Knowledge

• Knows relevant theoretical foundations of M2M and IoT
• Knows relevant gateway and processor architectures
• Knows relevant protocol stacks and communication systems
• Knows methodical background of IoT system design
• Is aware of critical limitations of IP based protocols, esp. in real time tasks

Skills

• Can model IoT and M2M systems
• Can implement embedded systems into IoT systems
• Can apply state of the art tools for SW for embedded systems
• Can select IoT and M2M platforms according to system requirements

Competence - attitude

• Can discuss IoT device and gateway systems with experts
• Can lead cross domain design for IoT systems
• Understands SW and HW experts and translates between different domains

The module is intended to give students to competence to understand, analyze, develop, set up and evaluate digital systems based on the latest scientific state of the art. This involves the basic layers of the Internet-of-Things (IoT) stack including M2M devices and gateways, the relevant protocol stacks for IoT and the relevant communication network technologies (both wireless and wireline). During the
module, students will set up a complete IoT device with all relevant functionality to be connected to the cloud. Recent topics from research projects (e.g. connected car, smart home) complement the course with the aim to stimulate discussion of scientific results.

Course Structure

1. Introduction to M2M and IoT devices and gateways
2. Processor architecture for embedded devices and gateways
3. IP based communication
4. IoT and M2M protocols
5. Communication gateway architectures
6. Wireline communication networks and standards
7. Wireless communication networks and standards
8. Case study of a state-of-the-art application, e.g. connected car or industry 4.0

none

Assessment of the course: Theoretical knowledge: Written Exam at the end of the course (50%) and Practical Skills: Individual programming task (50%): implementation of an IoT device, gateway and pro- tocol stack system => demonstration of the result

MOD2-03 - Digital Systems 2

5,00%

References

Andrew S. Tanenbaum, David J. Wetherall: Computer networks, 2014

Peter Prinz, Tony Crawford, C in a Nutshell, 2nd Edition, 2015

Herbert Schildt, Java: The Complete Reference, Eleventh Edition

K.C. Wang, Embedded and Real-Time Operating Systems, 2017

OWASP Foundation, „Open Web Application Security Project,“, [Online] Available: https://www.owasp.org/index.php/Main_Page

BSI - Federal Office for Information Security, “Protection profile for the gateway of a smart metering system,” 2014, [Online] Available: https://www.bsi.bund.de

BSI - Federal Office for Information Security, “BSI TR-03116-4,” 2012, [Online] Available: https://www.bsi.bund.de

„RFC 4253: The Secure Shell (SSH) Transport Layer Protocol“, [Online] Available: https://tools.ietf.org/html/rfc4253

„RFC 7252: The Constrained Application Protocol (CoAP)“, [Online] Available: https://tools.ietf.org/html/rfc7252

W3C, „Web of Things (WoT) Thing Description,“ 16 May 2019. [Online]. Available: https://www.w3.org/TR/wot-thing-description/.

OpenAPI Specification (Version 2.0), [Online] Available: https://swagger.io/specification/v2/
Research (Examples for selected papers)

M. Niemeyer und I. Kunold, „Security Aspects of Cyber Physical Systems and Services,“ in Smart Energy 2016 Digitalisierung der Energieversorgung — Treiber und Getriebene, Dortmund, vwh, 2016.

B. M. H. Alhafidh, W. H. Allen, “High Level Design of a Home Autonomous System Based on Cyber Physical System Modeling”, IEEE 017 IEEE 37th International Conference on Distributed Computing Systems Workshops (ICDCSW), July 2017

Hoeller and R. Toegl, “Trusted Platform Modules in Cyber-Physical Systems: On the Interference Between Security and Dependability “, 2018 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), London, 2018, pp. 136-144.

Knowledge

• Knows the theoretical background of the design thinking method
• Knows different software development processes especially agile software development
• Knows required steps and processes for agile software development
• Knows how to express software architectures based on the UML Diagrams
• Knows how to use tools like git, checkstyle, bug tracking and issue management systems

Skills

• Can conceptualize a software based on the design thinking method
• Can apply and choose between software development processes
• Can setup and manage a team based on agile principles
• Can work on a software development project

Competence - attitude

• Can work in a team on scientific topics
• Can present and defend scientific results in front of an audience
• Can discuss the topics related to the lecture
• Can understand related topics and translate between different domains

Innovation driven software engineering touches every aspect of modern software development. Today's software emphasizes novelty, usability, and joy of use. Modern software is usually created in creative and highly iterative processes. Many steps in these processes involve potential users. This integration of the user can be addressed with the so-called Design Thinking method.
Refined ideas and prototypes can be the foundation for new startup companies. One way to check the viability is the Business Model Canvas. Agile Software Development puts the focus back on user feed- back and iterations. The agile development process is accompanied with an extensive tool chain for designing and creating software solutions. For instance, UML Diagrams, Version control systems, Bug tracker and ticket management systems.

Course Structure

• Design Thinking
• Business Model Canvas
• Legacy process models
• Agile Software Development
• Agile Manifesto best practices
• UML Modeling
• Tooling like git, Bug tracker and ticket management systems, Checkstyle, etc.

none

Assessment of the course: Theoretical knowledge (40%): Written Exam at the end of the course, Practical Skills (40%): realizing a small real-world project within the lecture related topics of innovation dri- ven software engineering and Scientific Competences (20%): written paper (literature review, approx. 10 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund)

Passed exam and passed semester assignments

MOD2-01 - Usability Engineering

MOD-E03 - Human Centered Digitalization

5.00  %

Solving Problems with Design Thinking - Ten Stories of What Works, Jeanne Liedtka, Andrew King, Kevin

Bennett, Columbia Business School Publishing, 2013, ISBN: 0231163568
Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, Alexander Osterwalder, Yves Pigneur, John Wiley & Sons, 2010, ISBN: 9780470876411

Software Engineering, Ian Sommerville, Addison Wesley Pub Co Inc, 2015, ISBN: 0133943038

Knowledge: Upon completion of this module, students will be able to:

• Understand the core issues of agile projects.
• Know software development and deployment concepts and processes, such as DevOps and CI/CD.
• Explain methods for user participation in the software development process.
• Understand cooperation in virtual teams using collaboration tools.
• Explain and compare methods for managing agile projects, especially Scrum and Kanban.
• Explain and compare workflows and design flows for agile projects.

Skills: Upon completion of this module, students will be able to:

• Conduct a software development project in an agile team, using Scrum in a virtual collaboration setting.
• Apply tools for managing software development projects.
• Develop tailored processes for managing software development projects.
• Define team roles, especially Scrum Master and Product Owner.
• Set up IT environments for collaboration in virtual teams.

Competence - attitude: Upon completion of this module, students will develop the ability and attitude to:

• Cooperate in a virtual team using online collaboration tools.
• Develop an agile mindset.
• Handle complexities while working in groups.
• Present and defend team results in a complex virtual environment.
• Develop team competencies among the members.
• Perform successfully in an agile virtual team and accomplish tasks.
• Reflect on team situations, address resulting issues, and find solutions.
• Cooperate with a team of software developers from other Master's programs and manage interdisciplinary work successfully.
• Manage teams and projects in intercultural and international settings.
• Compile findings and literature reviews into scientific papers on virtual team collaboration in agile cross-border projects.

This course offers students a systematic approach to the agile management of projects, specifically software development projects. As the main example case, the development of software in virtual team environments using agile methodology is considered. This is part of Software Engineering Methodology, User Centered Design Methodology and Project Management Methodology. The intention of the course is to prepare the students on managing complex software development projects with distributed teams. The focus is the introduction of modern software development processes and the discussion of the implication of these processes on project management. One core aspect is the consideration of the recent and ongoing research on virtual collaboration in cross-border teams. Basic aspects of agile methods and practices are not the focus of this advanced course, but a refresher course on Scrum and reading materials about agile practices are provided.

Course Structure

The module has 3 core elements:

1) Introduction to Software Engineering Processes (lectures)
a)    Introduction to Agile Software Development (SW) Projects
b)    Refresher Course on Scrum
c)    Software Engineering Methodology, esp. DevOps, CI/CD
d)    User Centered Design

2) Project Simulation of an Agile SW Development Project in a virtual setting (team project)
a)    Setting up the team and assigning the roles, especially Scrum Master and Product Owner (based on a Belbin Test for all team members and reflection on own team/project personality)
b)    Developing an idea for a mobile app (based on a selection of cases) and pitching of the idea and the project planning as a kick-off event.
c)    Conducting 2 months of (weekly) sprints, documentation and review of project artefacts
d)    Demonstration of a klick prototype and final project review

3) Research Seminar on virtual collaboration in agile cross-border SW development projects
a)    Introduction to scientific methodology, especially literature reviews and paper writing
b)    Review and discussion of the recent research in the field, selection of topics for own paper
c)    Preparation of a scientific paper in group work (approx. 2 months)
d)    Peer review of the papers and assessment
e)    (if possible) submission to a scientific conference and presentation

Students will be guided through a case study project. They form agile teams and collaborate in the project execution via IT tools. In addition, they write a scientific paper as group work.

• Lectures introducing concepts, methods and tools
• Project simulation (agile, virtual SW development projects with Scrum) on the case study of a mobile app development, in mixed teams with SW developers from another international Master's program. Several sprints are conducted over 2 months' time. Review meetings with teachers and 2 reviews in the plenary.
• Group work on writing a scientific paper, peer review by students and teachers
• Presentations to communicate results

none

Assessment of the course: Theoretical knowledge: Oral exam at the end of the course (20%), Practical Skills: Group assessment on results of project simulation (50%) and Scientific Competences: paper presentation (30%)

Usability Engineering (MOD2-01)

Requirements Engineering (MOD-E02)

Managing Digital Change (MOD-E08)

5,00%

Breyter, Mariya (2022): Agile Product and Project Management: A Step-by-Step Guide to Building the Right Products Right, 1st ed. Edition, Apress

Rose, Robert F. (2022): Software Development Activity Cycles: Collaborative Development, Continuous Testing and User Acceptance, 1st ed. Edition, Apress

Schwaber, Ken; Sutherland, Jeff (2020): The Scrum Guide - The Definitive Guide to Scrum: The Rules of the Game, online https://www.scrum.org/resources/scrum-guide

Martin, Robert C. (2014):  Agile Software Development, Principles, Patterns, and Practices, First Edition, Pearson New International Edition, Pearson

Atlassian: The Agile Coach: https://www.atlassian.com/agile, last visited March 31, 2024

Agile Alliance: https://www.agilealliance.org/, last visited March 31, 2024  

Scrum.org: https://www.scrum.org/, last visited March 31, 2024 

Scrum Alliance: https://www.scrumalliance.org/, last visited March 31, 2024  

Scaled Agile Framework, SAFe 6.0: https://scaledagileframework.com/, last visited March 31, 2024 

Project Management Institute (PMI) (2017): Agile Practice Guide, online www.pmi.org

International Project Management Association (IPMA) (2018): IPMA Reference Guide ICB4 in an Agile World, online www.ipma.world

Lous, Pernille; Kuhrmann, Marco; Tell, Paolo (2017): Is Scrum Fit for Global Software Engineering? 2017 IEEE 12th International Conference on Global Software Engineering (ICGSE), IEEE Xplore

Hummel, Markus; Rosenkranz, Christian; Holten, Roland (2013): The Role of Communication in Agile Systems Development - An Analysis of the State of the Art, Business & Information Systems Engineering 5

Šmite, Darja; Moe, Nils Brede;  Gonzalez-Huerta, Javier (2021): Overcoming cultural barriers to being agile in distributed teams. Information and Software Technology, 138

Saunders, Mark; Lewis, Philip; Thornhill, Adrian (2019): Research Methods for Business Students, 8th edition, Pearson

Knowledge: Upon completion of this module, students will be able to:

• know research methods and tools of the digital transformation (scientific) domain
• know and understand the culture of different partner countries
• know programming languages and modeling techniques
• know web development techniques, languages, tools and frameworks
• have IT literacy in tools like MS Excel, Word and Powerpoint
• know German vocabulary and grammar at least on A1 level
• know English vocabulary and grammar at least on C1 level

Application and generation of knowledge:

The students are able to

• apply research methods and tools of the scientific domain
• work in international and intercultural settings
• can program software in Java (alternative: C# or Python)
• can model system in UML (or sysML)
• can develop basic web applications
• use tools like MS Excel, Word and Powerpoint proficiently
• speak, understand, read and write German at least on A1 level
• speak, understand, read and write English at least on C1 level

Communication and cooperation:

• Students can cooperate in a cross-border project with international students
• Students can adapt and to cope with different European cultures
• Students learn to communicate with people from different countries

Scientific self-understanding / professionalism:

• Students can plan and conduct scientific research in their field
• Students are aware of their own cultural background and can interact with other cultural background adequately

The module provides a set of several smaller training units to the students where they can choose in order to fill gaps from previous studies or add specific competences. Up to 9 courses are offered in the winter term (according to availability). The intercultural training (see list below, No. 1) is mandatory for all students. Students have to choose 3 out of 6 optional training units (from No. 2-7). For students without at least German A1, the German course (No. 8) is mandatory. For German native speakers, another language course has to be concluded at least on A1 level (No. 9). More courses can be added according to the analysis of the needs:

Course Structure

In the initial set up of the master a selection of 8 compact courses are offered. More can be added according to the analysis of the needs of actual students:

1. Intercultural Training (ICT): The intercultural training is intended to help the students to interact and work successfully with their teachers and peers at the university. It is also conducted as a team building event for the new class in the first semester. It should also motivate students for a later mobility/exchange with the partner universities.
2. Compact Web Development Course (online): This course delivers the basics of web programming languages and frameworks. It is intended for catch up for students with only very limited web development skills.
3. Compact Programming Course (Java, alternatives: C# or Python): This course delivers object-oriented programming skills in Java (decision is made prior to semester start, can be switched to C# or Python depending in the language used in the 1st semester). It is intended for catch up for students with limited programming skills.
4. Modeling of Software Systems (UML): This course delivers object-oriented modeling skills in UML. It is intended for catch up for students with limited software and systems engineering skills.
5. Research Methods and Tools - part A (RMT-A): Introduction to scientific methods and tools in the digital transformation domain. Furthermore, analysis of relevant scientific trends and communities. Students can prepare for scientific work via the sequence of RMT-A and RMT-B plus a Research Seminar.
6. Cross-Border Project A: During the November Master block week or a workshop at a partner university, projects with teams of students from several partners are formed. They conduct projects, e.g. on industry cases and present the results, e.g. in pitching.
7. ICDL-Excel: students who lack relevant IT skills can take part in the preparation courses for the International Computer Driver License (ICDL) at Fachhochschule Dortmund and do the respective exams. The Excel course puts the focus on using Excel for data analytics and business intelligence.
International Project Communication 1 e (German A1): A language certificate of German at least on level A1 has to be provided at the end of the semester. Respective courses are organized and embedded into the weekly schedule.
8. International Project Communication 1 g (other language): For students with native German background (e.g. German/Austrian/Swiss citizens or students with a prior degree taught in German (e.g. "Bildungsinländer"), a language certificate in an additional language (e.g. French, Spanish, Chinese, etc.) at least on A1 level is required. In case of an English language certificate, C2 level is needed

none

Depending on choice of courses

5,00%

Loy, M., Niemeyer, P., Leuck, D. (2023). Learning Java: An Introduction to Real-World Programming with Java, 6th Edition, O-Reilly Media

Miles, R., Hamilton, K. (2006). Learning UML 2.0: A Pragmatic Introduction to UML 1st Edition, O-Reilly Media

Dresch, A., Pacheco Lacerda, D., & Valle Antunes Jr., J. A. (2015). Design Science Research: A Method for Science and Technology Advancement. Springer International Publishing Switzerland

Bailey, S. (2018). Academic Writing – A Handbook for International Students (5th ed.). Routledge, New York

Saunders, M., Lewis, P., Thornhill, A. (2019). Research Methods for Business Students, 8th edition, Pearson

Bryman, A., Bell, E. (2011). Business research methods, 3rd Edition, Oxford University Press

Creswell, J.Q. (2022). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 6th edition, Sage Publications

Mayring, P. (2021). Qualitative content analysis, Sage Publications, 1st Edition

Jordan, C. (2022). ICDL Excel: A step-by-step guide to spreadsheets using Microsoft Excel

Knowledge

• Knows the concepts and structure of modern software architecture styles
• Knows technologies and tools related to the operation of modern software architectures
• Knows particular challenges of operating distributed systems
• Knows how to analyze an application by different metrics
• Knows to maintain and operate a distributed system
• Knows how to distribute a system based on workload of particular components

Skills

• Can critically evaluate the suitability of an architectural style given a particular problem
• Can design, develop and operate leveraging the lecture topics
• Can assess and improve an existing software architecture
• Can analyze a distributed system by different application metrics
• Can distribute a container-based system by workload

Competence - attitude

• Can discuss and assess the differences between various architectural styles
• Can communicate and explain architectural decisions
• Can work in a team on scientific topics
• Can demonstrate and discuss results in a group

In recent years' new architectural styles have emerged to cope with the increasing need of highly scalable and distributed systems. Among them are Microservices and Self-Contained Systems. The resul- ting systems are characterized by being componentized into independent services which communicate using well-defined interfaces.
This course the students learn about modern software architecture paradigms, both conceptually and practically. Additionally, subjects related to the operation of such systems are covered, such as infra- structure-technologies and particular challenges of operation like scaling or load balancing.
In addition to the lectures, the students have the opportunity to apply their knowledge in project-based group activities.

Course Structure

• Historical development of software architecture paradigms.
• Characteristics of modern architectural styles.
• Designing Microservices and Self-Contained Services.
• Developing Microservices and Self-Contained Services.
• Infrastructure, deployment, and operation: Methods, technologies and challenges.

none

Assessment of the course: Theoretical knowledge (40%): Theoretical knowledge (40%): Written Exam at the end of the course, Practical Skills (40%): Individual programming task, realizing a small real-world project within the lecture related topics of software architectures and Scientific Competences (20%): written paper (literature review, approx. 10 pages) and presentation (in class or at a student confe- rence, e.g. International Research Conference Dortmund)

MOD2-02 - Software-intensive Solutions

MOD-E01 - Software Engineering Project

5,00%

Newman, S.; Building Microservices, O’Reilly Media, 2016
Wolff, E.; Microservices: Flexible Software Architecture, Addison-Wesley, 2016

Learning outcomes

Knowledge

• Knows relevant theoretical foundations of internet security
• Knows relevant architectures for trusted platforms
• Knows relevant secure communication protocols
• Know the theoretical background of the operator controller module (OCM)
• Know methodical background of real time system design
• Is aware of critical limitations of CPS security and real-time OS

Skills

• Can develop a secure IoT system
• Can implement real-time OS into IoT systems
• Can apply state of the art tools for CPS security
• Can select embedded OS according to system requirements

Competence - attitude

• Can discuss CPS security issues with experts
• Can lead cross domain design for IoT systems based on OCM
• Understands the connections between cloud security and IoT security

Course Description

The module is expanding student competence to understand, analyze, develop, set up and evaluate digital systems based on the latest scientific state of the art. This mainly involves the topics security in cyber-physical systems (CPS) and operating systems. During the module, students will develop a secu- rity concept for the IoT devices from Digital Systems 1. Furthermore, they will structure an application with real-time requirements according to the operator controller module (OCM) and select an appro- priate operating system for the device. Recent topics from research projects (e.g. smart grid, eMobility) complement the course with the aim to stimulate discussion of scientific results.

Course Structure
1.    Introduction to internet security for CPS
2.    Architectures for trusted platforms
3.    Secure communication
4.    Intrusion detection and advanced methods in CPS
5.    Authentication, data protection and privacy and IoT systems
6.    Introduction to the Operator-Controller-Module
7.    Real-time processing
8.    Operating systems (OS) and databases for embedded systems
9.    Case study of a state-of-the-art application, e.g. smart grids

Application Focus
Project IoT System: students will use the security system for the IoT system from the previous semester. Furthermore, they will implement an application with real-time aspects based on a selected operating system. The respective case study will be taken from a recent R&D project or an industry case. The result will be a demonstrator system.
Trainings: students attend a training for CPS security tools from Institute for Internet Security.

Scientific Focus
Students will do a scientific evaluation of the security issues in a specific domain (e.g. eMobility char- ging systems) based on recent scientific literature.

Skills trained in this course: theoretical knowledge, practical skills and scientific competences

Teaching and training methods

• Theoretical knowledge: e-learning modules on IoT security and operating systems, tool tutorials
• Practical Skills: Projects, Labs & Exercises, continuation of the small project with an IoT device
• Scientific Competences: own research on IoT security issues

Input from:

MOD1-03 - Digital Systems 1

Assessment of the course: Theoretical knowledge: Written Exam at the end of the course (50%) and Practical Skills: Individual programming task (50%): implementation of an IoT security system in device, communication and cloud level (e.g. based on Eclipse IoT stack) => demonstration of the result

Passed exam and passed semester assignments

Input for:

MOD-E09 - Smart Home & Smart Building & Smart City

MOD-E10 - Edge Computing

 

5,00%

References

CERP-IoT: Vision and Challenges for realizing the Internet of Things, European Union, 2010

J. Clarke, N. Suri, A. Sharma: Trust and security of the Internet of Things (IoT), BIC Discussion Paper, Coordinated by Waterford Institute of Technology, Cork Road, Waterford, Ireland, 2012

IoT-A: Internet-of-Things-Architecture, FP7 Project Home Page, Retrieved from http://www.iot-a.eu/ public/front-page , last accessed June 06, 2013

Gausemeier, J., Steffen, D., Donoth, J., Kahl, S.: Conceptual Design of Modularized Advanced Mecha- tronic Systems. 17th International Conference on Engineering Design (ICED`09), August 24-27, 2009, Stanford, CA, USA, 2009

Lückel, J.; Hestermeyer, T.; Liu-Henke, X.: Generalization of the Cascade Principle in View of a Structu- red Form of Mechatronic Systems. 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2001), Villa Olmo; Como, Italy, 2001

Knowledge: Upon completion of this module, students will be able to:

• know advanced research methods and tools of the digital transformation (scientific) domain
• know and understand business models in the digital domain
• know TOGAF and enterprise IT & business architectures
• know training concepts
• have advanced IT literacy in tools like MS Excel
• know German vocabulary and grammar at least on A2 level
• know English vocabulary and grammar at least on C2 level

Application and generation of knowledge:

The students are able to

• apply research methods and tools of the scientific domain
• can develop business models based on case studies
• can develop enterprise IT architectures based on case studies
• can train users in IT tools
• use tools like MS Excel on an advanced level
• speak, understand, read and write German at least on A2 level
• speak, understand, read and write English at least on C2 level

Communication and cooperation:

• Students can cooperate in digital transformation projects
• Students can train users in digital technologies
• Students learn to communicate with people on different IT literacy levels
• Students learn to communicate in different languages, especially in German

Scientific self-understanding / professionalism:

• Students can plan and conduct scientific research in the digital transformation domain
• Students are aware of their own discipline and can interact with other discipline adequately
• Students can manage context beyond the IT technology domain

The module is the extension of the Scientific and Transversal Skills 1module and provides an additional set of several smaller training units to the students where they can develop specific competences. Up to 8 courses are offered in the summer term (according to availability). Students have to choose 3 out of 6 optional training units (from No. 1-6). For students without at least German A2, the German course (No. 7) is mandatory. For German native speakers, another language course has to be concluded at least on A1 level (No. 8). More courses can be added according to the analysis of the needs.

Course Structure

In the initial set up of the master a selection of 8 compact courses are offered. More can be added according to the analysis of the needs of actual students:

1. Compact Course on Business Models and Business Cases (TOPSIM): This course conducts a 1-week intensive workshop as a business simulation in the TOPSIM framework. The focus is on the development of a startup idea in the field of digital transformation.
2. Compact Course on TOGAF: This course conducts a 1-week intensive workshop on the TOGAF framework (The Open Group Architecture Framework). The focus is on the development of an enterprise architecture combining the business and the IT view.
3. Train-the-Trainer on IT tools for projects: The goal of the course is to let the IT students develop a training, starting from the training concept (didactics, learning objectives), then developing training materials, and finally delivering the training to students from a project management Master.
4. Research Methods and Tools - part B (RMT-B): Training on advanced scientific methods and tools in the digital transformation domain. The goal of the course is to prepare a concrete research project or a scientific publication. Students can continue the sequence of RMT-A and RMT-B plus a Research Seminar.
5. Cross-Border Project B: During the Mai Master block week or a workshop at a partner university, projects with teams of students from several partners are formed. They conduct projects, e.g. on industry cases and present the results, e.g. in pitching.
6. ICDL-Advanced Excel: This course is preparing for the Advanced Excel certificate of the International Computer Driver License (ICDL) and the respective exams. The course puts the focus on using Excel for data analytics and business intelligence.
International Project Communication 2 e (German A2): A language certificate of German at least on level A1 has to be provided at the end of the semester. Respective courses are organized and embedded into the weekly schedule.
7. International Project Communication 2 g (other language): For students with native German background (e.g. German/Austrian/Swiss citizens or students with a prior degree taught in German (e.g. "Bildungsinländer"), a language certificate in an additional language (e.g. French, Spanish, Chinese, etc.) at least on A1 level is required. In case of an English language certificate, C2 level is needed.

MOD1-05 - Scientific & Transversal Skills 1

Successful completion of course no. 1, 3 out of 6 technical courses (no. 2-7, graded), language certificate

Depending on choice of courses

5,00%

In this module, students deepen their competencies in designing software architectures of complex systems. Students learn how to design a scalable, robust and maintainable software architecture in a domain-driven manner by selecting and applying suitable principles, patterns and methods. The analysis and discussion of such software architectures is based on practical examples and concrete solutions from research projects.

Technical and methodological competence:

• Participants will be able to differentiate basic principles of software design and apply them to concrete application scenarios.
• Students will be able to differentiate, analyze, and apply key patterns at the macro- and micro-architecture level.
• The participants know relevant tools and methods for domain-driven design and can combine and implement them appropriately in concrete application scenarios.
• Students will be able to name and classify current research approaches to modeling software architectures.

Interdisciplinary methodological competence:

• The participants master the analysis of a complex problem and can break it down into subproblems. In this way, they strengthen their competencies to implement an extensive task within the context of a project over several weeks in a team.
• Students learn methods for the interdisciplinary development of solutions, e.g. together with experts without technical background.

Social skills:

• The participants develop and implement solutions cooperatively in a team.
• They are also able to present, explain and discuss their ideas and solutions.

Career Field Orientation:

• Students acquire knowledge for solving typical tasks in the area of software architectures. They are able to make and justify well-founded design decisions.
• They also gain experience in the use of essential software development tools, such as development environments or build management tools.

The module covers the following topics:

• Short repetition of the Bachelor material on software design (e.g. design patterns according to Gamma et al., Separation of Concerns, layered architecture).
• In-depth aspects of software design:
• Principles (e.g. loose coupling - high cohesion, SOLID)
• Architecture patterns (e.g. ports and adapters, CQRS)
• Methods (e.g. Domain-Driven Design, WAM approach)
• Characteristics and patterns of modern architectural styles (e.g. modular architectures, event-based architectures, microservice architectures)
• Model-driven design, development and reconstruction of software architectures

MOD1-02 Software Architectures

MOD1-03 Digital Systems 1

Assessment of the course: Theoretical knowledge (67%): Written or Oral Exam at the end of the course, Practical Skills and Scientific Competences (33%): implementation and presentation a software project

Passed exam and passed semester project assignment

MOD-E01 Software Engineering Project

5,00%

Evans E.; Domain-Driven Design: Tackling Complexity in the Heart of Software. Addison-Wesley; 2003

Richardson C.; Microservice Patterns. Manning; 2018

Martin R. C.; Clean Architecture. Prentice Hall; 2018

Bass, Len, Paul Clements, and Rick Kazman. Software Architecture in Practice: Software Architect Practice. Addison-Wesley, 2012.

Gamma E., Helm R., Johnson R., Vlissides J.; Design Patterns. Addison-Wesley; 1995

Rademacher, Florian. A language ecosystem for modeling microservice architecture. Diss. 2022.

Learning outcomes

Knowledge

• Knows relevant theoretical foundations of usability engineering
• Knows established usability engineering tools and methods (AB tests, GOMS, interviews, usability lab tests, remote tests, etc.)
• Knows the applicability of those tools and methods in a given project situation
• Knows communication concepts for different target groups (professional peers, user groups, management, etc.)

Skills

• Can observe, recognize and evaluate user behavior and behavioral patterns (e.g. analyzing video protocols from user tests)
• Can analyze context of use, derive requirements, prototype and evaluate a software system
• Can adapt and improve those methods and tools for new application areas
• Can develop communication concepts for new/adapted target groups

Competence - attitude

• Can provide a self-reliant evaluation of the recent research in a (small) given area
• Can relate and evaluate the methods and tools into the recent scientific publications
• Can critically reflect behavior (own and well as others) in general, as well as in a given situation

The course Usability Engineering is focusing on the essential methods and tools to evaluate and measure the effectiveness, efficiency and the joy of use with which a user and perform a task with a given system. The reoccurring scheme throughout the course is the User Centered Design Process. The students will learn how to observe and specify a context of use, derive requirements from it, create a prototype and evaluate it. For all those parts of the processes, specific tools and methods will be introduced, for different phases during the software development. Students will learn about the work in the area of usability engineering from a theoretical viewpoint, by studying state-of-the-art research publications, as well as from a practical point of view, by project examples and case studies. These methods and tools will be applied as well as critically evaluated and checked for potential of improvement.


Course Structure

1. Introduction
   1. Motivation
   2. Definition of usability engineering
2. Processes
   1. Usability engineering processes
   2. Integration into IT projects
   3. Potential conflicts
   4. Communicating Usability
3. Usability Engineering Tools and Methods
   1. Analyzing context of use
   2. Requirements management
   3. Concepts
   4. Evaluation
4. Additional topics:                                                                                                                                                           Coordinated with the student's interests one to three of the following topics will be chosen. The list will be adapted to take changes in the state of the art into account.
   1. Mobile Computing
   2. Individual software solutions
   3. Consumer vs. business software
   4. Industrial solutions

Application Focus

Block workshop: students attend an interdisciplinary one-week workshop where they apply the Usability Tools and Methods for an industry case (potentially together with EuroMPM, Master ESM and Master Computer Science), for example in an early project state with prototyping or in a later project state with focus on evaluation and last changes


Scientific Focus

Students prepare a homework and a presentation on an individually selected topic from recent usability engineering research, related to the project they worked on during the block workshop for the application focus, including a reflection on the lessons learned from practice in comparison to research.


Skills trained in this course: theoretical knowledge, practical skills and scientific competencies

Assessment of the course: Theoretical knowledge (40%): Written or oral Exam at the end of the course, Practical Skills (40%): realizing a small real-world project using usability engineering tools and methods during a block week and Scientific Competences (20%): written paper (literature review, approx. 10 pages) and presentation (in class or at a student conference)

Research Project Thesis (MOD3-03)

5,00%

Jakob Nielsen, Usability Engineering, Elsevier, 1994

Carol M. Barum, Usability Testing Essentials, Elsevier, 2010

Don Norman, The design of everyday things, Basic Books, 2013

Jeffrey Rubin and Dana Chisnell, Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests, Wiley, 2008

Knowledge: Students can

• explain the basics of cybernetics and systems theory
• explain and compare digital business models
• explain methods and tools for information supply chains
• explain the core concepts of DBEs

Skills: Students are able to

• analyze and develop value chains and information supply chains
• apply ICT tools for information supply chains
• develop tailored processes for DBEs

Competence - attitude

• Students train to develop and discuss concepts in teams
• Students work in teams and set up DBE environments for their respective case study project

The term "Digital Business Ecosystem" (DBE) emerged at the beginning of the 2000s by adding "Digital" to Moore's (1996) "Business Ecosystem" concept. The analysis, structuring, development and manage- ment of DBEs combine socio-economic concepts, ICT and biological concepts. Students will learn to understand, to analyze, to evaluate and to develop DBE for different application scenarios.

Course Structure
1.    Cybernetics and systems view
1.1    Biological Systems
1.2    Cybernetics and Systems theory, social theories
1.3    System models, e.g. Ropohl, Systems engineering
1.4    Evolutionary and self-organizing systems
2.    Socio-economic view
2.1    Business Ecosystems
2.2    Business processes, business models and value chains
2.3    Innovation, competition and dynamics in business ecosystems
2.3 Analysis of Case Studies
3.    ICT view
3.1    Information supply chain
3.2    ICT architectures and tools for DBEs
3.3    Efficiency and effectivity for DBEs
3.4    Analysis of Case Studies

none

Assessment of the course: Theoretical knowledge: Oral or written exam at the end of the course (50%) and Practical Skills: contributions within case study project (team presentation) => demonstration of the result (50%)

Passed exam and passed semester assignments
 

none

5,00%

F. Nashira, A. Nicolai, P. Dini, M.L. Louarn, L.R. Leon: Digital Business Ecosystem. European Commis- sion, 2010, Retrieved from http://www.digital-ecosystems.org/book/de-book2007.html, , last accessed June 06, 2013

S. Sun, J. Yen: Information Supply Chain: A Unified Framework for Information-Sharing, P. Kantor et al. (Eds.): ISI 2005, LNCS 3495, pp. 422 – 428, 2005

CERP-IoT: Vision and Challenges for realizing the Internet of Things, European Union, 2010

A. Humphreys, K. Grayson: The Intersecting Roles of Consumer and Producer: A Critical Perspective on Co-Production, Co-Creation and Prosumption, Sociology Compass 2, 2008

Knowledge

• Knows deep knowledge of formal verification methodologies
• Knows relevant theoretical background
• Knows, understands, and critically assesses specific system requirements

 
Skills

• Can apply advanced methods to novel and complex use cases
• Can designs and optimizes verification models and artefacts (e.g. properties)
• Can use and adapt UML approaches and tools (UPPAAL, TAPAAL) in innovative contexts

Competence - attitude

• Can research on state of the art and theoretical background
• Can present and critically discuss results in multidisciplinary teams
• Can structure and synthesize complex scientific fields to create new insights

Software is the driving force behind the development of software-intensive systems, which rely heavily on software to manage critical functions like hard real-time coordination between distributed components. Controllers are increasingly implemented through software.
Communication in software-intensive systems involves not only system and environmental data but also complex status information on protocols and communication channels, which can greatly impact component behavior.
This leads to highly complex hybrid systems that combine discrete and continuous processes. In safety-critical environments, software-intensive systems, require formal verification to ensure the correctness of specified properties and system behavior.
In the course, concepts and methods for the modeling and verification of software-intensive systems are introduced and formally described. To enable efficient verification of these systems, techniques such as abstraction, decomposition, and rule-based modeling are employed. These non-orthogonal techniques are skillfully combined to enhance their effectiveness. A key objective is to manage models across all relevant domains.
The proposed approach for model-based verification of mechatronic systems is distinguished by the integration of efficient verification techniques tailored to each domain, leveraging domain-specific, model-based knowledge.

Course Structure
1. motivation:

•    What are Formal Methods?
•    Why should we use Formal Methods?
•    When in the overall development process should we use Formal Methods?

2.    Introduction to Model Checking
3.    Introduction to Theorem Proving
4.    Write scientific paper on Formal Methods + Recent Research (literature review)
5.    Formal Verification in practice: Case study (Smart Farming, Smart Cities)

 

Assessment of the course: Write scientific paper (10 pages)  (50%) + semester assignments: group work as homework (40%) + demonstration and presentation (15min) (10%)

Passed exam (accepted paper) and passed semester assignments

 

Connects to (ESE):

• MOD-E04 - SW Architectures for Embedded Systems

5,00%

Reisig, W. (2013): Understanding Petri Nets – Modeling Techniques, Analysis Methods, Case Studies, Springer

Clarke, E.M., & Grumberg, O., & Peled (1999):, D.A.: Model Checking, MIT Press

Baier, C., & Katoen, J.-P. (2008): Principles of Model Checking, MIT Press

Spivey, J.M. (2001): The Z Reference Manual (https://github.com/Spivoxity/zrm/blob/master/zrm-pub.pdf)

Ruhela, V. (2012): Z Formal Specification Language – An Overview, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 01, Issue 06

http://www.tapaal.net

http://www.uppaal.org

Knowledge

• Knows relevant theoretical foundations, area: computer science and society
• Knows methodical background of case studies and surveys
• Is aware of critical limitations of methods for evaluating impact

Skills

• Can analyze the impact of changes in information technology on individuals, environment and society, based upon a given past scenario
• Can evaluate, analyze (and within limits predict) the impact of new products/services on individu- als, environment and society, during the concept and development phase
• Can conduct methodologically structured evaluations (e.g. field observation, lab tests) and surveys

Competence - attitude

• Can discuss impacts of changes in information technology on individuals, environment and society with experts
• Can advise during product/service development potential impacts of product/service structure/fea- tures on individuals, environment and society
• Understands scientific publication in the related areas

Digitalization in private and professional domains is influencing intensely and sometimes even revo- lutionizing people's life, the way they interact with systems, the way they interact between each other, the way a society changes. Within this course those influences will be addressed from two different viewpoints. From an analytical perspective, former and current developments and their influences will be analyzed and then projected on future trends. From a constructive perspective, those potential influ- ences of e.g. a product or service currently in development will be taken into account to shape the pro- spective solution.

Course Structure

• Basic Overview "Computer Science & Society"
• Ethics in computer science
• Digital media and art
• Surveillance and privacy
• Artificial intelligence and responsibility
• Case Studies "Disruptive Changes by Information Technology"
• Digitalization of work life & work environments, processes, products and services
• Evaluation of impacts (personal, environment, society)

Application Focus

Case Studies "Disruptive Changes by Information Technology"
Involvement in projects: Analyzing impacts and potentials for news products and services

Scientific Focus
(Pre-)Studies & surveys about socioeconomic impacts of digitalization Paper with literature review/state-of-the-art


Skills trained in this course: theoretical knowledge, practical skills and scientific competences

Innovation Driven Software Engineering (MOD1-01)

R&D Project Management (MOD1-04)

Usability Engineering (MOD2-01)

Assessment of the course: Practical Skills (50%): Group work and/or individual task, case studies and projects => demonstration/presentation of the result an Scientific Competences (50%): written paper (literature review, study report or survey, approx. 25 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund)

R&D project & Thesis

5,00%

Changing conference proceedings and journals, e.g.

ICT and Society: 11th IFIP TC 9 International Conference on Human Choice and Computers, HCC11 2014, Turku, Finland, July 30 - August 1, 2014, Proceedings 431 IFIP Advances in Information and Com- munication Technology, Springer, 2014, ISBN 3662442086, 9783662442081

eHealth: Legal, Ethical and Governance Challenges, Carlisle George, Diane Whitehouse, Penny Duque- noy, Springer Science & Business Media, 2012, ISBN 3642224741, 9783642224744

An Ethical Global Information Society: Culture and democracy revisited
IFIP Advances in Information and Communication Technology, Jacques J. Berleur, Diane Whitehouse, Springer, 2013, ISBN 0387353275, 9780387353272

Human Choice and Computers: Issues of Choice and Quality of Life in the Information Society
Band 98 von IFIP Advances in Information and Communication Technology, Klaus Brunnstein, Jacques Berleur, Springer, 2013, ISBN 0387356096, 9780387356099

Knowledge
Student can

• explain the basic characteristics of data and data collection
• explain advanced functionality of Excel
• explain database and data warehouse concepts
• explain the core concepts of data analytics and business intelligence

Skills

• develop data collection experiments with online tools
• apply MS Excel for data analytics
• set up and use simple SQL databases
• set up and use tools for statistical data analysis
• use IBM Watson for AI experiments

Competence - attitude

• students train to do surveys with people from different cultural backgrounds
• in discussion students develop a critical attitude to data based decision making and to issues like privacy and data protection

Modern management is based on facts and on data. Dealing with data, analyzing data and deriving conclusions and decisions from data is crucial for management. The module is developing the topics of information processing and data analytics along a case study.


Course Structure

1. information processing and data collection
1.1 Development of indicator systems
1.2 Design of data collection experiments with online tools
1.3 IT tools for data collection
1.4 Advanced MS Excel

2. data bases and data warehouses
2.1 Introduction to databases, SQL
2.2 Data warehouse systems
2.3 Cloud based systems
2.3 Analysis of Case Studies

3. data analytics
3.1 Data refinement
3.2 Data analytics and business intelligence
3.3 Probabilistic methods
3.4 Artificial intelligence and learning (introduction to IBM Watson)

none

Assessment of the course: Theoretical knowledge (30%): Written or oral Exam at the end of the course, Practical Skills (50%): contributions within case study project (team presentation) and Scientific Competences (20%): written paper (report, approx. 10 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund)

Passed exam and passed semester assignments
 

none

5,00%

References

Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, Bob Becker: The Kimball Group Reader: Relentlessly Practical Tools for Data Warehousing and Business Intelligence, John Wiley & Sons 2010, ISBN 9780470563106.

Scott Cameron: Microsoft® SQL Server® 2008 Analysis Services Step by Step, Microsoft Press 2000

The students know modern machine learning methods and can design, implement, apply and analyze them in the context of general information systems as well as in the biomedical domain. They can evaluate existing methods and can judge, if machine learning algorithms are a potential solution for
a given problem. They know several successful real-world applications of machine learning methods. They know and can apply formal and theoretical analysis methods in computational intelligence and machine learning. They are able to discuss the ethical problems of a given machine learning system.

This course gives an introduction into machine learning. From basic methods (nearest neighbor, decision trees, ...) to modern deep learning approaches (Convolutional Neural Networks, Transformer architectures) everything will be introduced and applied in the lab practice. Structured and unstructured data (video, image, audio, text) will be considered with machine learning techniques. Machine Learning is not always the best solution (a hammer is not always the best tool), we discuss the limitations and ethical dimensions of potential solutions. A speciality of this course are mini-projects that are implemented by teams of participants in collaboration with local companies, who propose the topics. The mini-projects results will be presented in a workshop with company participants.

Scientific Focus

Understanding of the function of classical and deep learning based machine learning algorithms. Knowledge about limitations and potential explainability of methods. Rigorous evaluation of machine learning models, avoiding common pitfalls like overfitting, information leakage and others.

none

Passed exam and passed semester assignments
 

none

5,00%

Witten, E. Frank, M. Hall und C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4. Edition, Morgan Kaufmann (2017) – electronic version via intranet access possible

C. M. Bishop, Pattern Recognition and Machine Learning, Springer (2006)

E. Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), Third Edition, MIT Press (2014)

I. Goodfellow, Y. Bengio und A. Courville: Deep Learning, MIT Press (2016) – free version available https://www.deeplearningbook.org

Knowledge and understanding: The students

• can explain the basics of the digital transformation in organizations
• can explain and compare digital business models
• know methods and tools for change management
• know the characteristics and specifics of digital change
• can explain the various aspects involved in setting up and running a company
• know maturity models and leadership concepts

Skills: The students are able to

• analyze and develop digital transformation projects
• apply change management to organizations
• design people development and training concepts for digital change
• develop tailored concepts for sustainable digital transformation

Communication and cooperation: The students have the ability to

• develop and discuss concepts in teams
• support teams as change agent or technology steward
• communicate, facilitate and motivate digital change
• present the results to companies and discuss in a professional context

Scientific self-understanding / professionalism: The students have developed the attitude to

• foster and promote digital change
• develop an ethical sense towards digital change and an entrepreneurial mindset
• think strategically in an uncertain environment
• work in teams and set up a digital transformation project for the respective case study

The digital is to a relevant extent a change process with a huge impact on organizations, processes, business model, the socio-economic environment and finally the affected hum beings. Managing the digital change means doing change management in a very specific context by implementing change projects. The module intends to give students a scientific insight into the relevant underlying mechanisms of the digital change process.

Course Structure

1)    What is Digital Change?

•     Digital Transformation - Incremental Change & Disruption
•     Definitions & Characteristics of Digital Change

2)    Manage the Pace - Practice - Collaboration

•     New Digitalized Forms of Management, Iterative & Incremental
•     Business Models and Business Relations in the Digital Era
•     Change Management (Lewin, Kotter ...)
•     Digital Transformation of Organizations - Maturity Models
•     Chances and Risks of Digital Transformation in Organizations

3)    Manage the Learning - People - Agility

•     Leadership in the Digital Age
•     Entrepreneurial Mindset, Culture & Ethics
•     Developing Competences, People and Teams
•     Change Agents & Technology Stewards

4)    Manage the Uncertainty - Perspective - Innovation

•     Strategy in the Digital Era - Scenario Based Strategy
•     Disruption
•     Lean (Startup)
•     Sustainable Digital Transformation - Impact & Responsibility

5)    Selected Topics and Specializations

•     Change vs. transhumanism vs. AI
•     Data Ethics
•     New Work based on Frithjof Bergman

The practical skills are trained by conducting a change project based on a real-world case study. This case study is elaborated in cooperation with companies or other partners from industry. The following case studies are foreseen (select one):

• conduct a digital transformation project in an existing company or organization with a focus on organisational change
• conduct a digital transformation project in an existing company or organization with a focus on the digital transformation of a business model
• develop a start-up project with a focus on a new, disruptive digital product or service

As part of the practical assignment students are required to work in groups (of 3-4) which do an analysis of the as-is-situation (e.g., market analysis, maturity assessment of an organization), develop an idea for the future to-be-situation (e.g. a new business model) and a transformation and change management plan. The learning activities can include:

• to write a business plan including financial planning.
• present a 90 second elevator pitch of the business idea
• perform a 15 minute pitch presentation to a fictional panel of potential investors

For the scientific component, students write a case study based on a real company of their choice to highlight how it managed its digital transformation. Students are encouraged to perform interviews or surveys with their case study company to gain detailed data for their case study. Student will write a scientific report in the form of an academic case study description. The case study will be presented at the end of the course as a Pecha Kucha presentation, meaning that they only have 20 slides which automatically change after every 20 seconds.

Methods are: Literature review, Case study method, Semi-Structured Interviews and Survey. Deductive own research based on the literature. Scientific reflection and discussion in the teams.

Students will be guided through a case study project. They form agile teams and collaborate in the project execution via IT tools. In addition, they write a scientific case study as group work.

• Online courses, videos, e-book, distance learning for the knowledge, possibly (virtual) lectures, provide material for further reading => use flipped/inverted classroom for discussion of topics, use exams (written, oral, online test) for competence assessment
• Project- and problem-based learning for the digital change project:

1. based on a company case provided by industry expert
2. own entrepreneurial startup project => work-integrated learning (WIL), challenge-based (e.g. with real investor pitch)
3. internship in a company => work-integrated learning (WIL)

• Case-writing method + surveys and interviews for elaboration of scientific case study => use regular reviews by teachers and industry experts, possibly peer review by other student teams => motivate to publish result as scientific paper (+ open data)
• Presentations to communicate results

none

Assessment of the course: contributions within case study project (team presentation) (50%) and writ- ten case study paper (literature review, report or survey, approx. 25 pages) and presentation (in class or at a student conference) (50%)

Passed semester assignments (2: case study project, written case study paper)

none

5,00%

Csedo, Zoltan; Kovacs, Kinga; Zavarko, Máté (2017): How does Digitalization Affect Change Management: Empirical Research at an Innovative Industrial Group. European Journal of Business and Management. 9 (36), 1-5

Dresch, Aline; Lacerda, Daniel P.; Valle Antunes Jr., José Antonio (2015) : Design Science Research - A Method for Science and Technology Advancement, Springer

Ehrhart, Mark; Schneider, Benjamin; Macey, William (2013): Organizational Climate and Culture - an Introduction to Theory, Research, and Practice. New York, Routledge

Verhoef, Peter C.; Broekhuizen, Thijs; Bart, Yakov; Bhattacharya, Abhi; Qi Dong, John; Fabian, Nicolai; Haenlein, Michael (2021): Digital transformation: A multidisciplinary reflection and research agenda, Journal of Business Research, Volume 122, Elsevier

Raskino, Mark; Waller, Graham (2016): Digital to the Core: Remastering Leadership for Your Industry, Your Enterprise, and Yourself, 1st edition, Routledge

Rogers, David L. (2016): The Digital Transformation Playbook - Rethink Your Business for the Digital Age, Columbia Business School Publishing

Barthel, Philipp; Hess, Thomas (2020): Towards a characterization of digitalization projects in the context of organizational transformation. Pacific Asia Journal of the Association for Information Systems, 12(3)

Ries, Eric (2011): The Lean Startup: How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses, 1st edition, Currency Westermann, George; Bonnet, Didier; McAfee, Andrew (2013): Leading Digital: Turning Technology into Business Transformation, Harvard Business Review Press

Sow, Mouhamadou; Aborbie, Solomon (2018): Impact of Leadership on Digital Transformation, Business and Economic Research (ISSN 2162-4860), Vol. 8, No. 3

Saunders, Mark; Lewis, Philip; Thornhill, Adrian (2019): Research Methods for Business Students, 8th edition, Pearson

Knowledge

• Knows frameworks and models for RE
• Knows relevant RE processes and interfaces to other processes
• Knows concepts and recent research on product line and variability management

Skills

• Can model requirements with RE tools
• Can set up and integrate RE tools into tool chains and design flows
• Can derive requirements in a structured and comprehensive way

Competence - attitude

• Understands the importance of RE in the early project phase
• Can set up and lead RE in a cross domain team

Requirements engineering (RE) is the very first activity in software, systems, and service development. Deriving a comprehensive set of requirements is a mandatory and critical task in the early phase of the systems engineering design flow. Requirements are the starting point and main angle for design, veri- fication & validation, and for the test and integration of systems. Configuration and change request management are connected with RE. Defining requirements and dealing with requirements in a struc- tured way is still a major area for research on tools and methodologies - especially for large and com- plex mechatronic systems. In this module, students will get specific knowledge about the state of the art and the main future challenges in RE.

Scientific Focus
Paper with literature review/state-of-the-art in RE

R&D Project Management (MOD1-04)

none

5,00%

Basics & Practitioner
Pohl, K.; Requirements Engineering: Fundamentals, Principles, and Techniques, Springer 2010.

Robertson, S. and Robertson, J.; Mastering the Requirements Process: Getting Requirements Right, Addison-Wesley, 2012

van Lamsweerde, A.; Requirements Engineering: From System Goals to UML Models to Software Speci- fications, John Wiley & Sons, 2009

Dick, J.; Hull, E.;Jackson, K.; Requirements Engineering 4th Edition, Springer, 2017

Ramachandran, M.; Zaigham, M.; Requirements Engineering for Service and Cloud Computing, Springer, 2017

Laplante, P. A.; Requirements Engineering for Software and Systems (Applied Software Engineering Series), 3rd Edition, Auerbach Publications, 2017

Research (Conferences, Journals & selected articles)

• ACM SIGSOFT
• International Workshop on Requirements Engineering and Testing, at ICSE International Conference on Software Engineering, IEEE Press
• IEEE International Requirements Engineering Conference (RE), e.g. 2019 Jeju Island, South Korea
• IEEE Transactions on Software Engineering
• IEEE Systems Journal

Peterson Rodrigues, Miguel Ecar, Stefane V. Menezes, João Pablo S. da Silva, Gilleanes T. A. Guedes, and Elder M. Rodrigues. 2018. Empirical Evaluation of Formal Method for Requirements Specification in Agile Approaches. In Proceedings of the XIV Brazilian Symposium on Information Systems (SBSI’18). Association for Computing Machinery, New York, NY, USA

Knowledge: Upon completion of this module, students will be able to:

• know research methods and tools of the digital transformation (scientific) domain
• know state of the art in a certain scientific field
• know open research questions in this field
• know relevant literature
• know how to document new findings according to scientific standards

Application and generation of knowledge:

The students are able to

• apply research methods and tools of the scientific domain
• apply appropriate research methodology
• apply deductive methods, especially literature review
• implement a project and create project results
• describe state of the art, methodology and findings in a scientific report

Communication and cooperation:

• Students can write scientific papers (in English)
• Students can present and defend results (in colloquium or at a conference)

Scientific self-understanding / professionalism:

• Students can plan and conduct scientific research in their field
• Students can compare own findings with state of the art and do a critical discussion
• Students can create new findings

Research Seminar is intended to introduce students into scientific writing, literature review and into discussion of research questions in a scientific auditory. Students will write a scientific report or essay on a recent research topic from one of the ongoing projects. The seminar will be a preparation for further work on the research project thesis and the master thesis. The intention of the seminar is to explore a certain scientific field and to formulate the scientific state of the art and the open research questions. A motivation for students will be the possibility to publish and present excellent papers at a small conference.

Course Structure
Students will select a topic from one of the ongoing projects in Digital Transformation, Software Engineering and Digital Systems. The will get individual consulting and feedback. During the semester the students will write a paper/report and present it in a colloquium at the end of the semester.
The research seminar is recommended for students who want to follow a more scientific path within the Master's program. It lays foundations for the scientific quality of the later Research Project Thesis and Master Thesis. Excellent papers will be published and presented (oral or poster) at a Master Student conference or a scientific conference.

Research seminars are done with individual supervision:

• Writing of a scientific report (individual or group homework)
• Presentations to communicate and discuss the findings
• Individual review and feedback on papers and presentations

Scientific & Transversal Skills 1 (MOD1-05)

Assessment of the course: quality and presentation of a scientific paper (100%)

5,00%

Specific scientific literature according to topic

General literature on scientific research:

Dresch, A., Pacheco Lacerda, D., & Valle Antunes Jr., J. A. (2015). Design Science Research: A Method for Science and Technology Advancement. Springer International Publishing Switzerland.

Bailey, S. (2018): Academic Writing – A Handbook for International Students (5th ed.). Routledge, New York

Bryman, A., Bell, E. (2022): Business research methods. 3rd + Edition, Oxford University Press

Mayring, P. (2014). Qualitative content analysis, Sage

Ritchie, J., & Lewis, J. (Eds.). (2014): Qualitative research practice: A guide for social science students and researchers (2nd ed.), London: Sage

Saunders, M., Lewis, P., Thornhill, A. (2023): Research Methods for Business Students (9th ed.). Upper Saddle River: Prentice Hall.

Knowledge

• Knows relevant home automation systems and standards
• Know smart building concepts (e.g. BIM)
• Knows relevant trends and projects in Smart City
• Is aware of critical limitations, esp. safety and security issues

Skills

• Can design concepts for smart home/smart building/smart city systems
• Can implement IoT, Cloud and SW components into such systems
• Can apply state of the art tools and systems (e.g. KNX)
• Can select IoT and cloud platforms according to smart home/building/city requirements

Competence - attitude

• Can discuss smart home/building/city systems with experts
• Can lead cross domain design in this domain
• Can contribute within the Dortmund Smart City Alliance

The digital transformation is a major driver for the change in people's living environment. It affects the technical design of infrastructure systems, starting from people's home via larger buildings and rea- ching up to systems like cities or districts. It covers home automation, energy and mobility systems and assistance systems. The course introduces the trends, developments and standards from the smart home, smart building and smart city domains and put them into the context of software and IoT systems. The aim is to enable students to develop larger software systems within the given context and to integrate them with other IoT and cloud systems. Therefore, it is intended to form a domain specific view on the digital transformation.

Course Structure

1.    Smart home
1.1    Home automation
1.2    Standards and bus systems (e.g. KNX)
1.3    Energy and mobility in smart home systems
1.4    Ambient Assisted Living

2     Smart Building
2.1    Building Information Systems (BIM)
2.2    Safety and Security in Smart Buildings
2.3    Facility Management and Smart Building

3.    Smart City
3.1    Smart City concepts and relevant trends
3.2    Integration of Logistics, Energy, Supplies and Mobility
3.3    Smart City platforms, esp. FIWARE
3.4    Stakeholder and Citizen Involvement
3.5    Case Study: Smart City Alliance Dortmund

MOD1-02 Software Architectures

MOD1-03 Digital Systems 1

MOD2-02 Software-intensive Solutions

MOD2-03 Digital Systems 2

Assessment of the course: Written Exam at the end of the course (50%) and Individual programming task (50%): implementation of Smart System (or parts of it), demonstration of the results

Passed exam and passed semester assignments
 

none

5,00%

to be defined

Knowledge

• Students know modeling tools for software design
• Students know concepts and processes for agile software development
• Students know how to create test suites for automated software testing
• Students know how to use typically used tools in the software engineering process

Skills

• Students can apply processes and methods to specific project needs
• Students can evaluate and use tools for developing software systems in a team
• Students can use tools to support the development process in a team
• Students can use tools to improve software quality

Competence - attitude

• Can discuss and defend results in topics related to the lecture content
• Can work in a team on scientific topics
• Can understand lecture related content and translates between different domains

The aim of this course is to provide students with theoretical and practical experience in software engineering. Therefore, the students work in teams on real world tasks in cooperation with industry part- ners. The course focuses on software architecture and software engineering principles that are the foun- dation for implementing software systems. During the course, the students need to apply agile methods to their project and team for a dynamic software engineering approach. Evaluating software tools for their project is another task within this course, e.g. continuous delivery, IDEs. In summary, the students implement the complete life cycle from requirements engineering to design over the development of a software system.

Course Structure Content

The course is training software engineering skills by applying the following competences (from pre- vious modules) within a realistic project (e.g. industry case):

• Object oriented modeling and design
• Architecture design (patterns, frameworks, libraries)
• Software testing
• Tools

1. Version control systems (Git, SVN, Mercurial SCM)
2. Code management
3. Ticket systems and bug tracker
4. (Continuous) integration and release management
5. Documentation

• Processes

1. Classical software development
2. Agile software development (Scrum)

• Requirements engineering
• Project management, project planning, quality management

MOD1-01 Innovation Driven Software Engineering

MOD1-02 Software Architectures

MOD1-04 R&D Project Management

MOD2-02 Software-intensive Solutions

Assessment of the course: Practical Skills (50%): realizing a real-world project within the User Innova- tion Center during a block week and Scientific Competences (50%): written paper (literature review, reflection of project with current research, approx. 25 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund)

none

5,00%

https://www.pearson-studium.de/software-engineering-global-edition.html (Ian Sommerville, ISBN 978-1-2920-9614-8)

http://eu.wiley.com/WileyCDA/WileyTitle/productCd-EHEP000908.html (Hans van Vliet)

Domain Driven Design (Eric Evan)

Design Pattern (Gang of Four; IBSN 978-0201633610)

(https://www2.swc.rwth-aachen.de/se_buch/ (deutsch; Horst Lichter, Jochen Ludewig))

http://www.hanser-fachbuch.de/buch/UML+2+glasklar/9783446430570  (deutsch;  ISBN 978-3-446-43057-0)

Knowledge

• Knows recent trends in Digital Transformation
• Knows the relevant scientific literature
• Knows practical cases

Skills

• Can do a structured literature review on a given topic
• Can design own research on the topic
• Can present research results

Competence - attitude

• Can systematically explore a new scientific field
• Can organize research work in an unknown field
• Can synthesize and summarize findings in a meaningful way
• Shows curiosity in scientific research

The module will introduce and discuss recent topics from scientific research and industrial R&D. The goal is to make students familiar with the trends and to encourage own scientific and practical work in the respective field. The module will use presentations by scientists and practitioners to introduce topics. Literature work including structured literature reviews and discussion of relevant research
papers will further enhance the practical knowledge. Industry presentations and visits can deliver practical insights. The module can introduce several different areas or topics, or it can dive deep into one topic. This can involve own research work of students, e.g. in order to develop a research paper for a conference (preferably a Master Student Conference). The module can also include practical labs or experiments. Individual project work or group work in small project teams can be used to develop new results. Presentations can be used to discuss the results.

Scientific & Transversal Skills 1 (MOD1-05)

Assessment of the course: Oral Exam (30 min) at the end of the course (50%) and group work as home- work (50%): research on a recent technology trend

Connects to:

• Scientific & Transversal Skills 2 (MOD2-04)
• Research Seminar
• Research Project (Thesis) (MOD3-03)
• Master's Thesis and Colloquium

 

5,00%

Specific for the recent research topic For Example:

• ACM Special Interest Group on Software Engineering (SIGSOFT)
• ACM Special Interest Group on Computers and Society (SIGCAS)
• ACM Special Interest Group on Mobility of Systems, Users, Data and Computing (SIGMOBILE)
• ACM Special Interest Group on Computer Human Interaction (SIGCHI)
• International Project Management Association, IPMA
• IEEE Transactions on Software Engineering
• IEEE Systems Journal
• ACM SGICAS Conference on Computing and Sustainable Societies (COMPASS)
• ACM/IEEE Symposium on Edge Computing (SEC)
• IEEE Transactions on Human-Machine Systems

Publications IDiAL, FH Dortmund:
https://www.fh-dortmund.de/en/idial/forschung/veroeffentlichungen_statisch.php

Learning outcomes

The student aquires the principles (protocols, architectures and applications) in computer networks. She applies technologies for network design on layer 2 and layer 3, for configuration of network components (routers, switches, etc.) and is able to configure and manage computer heterogeneous networks including virtualized network functions. She understands the design and implementation of communication protocols and is able to design distributed systems and toplogies with physical and virtual network network components.
By means of practical demonstrations and own acquired expertise she can review typical and approved technologies in data network communications domain including deployment of virtualized network functions.
 

Course Description

• Models for communication systems and other reference models
• Theoretical approaches to capacity planning and dimensioning based on statistical models and Markov chains
• Network algorithms for switching - Spanning Tree Protocol - and Routing - Open Shortest Path First
• Wide Area Network solutions, e.g. Multi Protocol Label Switching
• Virtualized Network Functions using CumulusVX and OPNSense as examples
• Network Management based on SNMP and deployment of Zabbix as network monitoring system
• Reference Architectures for company and data center networks
• Networking in Cloud Computing

Teaching and training methods

Lecture in seminar style, with blackboard writing and projection, solution of practical exercises in individual or team work.

Input from:

None

Assessment of the course:

Exam at the end of the course

Scientific Focus

passed exam and passed semester assignments
 

Input for:

None

5,00%

References

• Larry L. Peterson Bruce S. Davie: Computer Networks: a system approach, 2.ed., Morgan
  Kaufmann
• Douglas Comer / David L. Stevens: Internetworking with TCP/IP, Vol.1 und 2, Prentice Hall

Application Focus

Application of Machine Learning in Engineering, Medicine and Business Processes. Usage of Machine Learning models for structured and unstructured data. Miniprojects in collaboration with local companies.

Scientific Focus

Understanding of the function of classical and deep learning based machine learning algorithms. Knowledge about limitations and potential explainability of methods. Rigorous evaluation of machine learning models, avoiding common pitfalls like overfitting, information leakage and others.
 

Course Description
This course gives an introduction into machine learning. From basic methods (nearest neighbor, decision trees, ...) to modern deep learning approaches (Convolutional Neural Networks, Transformer architectures) everything will be introduced and applied in the lab practice. Structured and unstructured data (video, image, audio, text) will be considered with machine learning techniques. Machine Learning is not always the best solution (a hammer is not always the best tool), we discuss the limitations and ethical dimensions of potential solutions. A speciality of this course are mini-projects that are implemented by teams of participants in collaboration with local companies, who propose the topics. The mini-projects results will be presented in a workshop with company participants.

Course Structure

• - terminology of machine learning systems
• - Development of machine learning systems in KNime or other languages like python
• - design, implementation and evaluation of machine learning systems
• - linear models
• - supervised and unsupervised learning
• - neural networks
• - clustering, k-means
• - nearest-neighbor algorithms and lazy learning
• - decision trees
• - combination models, random forest, AdaBoost
• - Deep Learning (convolutional neural networks (CNN), long short-term memory (LSTM), Transformer (BERT))
• - Deep Learning Concepts - Transfer Learning, Data Augmentation, Generative Adversarial Networks (GAN)
• - Explainability of models
• - Applications for different modalities (text, image, sound), Word2Vec
• - theoretical concepts of machine learning (bias-variance dilemma, No Free Lunch Theorem)
• - methods to improve generalization abilities (regularization, feature selection, dimension reduction,
• complexity adjustment)
• - solution of real world tasks in form of miniprojects in collaboration with local companies
• Workshop with industrial partners presenting the results of miniprojects

Teaching and training methods

• video lecture accompanying project work with final presentation,
• Flip teaching (inverted classroom) is used.
• completion of programming tasks on the computer, individually or in teams,
• lab practice with KNime

Learning outcomes
The students know modern machine learning methods and can design, implement, apply and analyze them in the context of general information systems as well as in the biomedical domain. They can evaluate existing methods and can judge, if machine learning algorithms are a potential solution for a given problem. They know several successful real-world applications of machine learning methods. They know and can apply formal and theoretical analysis methods in computational intelligence and machine learning. They are able to discuss the ethical problems of a given machine learning system.

5,00%

References

• Witten, E. Frank, M. Hall und C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, 4. Edition, Morgan Kaufmann (2017) – electronic version via intranet access possible
• C. M. Bishop, Pattern Recognition and Machine Learning, Springer (2006)
• E. Alpaydin, Introduction to Machine Learning (Adaptive Computation and Machine Learning), Third Edition, MIT Press (2014)
• I. Goodfellow, Y. Bengio und A. Courville: Deep Learning, MIT Press (2016) – free version available https://www.deeplearningbook.org

Knowledge

• Graduates of the module master basic and advanced concepts of artificial intelligence and are able to apply current developments and methods of artificial intelligence to concrete practical issues in business informatics.
• The participants are able to confidently assess the benefits and limitations of the content and methods considered in relation to concrete practical applications of business informatics.
• The participants are confident in using current program libraries and are able to apply them to concrete problems in a project-oriented manner.

Skills

• The participants are able to independently deal with current developments in the field of artificial intelligence and its specializations and current applications in the field of business informatics and to comprehend the core statements.

Competence - attitude

• The participants are able to lead discussions on scientific issues (especially with regard to the applicability of the taught content for their field of study).
• The participants grasp the relevance of the taught contents for their field of study and are able to communicate this relevance adequately.
• The participants are able to discuss the challenges of the project tasks in project-oriented group work, identify possible alternative approaches and define, implement and evaluate justified approaches.

As part of this course, current trends in artificial intelligence with a relevance in the field of business informatics (such as the development of chatbots, the analysis of the sentiment of texts using sentiment analysis, the optimization of classic problems in logistics or reinforcement learning) are introduced in their mathematical basics and methods and implemented in a project-oriented manner on various tasks.
Graduates of the module are able to understand the topics dealt with in the course and apply them practically to various questions.

The course is taught in a project-oriented manner. In the first half of the semester, this involves teaching content in the form of interactive lectures and practicing the learned content in the form of small practical exercises. In the second half of the semester, the students work in groups to develop and implement specific practical applications, primarily in the field of business informatics.

Assessment of the course:
Project work (50% of the final grade)
Oral examination (50% of the final grade)

none

5,00%

Stuart Russell und Peter Norvig, Artificial Intelligence: A Modern Approach, Global Edition, Pearson 2021

Knowledge

• Knows state of the art in a certain scientific field
• Knows open research questions in this field
• Knows relevant literature
• Knows methodology and tools to execute project
• Knows how to document new findings according to scientific standards

Skills

• Can analyze problems and derive requirements
• Can define and plan an own research project
• Can apply appropriate research methodology
• Can implement a project and create project results
• Can describe state of the art, methodology and findings in a scientific report

Competence - attitude

• Can solve complex technical problems
• Can compare own findings with state of the art and do a critical discussion
• Can run an own scientific research project and create new findings
• Can deliver results on a quality level, e.g. for a company
• Masters uncertainty and unknown topics in new area
• Can present and defend results (in colloquium or at a conference)

The Research Project (Thesis) is a written scientific report on a project conducted by the student. The exact scope and content of the research project thesis is defined by the respective lecturer/examiner. The research project is intended to introduce students into scientific research work in a bigger context. Students may participate in one of the ongoing research projects of the university, may define a topic in cooperation with a company or in combination with an internship, or define an own topic together with the supervisor. The starting point is the definition of the research questions they want to answer and the selection of the appropriate methodology. The students will plan and execute their project independently with regular review and consulting. They will summarize their findings in a research project thesis (written scientific report). The research project can be a preparation for further work on the master thesis. The intention of the research project is to familiarize with the research methodology in a certain scientific field and to formulate the scientific state of the art and the research questions. The student proves the ability to execute own and independent research on master level and with a certain complexity. The focus is the quality of the project, its implementation and its results. In that sense, the research project thesis is the "smaller sister" of the Master thesis. The assessment and examination is based on the written scientific thesis report and the defense (presentation) in the colloquium. The examiners assess the thesis report and the colloquium, not the company project or internship. Nevertheless, the assessment takes the project quality and practical value (e.g. feedback of the company or project team) into account and this plays a bigger role, e.g. compared to the Master thesis where the scientific quality is the main assessment criteria.

Research project theses are done individually or as group work, with individual supervision and review:

• Project work, in a scientific project or within an internship in industry
• Writing of a scientific report
• Presentations to communicate and discuss the findings
• E-learning course on scientific work and scientific writing
• Individual review and feedback on results, papers and presentations

none

Assessment of the course: research project thesis about own research in an ongoing project as individual (or group) homework (90%) + presentation in colloquium (10%)

P - Master Thesis + Colloquium

15,00%

Specific scientific literature according to topic

General literature on scientific research:

Dresch, A., Pacheco Lacerda, D., & Valle Antunes Jr., J. A. (2015). Design Science Research: A Method for Science and Technology Advancement. Springer International Publishing Switzerland.

Bailey, S. (2018): Academic Writing – A Handbook for International Students (5th ed.). Routledge, New York

Bryman, A., Bell, E. (2022): Business research methods. 3rd + Edition, Oxford University Press

Mayring, P. (2014). Qualitative content analysis, Sage

Ritchie, J., & Lewis, J. (Eds.). (2014): Qualitative research practice: A guide for social science students and researchers (2nd ed.), London: Sage

Saunders, M., Lewis, P., Thornhill, A. (2023): Research Methods for Business Students (9th ed.). Upper Saddle River: Prentice Hall.

Learning outcomes

Knowledge

• Knows state of the art in a certain scientific field
• Knows open research questions in this field
• Knows relevant literature
• Knows methodology and tools to execute project
• Knows how to document new findings according to scientific standards

Skills

• Can define and plan an own research project
• Can apply appropriate research methodology
• Can create own research findings
• Can describe state of the art, methodology and findings in a scientific report

Competence - attitude

• Can compare own findings with state of the art and do a critical discussion
• Can run an own scientific research project and create new findings
• Masters uncertainty and unknown topics in new area
• Can present and defend results (in colloquium or at a conference)

Course Description

The research project is intended to introduce students into scientific research work in a bigger context. Students will participate in one of the ongoing research projects. They will contribute with an own sub project. The starting point is the definition of the research questions they want to answer and the selection of the appropriate methodology. The students will plan and execute their project independently with regular review and consulting. They will summarize their finding in a research project thesis (project report). The research project will be a preparation for further work on the master thesis. The intention of the research project is to familiarize with the research methodology in a certain scientific field and to formulate the scientific state of the art and the research questions. The student proves the ability to execute own and independent research on master level and with a certain complexity.  

Course Structure

Students will select a topic from one of the ongoing projects or an industry case in Digitalization, Software Engineering and Digital Systems. The will get individual consulting and feedback. During the semester the students will write a project thesis and present it in a colloquium at the end of the semester.

Excellent results are intended to be published and presented (oral or poster) at a conference (can be done in connection with the master thesis, too).

Application Focus

The Master thesis is done in connection with a research project. It is recommended to do the project and the thesis in connection with an internship/student job in industry or within a research project at a university or research institute, e.g. Institute for the Digital Transformation of Application and Living Domains (IDiAL).


Scientific Focus

The Master thesis is embedded into the scientific activities of the university, especially within the research institutes Institute for the Digital Transformation of Application and Living Domains (IDiAL).  

Teaching and training methods

Project Theses are done with individual supervision:

• Project Work, in a scientific project or within an internship in industry
• Writing of a scientific report
• Presentations to communicate and discuss the findings
• E-learning course on scientific work and scientific writing
• Individual review and feedback on papers and presentations

None - can be based on research project thesis

Assessment of the course: Master thesis about own research in an ongoing project as individual homework + presentation in colloquium (100%)

Only one module from semester 1 - 3 open

25.00  %

References

According to topic