@inproceedings{47049, abstract = {{As technology advances, Unmanned Aerial Vehicles ( UAVs) have emerged as an innovative solution to a variety of problems in many fields. Automated control of UAVs is most common in large area operations, but they may also increase the versatility of smart home compositions by acting as a physical helper. For example, a voice- controlled UAV could act as an intelligent aerial assistant that can be seamlessly integrated into smart home systems. In this paper, we present a novel Augmented Reality (AR )-based UAV control that provides high-level control over a UAV by automating common UAV missions. In our work, we enable users to operate a small UAV hands-free using only a small set of voice commands. To help users identify the targets, and to understand the UAV ’s intentions, targets within the user’s field of vision are highlighted in an AR interface. We evaluate our approach in a user study (n=26) regarding usability, physical and mental demand, as well as a focus on the users’ preferences. Our study showed that the use of the proposed control was not only accepted, but some users stated that they would use such a system at home to help with some tasks at home }}, author = {{Helmert, Robin and Hardes, Tobias and Yigitbas, Enes}}, booktitle = {{Proceedings of the ACM Symposium on Spatial User Interaction (SUI 2023)}}, location = {{ Sydney, Australia }}, publisher = {{ACM}}, title = {{{Design and Evaluation of an AR Voice-based Indoor UAV Assistant for Smart Home Scenarios}}}, year = {{2023}}, } @phdthesis{51352, abstract = {{Erfolg und Misserfolg eines Unternehmens werden maßgeblich durch getroffene Entscheidungen beeinflusst. Daher verlassen sich Entscheider oft auf Entscheidungsunterstützungssysteme, die durch Datensimulation, -optimierung und -visualisierung bei der Identifizierung von geeigneten Entscheidungen unterstützen. Für eine optimale Unterstützung muss ein Entscheidungsunterstützungssystem (EUS) jedoch auf den Entscheidungsprozess eines Entscheiders abgestimmt sein und verfügbare Daten, Optimierungsziele, persönliche Präferenzen sowie weitere Einflussfaktoren berücksichtigen. EUS-Entwickler können aufgrund der Komplexität und Volatilität von Geschäftsumgebungen allerdings nicht alle potenziellen Entscheidungsprozesse während des Entwurfs eines EUS vorhersehen, wodurch ein EUS einem Entscheider häufig nur unzureichende Anpassungsmöglichkeiten an den individuellen Entscheidungsprozess bietet. Die Einzelanfertigung eines EUS, das auf einen Entscheidungsprozess zugeschnitten ist, ist ein kosten- und zeitintensives Unterfangen aufgrund der begrenzten Verfügbarkeit von Softwareentwicklern oder Missverständnissen zwischen Entwicklern und Entscheidern während der Entwicklung. Daher geben sich Entscheider möglicherweise mit einem handelsüblichen EUS zufrieden, das nicht vollständig mit ihrem Entscheidungsprozess übereinstimmt, suboptimale Entscheidungen begünstigt und so den Unternehmenserfolg negativ beeinflusst. In dieser Arbeit wird ein Ansatz vorgeschlagen, der es Entscheidern ermöglicht, selbst maßgeschneiderte Entscheidungsunterstützungssysteme zu entwickeln und so die Diskrepanz zwischen benötigter und tatsächlicher Entscheidungsunterstützung zu vermeiden. Dazu stellen EUS-Entwickler einen Teil der EUS-Funktionalität als wiederverwendbare Software-Dienste bereit ...}}, author = {{Kirchhoff, Jonas}}, publisher = {{Universität Paderborn}}, title = {{{Decision Support Ecosystems: Assisted Low-Code Development of Tailored Decision Support Systems}}}, doi = {{10.17619/UNIPB/1-1845}}, year = {{2023}}, } @article{34402, author = {{Yigitbas, Enes and Klauke, Jonas and Gottschalk, Sebastian and Engels, Gregor}}, journal = {{Journal on Computer Languages (COLA) }}, publisher = {{Elsevier}}, title = {{{End-User Development of Interactive Web-Based Virtual Reality Scenes}}}, year = {{2023}}, } @inproceedings{33511, author = {{Yigitbas, Enes and Engels, Gregor}}, booktitle = {{56th Hawaii International Conference on System Science (HICSS 2023) }}, publisher = {{ScholarSpace}}, title = {{{Enhancing Robot Programming through Digital Twin and Augmented Reality }}}, year = {{2023}}, } @inproceedings{34401, author = {{Yigitbas, Enes and Krois, Sebastian and Gottschalk, Sebastian and Engels, Gregor}}, booktitle = {{Proceedings of the 7th International Conference on Human Computer Interaction Theory and Applications (HUCAPP'23) }}, title = {{{Towards Enhanced Guiding Mechanisms in VR Training through Process Mining}}}, year = {{2023}}, } @inproceedings{43424, author = {{Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Proceedings of the 19th IFIP TC13 International Conference on Human-Computer Interaction (INTERACT 2023)}}, publisher = {{Springer}}, title = {{{Supporting Construction and Architectural Visualization through BIM and AR/VR: A Systematic Literature Review}}}, year = {{2023}}, } @techreport{46491, author = {{Kundisch, Dennis}}, pages = {{12--13}}, title = {{{#DIGITALENTS - Digital Talents Programm geht in die zweite Runde}}}, volume = {{1}}, year = {{2023}}, } @inproceedings{47050, author = {{Wecker, Daniel and Yigitbas, Enes}}, booktitle = {{Proceedings of the ACM Symposium on Spatial User Interaction (SUI 2023)}}, publisher = {{ACM}}, title = {{{Minimizing Eye Movements and Distractions in Head-Mounted Augmented Reality through Eye-Gaze Adaptiveness}}}, year = {{2023}}, } @article{47051, author = {{Yigitbas, Enes and Schmidt, Maximilian and Bucchiarone, Antonio and Gottschalk, Sebastian and Engels, Gregor}}, journal = {{Science of Computer Programming}}, publisher = {{Elsevier}}, title = {{{GaMoVR: Gamification-Based UML Learning Environment in Virtual Reality}}}, year = {{2023}}, } @inproceedings{47057, author = {{Schmidt, Leonard and Yigitbas, Enes}}, booktitle = {{Proceedings of the 27th International Workshop on Personalization and Recommendation}}, publisher = {{GI DL}}, title = {{{Transitional Cross Reality Interfaces for Spatially Demanding Search and Collect Tasks }}}, year = {{2023}}, } @inproceedings{47055, author = {{Neumayr, Thomas and Yigitbas, Enes and Augstein, Mirjam and Herder, Eelco}}, booktitle = {{Proceedings of the Mensch & Computer (2023)}}, title = {{{ABIS 2023 – 27th International Workshop on Personalization and Recommendation}}}, year = {{2023}}, } @inproceedings{47150, author = {{Yigitbas, Enes and Witalinski, Iwo and Gottschalk, Sebastian and Engels, Gregor}}, booktitle = {{Proceedings of the 24th International Conference on Product-Focused Software Process Improvement (PROFES 2023)}}, publisher = {{Springer}}, title = {{{Virtual Reality Collaboration Platform for Agile Software Development}}}, year = {{2023}}, } @inproceedings{29839, abstract = {{The development of business models is a challenging task that can be supported with software tools. Here, existing approaches and tools do not focus on the company’s situation in which the development takes place (e.g., financial resources, product type). To tackle this challenge, we used design science research to develop a situation-specific business model development approach that contains three stages: First, existing knowledge in terms of tasks to do (e.g., analyze competitive advantage), and decisions to be made (e.g., social media marketing) are stored in repositories. Second, the knowledge is used to compose a development method based on the company’s situation. Third, the development method is enacted to develop a business model. This demonstration paper presents a tool-support called Situational Business Model Developer that supports all stages of our approach. We release the tool under open-source and evaluate it with a case study on developing business models for mobile apps.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Proceedings of the 17th International Conference on Wirtschaftsinformatik}}, keywords = {{Business Model Development, Situational Method Engineering, Tool Support}}, location = {{Nuremberg}}, publisher = {{AIS}}, title = {{{Situational Business Model Developer: A Tool-support for Situation-specific Business Model Development}}}, year = {{2022}}, } @inproceedings{29840, abstract = {{Due to the proliferation of Virtual Reality (VR) technology, VR is finding new applications in various domains, such as stock trading. Here, traders invest in stocks intending to increase their profit. For this purpose, in conventional stock trading, traders usually make use of 2D applications on desktop or laptop devices. This leads to many drawbacks such as poor visibility due to limited 2D representation, complex interaction due to indirect interaction via mouse and keyboard, or restricted support for collaboration between traders. To overcome these issues, we have developed a novel collaborative, virtual environment for stock trading, which enables stock traders to view financial information and trade stocks with other collaborators. The main results of a usability study indicate that the VR environment, compared to conventional stock trading, shows no significant advantages concerning efficiency and effectiveness, however, we could observe an increased user satisfaction and better collaboration.}}, author = {{Yigitbas, Enes and Gottschalk, Sebastian and Nowosad, Alexander and Engels, Gregor}}, booktitle = {{Proceedings of the 17th International Conference on Wirtschaftsinformatik}}, keywords = {{virtual reality, stock trading, collaboration, usability}}, location = {{Nuremberg}}, publisher = {{AIS}}, title = {{{Development and Evaluation of a Collaborative Stock Trading Environment in Virtual Reality}}}, year = {{2022}}, } @inproceedings{29927, author = {{Yigitbas, Enes and Karakaya, Kadiray and Jovanovikj, Ivan and Engels, Gregor}}, booktitle = {{Software Engineering 2022, Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-25. Februar 2022, Virtuell}}, editor = {{Grunske, Lars and Siegmund, Janet and Vogelsang, Andreas}}, pages = {{95–96}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Enhancing Human-in-the-Loop Adaptive Systems through Digital Twins and VR Interfaces}}}, doi = {{10.18420/se2022-ws-033}}, volume = {{{P-320}}}, year = {{2022}}, } @inproceedings{29926, author = {{Yigitbas, Enes and Gorissen, Simon and Weidmann, Nils and Engels, Gregor}}, booktitle = {{Software Engineering 2022, Fachtagung des GI-Fachbereichs Softwaretechnik, 21.-25. Februar 2022, Virtuell}}, editor = {{Grunske, Lars and Siegmund, Janet and Vogelsang, Andreas}}, pages = {{93–94}}, publisher = {{Gesellschaft für Informatik e.V.}}, title = {{{Collaborative Software Modeling in Virtual Reality}}}, doi = {{10.18420/se2022-ws-032}}, volume = {{{P-320}}}, year = {{2022}}, } @inbook{29928, author = {{Yigitbas, Enes and Sauer, Stefan and Engels, Gregor}}, booktitle = {{Digital Transformation: Core Technologies and Emerging Topics from a Computer Science Perspective}}, editor = {{Vogel-Heuser, Birgit and Wimmer, Manuel}}, publisher = {{Springer-Vieweg}}, title = {{{Self-Adaptive Digital Assistance Systems for Work 4.0}}}, year = {{2022}}, } @inproceedings{29842, abstract = {{To build successful software products, developers continuously have to discover what features the users really need. This discovery can be achieved with continuous experimentation, testing different software variants with distinct user groups, and deploying the superior variant for all users. However, existing approaches do not focus on explicit modeling of variants and experiments, which offers advantages such as traceability of decisions and combinability of experiments. Therefore, our vision is the provision of model-driven continuous experimentation, which provides the developer with a framework for structuring the experimentation process. For that, we introduce the overall concept, apply it to the experimentation on component-based software architectures and point out future research questions. In particular, we show the applicability by combining feature models for modeling the software variants, users, and experiments (i.e., model-driven) with MAPE-K for the adaptation (i.e., continuous experimentation) and implementing the concept based on the component-based Angular framework.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Engels, Gregor}}, booktitle = {{Proceedings of the 18th International Conference on Software Architecture Companion }}, keywords = {{continuous experimentation, model-driven, component-based software architectures, self-adaptation}}, location = {{Hawaii}}, publisher = {{IEEE}}, title = {{{Model-driven Continuous Experimentation on Component-based Software Architectures }}}, doi = {{10.1109/ICSA-C54293.2022.00011}}, year = {{2022}}, } @inbook{34023, abstract = {{Decision makers increasingly rely on decision support systems for optimal decision making. Recently, special attention has been paid to process-driven decision support systems (PD-DSS) in which a process model prescribes the invocation sequence of software-based decision support services and the data exchange between them. Thus, it is possible to quickly combine available decision support services as needed for optimally supporting the decision making process of an individual decision maker. However, process modelers may accidentally create a process model which is technically well-formed and executable, but contains functional and behavioral flaws such as redundant or missing services. These flaws may result in inefficient computations or invalid decision recommendations when the corresponding PD-DSS is utilized by a decision maker. In this paper, we therefore propose an approach to validate functionality and behavior of a process model representing a PD-DSS. Our approach is based on expressing flaws as anti-patterns for which the process model can be automatically checked via graph matching. We prototypically implemented our approach and demonstrate its applicability in the context of decision making for energy network planning.}}, author = {{Kirchhoff, Jonas and Engels, Gregor}}, booktitle = {{Software Business}}, isbn = {{9783031207051}}, issn = {{1865-1348}}, pages = {{227----243}}, publisher = {{Springer International Publishing}}, title = {{{Anti-pattern Detection in Process-Driven Decision Support Systems}}}, doi = {{10.1007/978-3-031-20706-8_16}}, volume = {{463}}, year = {{2022}}, } @inbook{32792, abstract = {{Decision makers in complex business environments have different goals and constraints and therefore require tailored decision support systems (DSS). Following a low-code approach, a tailored DSS can be created by a decision maker as a process-based composition of existing, interoperable decision support services. Data incompatibilities may be introduced during the design or execution of such a process-driven DSS, e.g., when a service always generates or a decision maker selects data which violates a data constraint of a subsequent service. These incompatibilities cause interrupted or erroneous decision processes. In this paper, we contribute an approach which enables the detection of data incompatibilities in process-driven DSS during process design and execution. Our approach utilizes the JSON Schema specification to define service interfaces and associated type constraints which data produced by services or decision makers can be validated against. We demonstrate our approach in the context of decision support for energy network planning using a prototypical open-source implementation.}}, author = {{Kirchhoff, Jonas and Gottschalk, Sebastian and Engels, Gregor}}, booktitle = {{Lecture Notes in Business Information Processing}}, isbn = {{9783031115097}}, issn = {{1865-1348}}, publisher = {{Springer International Publishing}}, title = {{{Detecting Data Incompatibilities in Process-Driven Decision Support Systems}}}, doi = {{10.1007/978-3-031-11510-3_6}}, year = {{2022}}, }