Title
Prof. Dr.
Last Name
Seiger
First name
Ronny
Email
ronny.seiger@unisg.ch
ORCID
1 - 10 of 29
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PublicationRBPMN: the value of roles for business process modelingBusiness process modeling is essential for organizations to comprehend, analyze, and enhance their business operations. The business process model and notation (BPMN) is a standard widely adopted for illustrating business processes. However, it falls short when modeling roles, interactions, and responsibilities within complex modern processes that involve digital, human, and non-human entities, typically found in cyber-physical systems (CPS). In this paper, we introduce Role-based BPMN (RBPMN), a standard-compliant extension of BPMN 2.0 that distinctly depicts roles and their interactions within business processes. We underscore the value of RBPMN and a role-based context modeling approach through a modeling example in CPS that facilitates the representation of role-based variations in the process flow, namely a production process in a smart factory. Our findings suggest that RBPMN is a valuable BPMN extension that enhances the expressiveness, variability, and comprehensiveness of business process models, especially in complex and context-sensitive processes.
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PublicationThe biggest business process management problems to solve before we die(Elsevier, 2023-01)
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PublicationAn Interactive Method for Detection of Process Activity Executions from IoT Data( 2023-02)
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PublicationIntegrating process management and event processing in smart factories: A systems architecture and use casesThe developments of new concepts for an increased digitization of manufacturing industries in the context of Industry 4.0 have brought about novel system architectures and frameworks for smart production systems. These range from generic frameworks for Industry 4.0 to domain-specific architectures for Industrial Internet of Things (IIoT). While most of the approaches include a service-based architecture for selective integration with enterprise systems, a close two-way integration of the production control systems and IIoT sensors and actuators with Process-Aware Information Systems (PAIS) on the management level for automation and mining of production processes is rarely discussed. This fusion of Business Process Management (BPM) with IIoT can be mutually beneficial for both research areas, but is still in its infancy. We propose a systems architecture for IIoT that shows how to integrate the low-level hardware components–sensors and actuators–of a smart factory with BPM systems. We discuss the software components and their interactions to address challenges of device encapsulation, integration of sensor events, and interaction with existing BPM systems. This integration is demonstrated within several use cases regarding process modeling, automation and mining for a smart factory model, showing benefits of using BPM technologies to analyze, control, and adapt discrete production processes in IIoT.
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PublicationHoloFlows: modelling of processes for the Internet of Things in mixed realityOur everyday lives are increasingly pervaded by digital assistants and smart devices forming the Internet of Things (IoT). While user interfaces to directly monitor and control individual IoT devices are becoming more sophisticated and end-user friendly, applications to connect standalone IoT devices and create more complex IoT processes for automating and assisting users with repetitive tasks still require a high level of technical expertise and programming knowledge. Related approaches for process modelling in IoT mostly suggest extensions to complex modelling languages, require high levels of abstraction and technical knowledge, and rely on unintuitive tools. We present a novel approach for end-user oriented--no-code--IoT process modelling using Mixed Reality (MR) technology: HoloFlows. Users are able to explore the IoT environment and model processes among sensors and actuators as first class citizens by simply "drawing" virtual wires among physical IoT devices. MR technology hereby facilitates the understanding of the physical contexts and relations among the IoT devices and provides a new and more intuitive way of modelling IoT processes. The results of a user study comparing HoloFlows with classical modelling approaches show an increased user experience and decrease of required modelling knowledge and technical expertise to create IoT processes.Type:Journal:
Scopus© Citations 33 -
PublicationImmersives verteiltes Robotic Co-working(Springer, 2020-09-02)
Scopus© Citations 1 -
Publication
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PublicationTeaching Computer Science Fundamentals to Business Students: A SoTL Experience Report on How to Increase Student Engagement via Practical Programming Tasks and AIThe current hype about artificial intelligence (AI) attracts many researchers and students to computer science (CS) education. New course offerings in various study programs emerge that teach concepts and applications of AI and data science. At the same time, interest in more fundamental theoretical computer science topics declines, even though basic knowledge in data structures and algorithms is the foundation of many modern AI and software applications. In this work we investigate an elective course teaching fundamentals of computer science to business students. The course is covering mostly theoretical CS concepts including data structures, sorting and search algorithms, computational complexity, and graphs, accompanied by small Python code examples. It used to be taught in a traditional front-of-class style to the non-CS students, which is among the reasons why it suffers from decreasing numbers of students and low student engagement. In this experience report, we describe the implementation of an 18 months-long teaching innovation project following the Scholarship of Teaching and Learning (SoTL) methodology to modernize the course with interactive programming tasks and the application of AI. We analyze the initial situation of student engagement based on lecturer experience, expert interviews, and observations. Then we design new interactive course elements in the form of short in-class programming exercises that are aligned with the lecture contents to increase student engagement. These exercises cover the aforementioned theoretical topics and revolve around analyzing different real world datasets collected from a smart factory. The tasks involve learning about typical data analysis and pre-processing tasks such as data cleaning, reordering, filtering, and visualization that can all be linked to the theoretical CS concepts. We combine these practical tasks with the use of AI to generate source code such that students can directly apply the lecture contents in an interactive, introductory programming setting. The analysis of this new course format based on observations and a small user study among participating students shows an increase of student engagement and an overall improvement of the learning atmosphere. The practical tasks and opportunity to work with a real world dataset from a smart factory were well received by the students. These insights may be beneficial for other computer science educators outside of the computer science majors to partially adopt the new interactive formats for an improved student engagement. We provide open access to the datasets and Python-based programming exercises in Jupyter notebooks via a public repository such that lecturers can adopt and embed the teaching materials both in digital or physical learning settings. We view the results of this teaching innovation project as a first successful step towards transitioning the course from classical teaching to a more interactive and engaging learning format.
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PublicationRevision of a Smart Factory Software Architecture from Monolith to Microservices( 2024-09)
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PublicationMethod to Identify Process Activities by Visualizing Sensor EventsWith the onset of the Internet of Things (IoT) everyday objects suddenly become data sources equipped with sensors measuring the object’s properties and surroundings. However, the lack of process-awareness in IoT environments (e.g., smart factories) prevents the adoption of more sophisticated process analysis and optimization. One hurdle is the differing abstraction level of low-level sensor data and process-level activities. We propose a method to identify activities step-by-step from raw IoT data using visualizations. By relying on minimal process knowledge, we discover process activities from sensor events. These activities are represented by specific sequences of sensor events–Activity Signatures–that serve as a basis for finding similar activities. We demonstrate the method’s validity with a proof of concept in a smart factory.