local event platform's business model development method.    }},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}},
  booktitle    = {{Enterprise, Business-Process and Information Systems Modeling}},
  keywords     = {{Business Model Development, Situational Method Engineering, Mobile App, Business Model Development Tools}},
  publisher    = {{Springer}},
  title        = {{{Situation-specific Business Model Development Methods for Mobile App Developers}}},
  doi          = {{10.1007/978-3-030-79186-5_17}},
  year         = {{2021}},
}

@article{23525,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>In the field of Model-Driven Engineering, Triple Graph Grammars
(TGGs) play an important role as a rule-based means of implementing
consistency management. From a declarative specification of a
consistency relation, several operations including forward and
backward transformations, (concurrent) synchronisation, and
consistency checks can be automatically derived. For TGGs to be
applicable in realistic application scenarios, expressiveness in
terms of supported language features is very important. A TGG tool
is schema compliant if it can take domain constraints, such as
multiplicity constraints in a meta-model, into account when
performing consistency management tasks. To guarantee schema
compliance, most TGG tools allow application conditions to be
attached as necessary to relevant rules. This strategy is
problematic for at least two reasons: First, ensuring compliance to
a sufficiently expressive schema for all previously mentioned
derived operations is still an open challenge; to the best of our
knowledge, all existing TGG tools only support a very restricted
subset of application conditions. Second, it is conceptually
demanding for the user to indirectly specify domain constraints as
application conditions, especially because this has to be completely
revisited every time the TGG or domain constraint is changed. While
domain constraints can in theory be automatically transformed to
obtain the required set of application conditions, this has only
been successfully transferred to TGGs for a very limited subset of
domain constraints. To address these limitations, this paper
proposes a search-based strategy for achieving schema compliance. We
show that all correctness and completeness properties, previously
proven in a setting without domain constraints, still hold when
schema compliance is to be additionally guaranteed. An
implementation and experimental evaluation are provided to support
our claim of practical applicability.</jats:p>}},
  author       = {{Weidmann, Nils and Anjorin, Anthony}},
  issn         = {{0934-5043}},
  journal      = {{Formal Aspects of Computing}},
  publisher    = {{Springer}},
  title        = {{{Schema Compliant Consistency Management via Triple Graph Grammars and Integer Linear Programming}}},
  doi          = {{10.1007/s00165-021-00557-0}},
  year         = {{2021}},
}

@inproceedings{20540,
  author       = {{Jovanovikj, Ivan and Thottam, Anu Tony and Joseph Vincent, Vishal and Yigitbas, Enes and Sauer, Stefan and Engels, Gregor}},
  booktitle    = {{Proceedings of the 9th International Conference on Model-Driven Engineering and Software Development }},
  location     = {{Vienna}},
  pages        = {{232--239}},
  publisher    = {{SCITEPRESS}},
  title        = {{{A Modeling Workbench for the Development of Situation-specific Test Co-Migration Methods }}},
  year         = {{2021}},
}

@inproceedings{20886,
  author       = {{Nickchen, Tobias and Heindorf, Stefan and Engels, Gregor}},
  booktitle    = {{Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision}},
  location     = {{Hawaii}},
  pages        = {{1994--2002}},
  title        = {{{Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts}}},
  year         = {{2021}},
}

@inproceedings{22155,
  author       = {{Gottschalk, Sebastian}},
  booktitle    = {{Advanced Software Engineering. Doctorial Consortium}},
  publisher    = {{CEUR}},
  title        = {{{Situation-specific Development of Business Models for Services in Software Ecosystems}}},
  year         = {{2021}},
}

@article{22814,
  author       = {{Weidmann, Nils and Salunkhe, Shubhangi and Anjorin, Anthony and Yigitbas, Enes and Engels, Gregor}},
  issn         = {{1660-1769}},
  journal      = {{The Journal of Object Technology}},
  title        = {{{Automating Model Transformations for Railway Systems Engineering.}}},
  doi          = {{10.5381/jot.2021.20.3.a10}},
  year         = {{2021}},
}

@inproceedings{22819,
  author       = {{Yigitbas, Enes and Karakaya, Kadiray and Jovanovikj, Ivan and Engels, Gregor}},
  booktitle    = {{2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)}},
  title        = {{{Enhancing Human-in-the-Loop Adaptive Systems through Digital Twins and VR Interfaces}}},
  doi          = {{10.1109/seams51251.2021.00015}},
  year         = {{2021}},
}

@inproceedings{22959,
  author       = {{Weidmann, Nils and Engels, Gregor}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  location     = {{Lille, France}},
  title        = {{{Concurrent model synchronisation with multiple objectives}}},
  doi          = {{10.1145/3449639.3459283}},
  year         = {{2021}},
}

@inproceedings{21727,
  abstract     = {{Platform-based business models underlie the success of many of today’s largest, fastest-growing, and most disruptive companies. Despite the success of prominent examples, such as Uber and Airbnb, creating a profitable platform ecosystem presents a key challenge for many companies across all industries. Although research provides knowledge about platforms’ different value drivers (e.g., network effects), companies that seek to transform their current business model into a platform-based one lack an artifact to reduce knowledge boundaries, collaborate effectively, and cope with the complexities and dynamics of platform ecosystems. We address this challenge by developing two artifacts and combining research from variability modeling, business model dependencies, and system dynamics. This paper presents a design science research approach to develop the platform ecosystem modeling language and the platform ecosystem development tool that support researcher and practitioner by visualizing and simulating platform ecosystems. }},
  author       = {{Vorbohle, Christian and Gottschalk, Sebastian}},
  booktitle    = {{Proceedings of the 29th European Conference on Information Systems (ECIS)}},
  keywords     = {{Platform Ecosystems, Platform Ecosystem Modeling Language, Platform Ecosystem Development Tool, Business Models, Design Science}},
  location     = {{Virtual Conference/Workshop}},
  publisher    = {{AIS}},
  title        = {{{Towards Visualizing and Simulating Business Models in Dynamic Platform Ecosystems }}},
  year         = {{2021}},
}

@inproceedings{29235,
  author       = {{Gottschalk, Sebastian and Aziz, Muhammad Suffyan and Yigitbas, Enes and Engels, Gregor}},
  booktitle    = {{Software Business - 12th International Conference, ICSOB 2021, Drammen, Norway, December 2-3, 2021, Proceedings}},
  editor       = {{Wang, Xiaofeng and Martini, Antonio and Nguyen-Duc, Anh and Stray, Viktoria}},
  pages        = {{205–220}},
  publisher    = {{Springer}},
  title        = {{{Design Principles for a Crowd-Based Prototype Validation Platform}}},
  doi          = {{10.1007/978-3-030-91983-2_16}},
  volume       = {{434}},
  year         = {{2021}},
}

@inbook{25528,
  abstract     = {{Developing effective business models is a complex process for a company where several tasks (e.g., conduct customer interviews) need to be accomplished, and decisions (e.g., advertisement as a revenue stream) must be made. Here, domain experts can guide the choices of tasks and decisions with their knowledge. Nevertheless, this knowledge needs to match the situation of the company (e.g., financial resources) and the application domain of the product/service (e.g., mobile app) to reduce the risk of developing ineffective business models with low market penetration. This is not covered by one-size-fits-all development methods without tailoring before the enaction.
Therefore, we conduct a design science study to create a situation-specific development approach for business models. Based on situational method engineering and our previous work in storing knowledge of methods and models in distinct repositories, this paper shows the situation-specific composition and enaction of business model development methods. First, the method engineer composes the development method out of both repositories based on the situational context. Second, the business developer enacts the method and develops the business model.  We implement the approach in a tool and evaluate it with a industrial case study on mobile apps.}},
  author       = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}},
  booktitle    = {{Product-focused Software Process Improvement}},
  keywords     = {{Business Model Development, Situational Method Engineering, Lean Development, Kanban Boards, Canvas Models}},
  location     = {{Turin}},
  publisher    = {{Springer}},
  title        = {{{Situation- and  Domain-specific Composition and Enactment of Business Model Development Methods}}},
  year         = {{2021}},
}

@inproceedings{21593,
  author       = {{Yigitbas, Enes and Jovanovikj, Ivan and Engels, Gregor}},
  booktitle    = {{Proceedings of the 18th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2021) }},
  publisher    = {{Springer}},
  title        = {{{Simplifying Robot Programming using Augmented Reality and End-User Development}}},
  year         = {{2021}},
}

@inproceedings{21707,
  author       = {{Yigitbas, Enes and Sauer, Stefan and Engels, Gregor}},
  booktitle    = {{Proceedings of the 13th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2021)}},
  publisher    = {{ACM}},
  title        = {{{Using Augmented Reality for Enhancing Planning and Measurements in the Scaffolding Business}}},
  year         = {{2021}},
}

@inproceedings{22706,
  author       = {{Yigitbas, Enes and Gorissen, Simon and Weidmann, Nils and Engels, Gregor}},
  booktitle    = {{Proceedings of the 24th International Conference on Model Driven Engineering Languages and Systems (MODELS'21) }},
  publisher    = {{ACM/IEEE}},
  title        = {{{Collaborative Software Modeling in Virtual Reality}}},
  year         = {{2021}},
}

@inproceedings{21093,
  abstract     = {{Requirements for energy distribution networks are changing fast due to the growing share of renewable energy, increasing electrification, and novel consumer and asset technologies. Since uncertainties about future developments increase planning difficulty, flexibility potentials such as synergies between the electricity, gas, heat, and transport sector often remain unused. In this paper, we therefore present a novel module-based concept for a decision support system that helps distribution network planners to identify cross-sectoral synergies and to select optimal network assets such as transformers, cables, pipes, energy storage systems or energy conversion technology. The concept enables long-term transformation plans and supports distribution network planners in designing reliable, sustainable and cost-efficient distribution networks for future demands.}},
  author       = {{Kirchhoff, Jonas and Burmeister, Sascha Christian and Weskamp, Christoph and Engels, Gregor}},
  booktitle    = {{Energy Informatics and Electro Mobility ICT}},
  editor       = {{Breitner, Michael H. and Lehnhoff, Sebastian and Nieße, Astrid and Staudt, Philipp and Weinhardt, Christof and Werth, Oliver}},
  title        = {{{Towards a Decision Support System for Cross-Sectoral Energy Distribution Network Planning}}},
  year         = {{2021}},
}

@phdthesis{28683,
  abstract     = {{In den letzten Jahren haben sich Software-Ökosysteme als neue, erfolgreiche Geschäftsform etabliert. Unternehmen agieren hierbei als Anbieter von Software-Plattformen, auf denen Drittanbieter Softwarelösungen für den Markt anbieten können.  Etablierte Beispiele sind hierbei sogenannte App-Stores, die z.B. von Google oder Apple angeboten werden.

Beim Aufbau von Software-Ökosystemen müssen vom Plattformanbieter viele architektonische Entwurfsentscheidungen getroffen werden. Bisher gibt es keine Architekturrichtlinien und -werkzeuge, die den Entwurf einer Ökosystemarchitektur unterstützen. Dadurch fehlt hier systematisches, wiederverwendbares Wissen. Plattformanbieter müssen auf ad-hoc Entscheidungen zurückgreifen. Dies kann dann zu Problemen im Betrieb der Software-Plattformen führen, zu erhöhten Ausfallrisiken und Mehrkosten.

Der Mangel an Architekturwissen manifestiert sich konkret in zwei Gruppen von Herausforderungen: Erstens fehlt eine Wissensbasis zu Architekturalternativen und zweitens fehlt es an methodischem Wissen zu Entwicklung und Betrieb von Software-Ökosystemen. Eine Architekturwissensbasis würde Orientierungshilfen zu den Bestandteilen von Software-Ökosystemen und deren Abhängigkeiten geben, während methodisches Wissen die Erstellung dieser Systeme erleichtern würde.

In der Dissertation werden diese Herausforderungen durch die Entwicklung des Frameworks SecoArc für die Modellierung von Software-Ökosystemen angegangen. Der Beitrag der Dissertation ist zweifach: 
1.	Das SecoArc-Framework umfasst eine Architekturwissensbasis, die wiederverwendbare Architekturentwurfsentscheidungen
von Software-Ökosystemen enthält. Die Wissensbasis wurde entwickelt, indem das Architekturwissen bestehender Ökosysteme sowie aus existierender Fachliteratur ermittelt wurde und in einer Produktlinie für Software-Ökosysteme konsolidiert wurde. Die Produktlinie umfasst architektonische Gemeinsamkeiten und Variabilitäten von Software-Ökosystemen. 
2.	Das SecoArc-Framework liefert methodisches Wissen, um die Ökosystemarchitektur in Modellen zu entwerfen und zu analysieren. Dieses Wissen wurde entwickelt, indem drei Architekturmuster identifiziert wurden. Jedes Muster erfasst unterschiedliche Beziehungen zwischen architektonischen Entwurfsentscheidungen zu den Qualitätsmerkmalen einer Ökosystemgesundheit und der Erreichung von Geschäftszielen. 

Die Architekturmuster und die Produktlinie wurden dazu genutzt, ein Modellierungsframework zu entwickeln und in Form eines Prototypen umzusetzen, welches einen Entwurfsprozess, eine Modellierungssprache und eine Architekturanalysetechnik umfasst. Es erleichtert das Modellieren, Analysieren und Vergleichen von Ökosystemarchitekturen.

Die Ergebnisse der Dissertation wurden im Rahmen von zwei Studien evaluiert. In der ersten Validierungsstudie wurden das Framework sowie der Prototyp verwendet, um zwei alternative Ökosystemarchitekturen zu entwerfen und zu analysieren. In der zweiten Studie wurde eine Analyse von existierenden Ökosystemen basierend auf den architektonischen Variabilitäten des Frameworks durchgeführt.}},
  author       = {{Schwichtenberg, Bahar}},
  keywords     = {{Enterprise Architecture, Architectural Design Decisions, Open Platforms}},
  title        = {{{Modeling and Analyzing Software Ecosystems}}},
  doi          = {{10.17619/UNIPB/1-1270 }},
  year         = {{2021}},
}

@inproceedings{22482,
  author       = {{Yigitbas, Enes and Klauke, Jonas and Gottschalk, Sebastian and Engels, Gregor}},
  booktitle    = {{Proceedings of the 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) }},
  publisher    = {{IEEE}},
  title        = {{{VREUD - An End-User Development Tool to Simplify the Creation of Interactive VR Scenes}}},
  year         = {{2021}},
}

@inproceedings{28988,
  author       = {{Kirchhoff, Jonas}},
  booktitle    = {{The 1st Early Career Researchers Workshop Co-Located with ECSS 2021}},
  location     = {{Madrid}},
  title        = {{{Providing Decision Makers with Tailored Decision Support Systems}}},
  year         = {{2021}},
}

@inproceedings{29294,
  author       = {{Nickchen, Tobias and Heindorf, Stefan and Engels, Gregor}},
  booktitle    = {{2021 IEEE Winter Conference on Applications of Computer Vision (WACV)}},
  publisher    = {{IEEE}},
  title        = {{{Generating Physically Sound Training Data for Image Recognition of Additively Manufactured Parts}}},
  doi          = {{10.1109/wacv48630.2021.00204}},
  year         = {{2021}},
}