@misc{10962, abstract = {{The increasing number of product artifacts (e.g., mechanical or electronic components, software functions, documents) confronts small and medium-sized companies with the challenge of assessing change effects. The lack of knowledge of artifact relationships causes problems, such as outdated documentation, lack of coordination with affected disciplines, or delayed changes. The Design Structure Matrix (DSM) can clearly represent the elements and relationships of complex systems. This paper presents an assistance system for intuitive visualization of engineering change effects using existing DSM-based methods for complexity management. The implemented algorithms compute graph layouts, cluster analyses, and change predictions in the form of change risk, time, and cost. An application example of a 3D-printed intelligent lamp demonstrates the approach's viability. The paper concludes with a discussion of the benefits and future activities.}}, author = {{Herrmann, Jan-Phillip and Tackenberg, Sven and Trojanowski, Christoph and Pankrath, Carolin and Imort, Sebastian and Deuter, Andreas}}, booktitle = {{DS 126: Proceedings of the 25th International DSM Conference (DSM 2023)}}, editor = {{Stowe, Harold and Browning, Tyson R. and Eppinger, Steven D. and Trauer, Jakob and Langner, Christopher and Kreimeyer, Matthias and Isaksson, Ola and Panarotto, Massimo and Brahma, Arindam}}, keywords = {{Graph-based Visualization, Assistance System, Engineering Change Management, Complexity Management}}, location = {{Gothenburg, Sweden}}, pages = {{58--67}}, publisher = {{The Design Society}}, title = {{{Assistance System for graph-based 3D Visualization of Design Structure Matrices}}}, doi = {{10.35199/dsm2023.07}}, year = {{2023}}, } @misc{13010, abstract = {{Especially in highly interdisciplinary fields such as automation engineering, contemporary programming education with tailored assignments and individual feedback is a major challenge for educational institutions due to the increasing number of students per teacher and the ever-increasing demand for computer science professionals. To address this gap, we present ”KIAAA” an AI Assistant for Automation Engineering Teaching, a work-in-progress approach for an integrated, customized, and AI-based learning support system for automation and programming courses based on instructor-defined course objectives. Thereby in the KIAAA system, the individual knowledge level of the students is determined and individually tailored virtual learning scenarios are generated based on the knowledge and learning profile of the students. These are iteratively adapted based on the answers given. To achieve this, KIAAA uses several AI components, a hybrid rule-based scenario generation component, a Help-DKT-based cognitive model, and a solution assessor that uses a combination of traditional code analysis methods and AI-based analyses methods for automated programming task assessment. These components are the main parts of KIAAA to generate customized programming scenarios as well as visualization and simulation based on a modern game and physics engine.}}, author = {{Eilermann, Sebastian and Wehmeier, Leon and Niggemann, Oliver and Deuter, Andreas}}, booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}}, editor = {{Jasperneite, Jürgen}}, isbn = {{978-1-6654-9314-7}}, keywords = {{Visualization, Automation, Education, Games, Hybrid power systems, Task analysis, Artificial intelligence}}, location = {{Lemgo}}, publisher = {{IEEE}}, title = {{{KIAAA: An AI Assistant for Teaching Programming in the Field of Automation}}}, doi = {{10.1109/indin51400.2023.10218157}}, year = {{2023}}, } @inproceedings{682, abstract = {{Well-designed interactive visualizations help users gaining insights into an organization's data and, finally, making decisions. In the Business Intelligence (BI) context, the most popular visualization approach is dashboards, which combine multiple visual components, such as charts, on a single view. Well-founded decisions require the collaboration of several analysts, such as domain experts, line-of-business managers, or key suppliers. For face-to-face collaboration settings, multi-display environments and smart meeting rooms have improved. In the BI context, support for boardrooms is being discussed for this setting. In this paper, we propose annotation dashboards, based on a multitouch and multiuser interaction approach, which are integrated in a multi-display environment constituting a BI digital boardroom. In addition, means of semantic navigation help business users to easily get insights into business context information.}}, author = {{Rubart, Jessica and Lietzau, Benjamin and Soehlke, Patrick and Alex, Bastian and Becker, Stephan and Wienboeker, Tim}}, booktitle = {{2017 IEEE 11th International Conference on Semantic Computing (ICSC)}}, isbn = {{978-1-5090-4285-2 }}, keywords = {{Semantics, Data visualization, Navigation, Collaboration, Context, Business intelligence, Digital Boardroom, Multitouch and Multiuser Interaction}}, location = {{San Diego, CA, USA}}, publisher = {{IEEE}}, title = {{{Semantic Navigation and Discussion in a Digital Boardroom}}}, doi = {{10.1109/icsc.2017.39}}, year = {{2017}}, } @book{4336, abstract = {{Prolonged life expectancy along with the increasing complexity of medicine and health services raises health costs worldwide dramatically. Whilst the smart health concept has much potential to support the concept of the emerging P4-medicine (preventive, participatory, predictive, and personalized), such high-tech medicine produces large amounts of high-dimensional, weakly-structured data sets and massive amounts of unstructured information. All these technological approaches along with “big data” are turning the medical sciences into a data-intensive science. To keep pace with the growing amounts of complex data, smart hospital approaches are a commandment of the future, necessitating context aware computing along with advanced interaction paradigms in new physical-digital ecosystems. The very successful synergistic combination of methodologies and approaches from Human-Computer Interaction (HCI) and Knowledge Discovery and Data Mining (KDD) offers ideal conditions for the vision to support human intelligence with machine learning. The papers selected for this volume focus on hot topics in smart health; they discuss open problems and future challenges in order to provide a research agenda to stimulate further research and progress.}}, editor = {{Holzinger, Andreas and Röcker, Carsten and Ziefle, Martina}}, isbn = {{978-3-319-16225-6}}, issn = {{1611-3349}}, keywords = {{HCI, ambient assisted living, big data, computational intelligence, context awareness, data centric medicine, decision support, interactive data mining, keyword detection, knoweldge bases, knoweldge discovery, machine learning, medical decision support, medical informatics, natural language processing, pervasive health, smart home, ubiquitous computing, visualization, wearable sensors}}, pages = {{275}}, publisher = {{Springer}}, title = {{{Smart Health: Open Problems and Future Challenges}}}, doi = {{10.1007/978-3-319-16226-3}}, volume = {{8700}}, year = {{2015}}, } @inproceedings{4371, abstract = {{A major challenge in modern data-centric medicine is the increasing amount of time-dependent data, which requires efficient user-friendly solutions for dealing with such data. To create an effective and efficient knowledge discovery process, it is important to support common data manipulation tasks by creating quick, responsive and intuitive interaction methods. In this paper we describe some methods for interactive longitudinal data visualization with focus on the usage of mobile multi-touch devices as interaction medium, based on our design and development experiences. We argue that when it comes to longitudinal data this device category offers remarkable additional interaction benefits compared to standard point-and-click desktop computer devices. An important advantage of multi-touch devices arises when interacting with particularly large longitudinal data sets: Complex, coupled interactions such as zooming into a region and scrolling around almost simultaneously is more easily achieved with the possibilities of a multi-touch device than compared to a regular mouse-based interaction device.}}, author = {{Holzinger, Andreas and Schwarz, Michael and Ofner, Bernhard and Jeanquartier, Fleur and Calero-Valdez, Andre and Röcker, Carsten and Ziefle, Martina}}, booktitle = {{ Availability, Reliability, and Security in Information Systems }}, editor = {{Teufel, Stephanie and Min, Tjoa A and You, Ilsun and Weippl, Edgar }}, isbn = {{978-3-319-10974-9}}, keywords = {{Data Visualization, Longitudinal Data, Time Series, Multi-Touch, Mobile Computing}}, location = {{Fribourg, Switzerland}}, pages = {{124 -- 137}}, publisher = {{Springer}}, title = {{{Towards Interactive Visualization of Longitudinal Data to Support Knowledge Discovery on Multi-Touch Tablet Computers}}}, doi = {{10.1007/978-3-319-10975-6_9}}, volume = {{8708}}, year = {{2014}}, } @inproceedings{4378, abstract = {{The development of a widely applicable automatic motion coaching system requires one to address a lot of issues including motion capturing, motion analysis and comparison, error detection as well as error feedback. In order to cope with this complexity, most existing approaches focus on a specific motion sequence or exercise. As a first step towards the development of a more generic system, this paper systematically analyzes different error and feedback types. A prototype of a feedback system that addresses multiple modalities is presented. The system allows to evaluate the applicability of the proposed feedback techniques for arbitrary types of motions in a next step.}}, author = {{Ukita, Norimichi and Kaulen, Daniel and Röcker, Carsten}}, booktitle = {{Proceedings of the International Conference on Physiological Computing Systems (PhyCS'14)}}, editor = {{Holzinger, Andreas}}, keywords = {{Motion Coaching, Motion Error Feedback, Prototyping, Error Visualization, Error Audiolization.}}, location = {{Lisbon, Portugal}}, number = {{PhyCS}}, pages = {{167 -- 172}}, publisher = {{ SCITEPRESS }}, title = {{{Towards an Automatic Motion Coaching System: Feedback Techniques for Different Types of Motion Errors}}}, doi = {{10.5220/0004884901670172}}, volume = {{1}}, year = {{2014}}, }