Knowledge

• Knows relevant theoretical foundations of M2M and IoT
• Knows relevant gateway and processor architectures
• Knows relevant protocol stacks and communication systems
• Know 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:

• Understanding the role of innovation in the software development lifecycle, particularly in the context of digital transformation
• Mastering the stages and techniques of Design Thinking and in user-centered software design
• Gaining a deep understanding of Agile methodologies, including SCRUM and Kanban, and their relevance to iterative software development.
• Familiarity with essential tools for software development, including version control (Git), bug tracking systems, UML modeling, and Agile project management tools (e.g., Jira).
• Understanding DevOps principles, continuous integration/continuous delivery practices, and tools for automating software deployment and monitoring.
• Knowledge of emerging trends such as AI-driven development, cloud-native architectures, microservices, IoT, and blockchain technologies in the context of innovative software solutions.
• Understanding the key elements of successful pitches, including audience analysis, value propositions, and clear communication of technical solutions to non-technical stakeholders.

Skills:

• Apply Design Thinking techniques to create and prototype software solutions that effectively address user needs and challenges
• Setup and manage a team based on agile principles
• Utilize software tools to manage collaborative development in team environments
• Create and interpret UML diagrams (use case, sequence, class diagrams, etc.) and other modeling tools for designing and communicating software architecture.
• Prototype cloud-native solutions using containers, microservices, and serverless architectures
• Adapt and integrate new and emerging technologies into software projects
• Develop and deliver compelling pitches for software innovations, tailored to various audiences, including investors, stakeholders, and users.

Competence – attitude:

• Ability to think critically and apply innovative methodologies to design, develop, and refine software solutions that address complex, real-world problems
• Lead cross-functional teams in the development of software products that prioritize user needs
• Ability to work in interdisciplinary teams, integrating perspectives from design, business, and technology to drive software innovation and translate between different domains
• Apply Lean startup principles to turn software prototypes into viable products or business ventures, validating ideas, and scaling solutions in the market
• Competence in considering ethical, social, and environmental impacts of software solutions
• Practice communication strategies to clearly explain technical concepts, business value, and user benefits.

In parallel, Agile Software Development methodologies—such as SCRUM and Kanban—complement these innovation processes by emphasizing short, iterative development cycles, frequent user feedback, and the ability to quickly pivot in response to changing requirements. Agile’s adaptability, when combined with innovative thinking, fosters environments that are not only reactive but proactive, driving continual improvements in both product quality and user satisfaction.

To support this dynamic and iterative approach, a robust DevOps pipeline is introduced, focusing on integrating development and operations teams to ensure continuous integration, deployment, and monitoring. This infrastructure, aided by tools like Version Control Systems (e.g., Git), Bug Tracking and Ticket Management Systems (e.g., Jira), enables efficient team collaboration, rapid troubleshooting, and effective project management. Furthermore, modeling techniques such as Unified Modeling Language (UML) Diagrams, Entity-Relationship Diagrams (ERD), and Business Process Model and Notation (BPMN) are essential for visualizing software architecture, workflows, and system interactions. These tools provide clarity in the design and development phases, ensuring alignment between stakeholder vision and the technical implementation.

Students will also explore emerging trends in software engineering, such as AI-driven development, cloud-native architectures, and microservices, all of which play a critical role in digital transformation and large-scale innovation. Topics such as Test-Driven Development (TDD) and Continuous Delivery/Continuous Deployment (CI/CD) will further solidify their understanding of modern software engineering practices.

By the end of this course, students will gain not only technical expertise but also the innovation mindset required to lead digital transformation projects, creating software solutions that are both innovative and highly responsive to evolving user needs.

Course Structure

• Introduction to Innovation in Software Engineering
• Design Thinking and Prototyping
• Agile Methodologies in Software Development
• Tools and Techniques for Modern Software Development
• DevOps and Continuous Delivery
• Advanced Topics in Software Innovation

none

Assessment of the course: Theoretical knowledge (50%): Written or Oral Exam at the end of the course, Practical Skills and Scientific Competences (50%): Development of an innovative software prototype or concept for a given real-world challenge using design thinking and agile methodologies, supported by documentation and a presentation including pitching.

Passed exam and passed semester assignments

MOD2-01 – Usability Engineering

MOD-E03 – Human Centered Digitalization

5,00  %

Plattner, H., Meinel, C., & Weinberg, U. (2009). Design thinking (p. 64f). Landsberg am Lech: Mi-Fachverlag.

Uebernickel, F., Jiang, L., Brenner, W., Pukall, B., Naef, T., & Schindlholzer, B. (2020). Design thinking: The handbook. World Scientific.

Przybilla, L., Klinker, K., Lang, M., Schreieck, M., Wiesche, M., & Krcmar, H. (2020). Design thinking in digital innovation projects—Exploring the effects of intangibility. IEEE Transactions on Engineering Management, 69(4), 1635-1649.

Belling, S. (2020). Design Thinking with Agile: Shared concepts and applications. Succeeding with Agile Hybrids: Project Delivery Using Hybrid Methodologies, 109-117.

Corral, L., & Fronza, I. (2018, September). Design thinking and agile practices for software engineering: an opportunity for innovation. In Proceedings of the 19th Annual SIG Conference on Information Technology Education (pp. 26-31).

Tsui, F., Karam, O., & Bernal, B. (2022). Essentials of software engineering. Jones & Bartlett Learning.

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 (ca. 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 programme. 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 modelling 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 conducted also 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 FH Dortmund and do the respective exams. The Excel course puts the focus on using Excel for data analytics and business intelligence.
8. 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.
9. 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: Upon completion of this module, students will be able to:

• Analyze and differentiate between different architectures and central architectural patterns of web applications.
• Name and categorize important web standards and technologies.

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

• Derive and design a suitable architecture for solving a specific problem.
• Determine and combine suitable web standards and technologies for implementing this architecture.
• Use advanced web engineering tools, such as development environments, bundlers, scaffolding and transpilers.

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

• Analyze a complex requirement and break it down into sub-requirements.
• Implement an extensive task within the context of a project over several weeks.
• Develop and implement solutions cooperatively in a team.
• Present, explain and discuss their ideas and solutions.

In this module, students gain an overview of the architectures of complex web applications and analyze their differences and areas of application. They learn how corresponding web applications can be implemented by selecting and using suitable client and server-side technologies.

Course Structure

The module covers the following topics:

1.    Brief review of the basics of building websites with HTML, CSS and JavaScript (Bachelor material)
2.    Identification, analysis and differentiation of architectures of modern web applications:

•    Architectural patterns such as MVC and its variants (MVVM, MVP, etc.)
•    Request-based and component-based backend web frameworks
•    Single vs. multi page applications, server-side rendering, client-side rendering, hybrid approaches (e.g. rehydration, resumability)
•    Reactive programming/streaming

3.    In-depth study of server-side technologies for the development of web applications (e.g. with Java, JavaScript) 5.    Overview of current developments in web standards and research (e.g. Web Components, WebAssembly)

• Flipped/inverted classroom:

1. Online E-Learning materials with interactive slides and videos (asynchronous self-study)
2. Interactive classroom sessions (on-premise) for tasks and exercises based on examples from practice and research (e.g. coding, group exercises, lightning talks), for additional in-depth content, and for answering and discussing questions

• Lab project: Project task which is worked on in teams over the entire semester
• Guest lectures featuring experts and recent topics from research and industry

none

Assessment of the course: Theoretical knowledge (60%): Written or Oral Exam at the end of the course, Practical Skills and Scientific Competences (40%): implementation and presentation a software project (lab project)

MOD2-02 – Software-intensive Solutions

MOD-E01 – Software Engineering Project

5,00%

Simpson, Kyle (2015-2020): You Don’t Know JS (Yet), Volume 1-6, O’Reilly/Independently published

Ullenboom, Christian (2024): Spring Boot and Spring Framework 6, Rheinwerk Computing

Jacobson, Daniel; Brail, Greg; Woods, Dan (2011): APIs: A Strategy Guide: Creating Channels with Application Programming Interfaces, O'Reilly

Masse, Mark (2011): REST API Design Rulebook: Designing Consistent Restful Web Service Interfaces, O’Reilly

Porcello, Eve; Banks, Alex (2018): Learning GraphQL: Declarative Data Fetching for Modern Web Apps, O’Reilly

Bass, Len; Clements, Paul; Kazman, Rick (2021): Software Architecture in Practice, SEI Series in Software Engineering, Fourth Edition, Addison-Wesley Professional

Osmani, Addy (2023): Learning JavaScript Design Patterns: A JavaScript and React Developer's Guide, Second Edition, O’Reilly

Relevant standards:

• Ecma International (2024): ECMA-262: ECMAScript® 2024 language specification, 15th Edition, https://tc39.es/ecma262/
• WHATWG (2024): HTML Living Standard, https://html.spec.whatwg.org/
• WHATWG (2024): DOM Living Standard, https://dom.spec.whatwg.org
• WHATWG (2024): Fetch Living Standard, https://fetch.spec.whatwg.org
• GraphQL Foundation (2024): GraphQL Specification, http://spec.graphql.org

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 involves mainly 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 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.
7. 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.
8. 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 Nr. 1, 3 out of 6 technical courses (Nr. 2-7, graded), language certificate

Depending on choice of courses

5,00%

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

• Differentiate basic principles of software design.
• Differentiate, analyze, and apply key patterns at the macro- and micro-architecture level.
• Know relevant tools and methods for domain-driven design.
• Name and classify current research approaches to modeling software architectures.

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

• Apply basic principles of software design to concrete application scenarios.
• Select, combine and implement suitable methods for domain-driven design.

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

• Analyze a complex problem and break it down into subproblems.
• Implement an extensive task within the context of a project over several weeks.
• Develop and implement solutions cooperatively in a team.
• Select and apply appropriate methods for the interdisciplinary development of solutions, in particular together with domain experts without technical background.
• Present, explain and discuss their ideas and solutions.

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.

Course Structure

The module covers the following topics:
1.    Short repetition of the Bachelor material on software design (e.g. design patterns according to Gamma et al., Separation of Concerns, layered architecture)
2.    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)

3.    Characteristics and patterns of modern architectural styles (e.g. modular architectures, event-based architectures, microservice architectures)
4.    Model-driven design, development and reconstruction of software architectures
 

• Flipped/inverted classroom:

1.     Online E-Learning materials with interactive slides and videos (asynchronous self-study)
2.     Interactive classroom sessions (on-premise) for tasks and exercises based on examples from practice and research (e.g. coding, group exercises, lightning talks), for additional in-depth content, and for answering and discussing questions

• Lab project: Project task which is worked on in teams over the entire semester
• Guest lectures featuring experts and recent topics from research and industry

MOD1-02 Software Architectures

MOD1-03 Digital Systems 1

Assessment of the course: Theoretical knowledge (60%): Written or Oral Exam at the end of the course, Practical Skills and Scientific Competences (40%): implementation and presentation a software project (lab project)

Passed exam and passed lab project

MOD-E01 Software Engineering Project

5,00%

Vernon, Vernon (2016): Domain-Driven Design Distilled, Addison-Wesley

Evans, Eric (2003): Domain-Driven Design: Tackling Complexity in the Heart of Software, Addison-Wesley

Richardson, Chris (2018): Microservice Patterns: With examples in Java, Manning

Martin, Robert C. (2017): Clean Architecture: A Craftsman's Guide to Software Structure and Design, Pearson

Lilienthal, Carola (2019): Sustainable Software Architecture: Analyze and Reduce Technical Debt; dpunkt.verlag

Bass, Len; Clements, Paul; Kazman, Rick (2021): Software Architecture in Practice, SEI Series in Software Engineering, Fourth Edition, Addison-Wesley Professional

Gamma, Erich; Helm, Richard; Johnson, Ralph; Vlissides, John (1994): Design Patterns: Elements of Reusable Object-Oriented Software, Addison-Wesley

Combemale, Benoit; France, Robert; Jézéquel, Jean-Marc; Rumpe, Bernhard;  Steel, James; Vojtisek, Didier (2016): Engineering Modeling Languages. CRC Press

Rademacher, Florian (2022). A language ecosystem for modeling microservice architecture, Phd Thesis, https://dx.doi.org/doi:10.17170/kobra-202209306919

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 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 and understanding: The students

• can explain the design of digital ecosystems as a field of activity in industry
• can explain and contrast design patterns for business models of digital ecosystems.
• can explain and contrast design patterns for the system structure of digital ecosystems.

Skills: The students are able to

• identify and explain the the business model and the components for a given digital ecosystem.
• identify and critically comment on ethical and social issues of given digital ecosystems.
• plan and execute the design of a digital ecosystem as part of the construction process.
• explain and apply systemic thinking as interdisciplinary and cross-organizational thinking.

Communication and cooperation: The students have the ability to

• critically comment on and discuss a given plan for shaping a digital ecosystem.
• critically comment on and discuss a given design for a digital ecosystem with regard to its design quality.

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

• successfully contribute their own work results in an interdisciplinary exchange.
• include the social dimension in the introduction and further development of digital ecosystems as part of the design process
• recognize and explain ethical and social dimensions as part of the design work of digital ecosystems.

The module focuses on the challenges and special aspects of designing digital ecosystems. From a content perspective, the focus is particularly on business models of digital ecosystems and on the system structure of digital ecosystem solutions. Methods and techniques for designing digital ecosystems along the entire building process are also considered. The course focuses not only on design work, but also on evaluation as a core component of design.

Course Structure
1)    Process models for the design and evaluation of digital ecosystems (Future Search, Advanced Imagineering, Co-Creation)
2)    Techniques for the evaluation of digital ecosystems as part of the design work (simulations, simulation games, technology assessment)
3)    Opportunities and challenges in integrating design work into the construction process of digital ecosystems:

• Evolution of functionalities within the ecosystem
• Changes/extensions of the business model
• Expansion/reduction of the elements of an ecosystem

4)    Design patterns for digital ecosystems

• Solution level (patterns for business models)
• System level (patterns for system structures: open vs. closed, hierarchical vs. heterarchical ecosystems, agent systems as patterns)

5)    Ethical and societal issues of digital ecosystems, in particular

• Impact of digital ecosystems on existing sectors of the economy (example "click worker" and "supplier precariat")
• Sustainability issues relating to digital ecosystems (example: mail order)
• monopoly positions of powerful ecosystems (e.g. Amazon as a marketplace)

The diactic concept of this module is based on a mixture of problem-based learning, small group work and project orientation:

• theoretical principles and concepts are taught in interactive formats (e.g. Piazza technique) and deepened using given examples
• Methodological skills and practical application are taught and practiced using a self-chosen case study as a group project.
• Critical analysis and reflection on ecosystems is enhanced in the frame of project presentations and written assignments.

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 exam and passed semester assignments
 

none

5,00%

Brown, Tim (2019): Change by Design, Revised and Updated: How Design Thinking Transforms Organizations and Inspires Innovation, Harper Business.

Kelly, Kevin (2016): The Inevitable - Understanding the 12 Technological Forces That Will Shape Our Future. Viking.
Nijs, Diane (Eds) (2019): Advanced Imagineering – Designing Innovation as Collective Creation. Edward Elgar.

Osterwalder, Alexander; Pigneur, Yves (2010): Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers. Wiley.

Rossman, John (2019): Think Like Amazon: 50 ½ Ideas to Become a Digital Leader. McGraw Hill; 1. Edition

Skilton, Mark (2015): Building Digital Ecosystem Architectures: A Guide to Enterprise Architecting Digital Technologies in the Digital Enterprise. Palgrave Macmillan, 2015.

Weisbord, Marvin; Janoff; Sandra (2010): Future Search: An Action Guide to Finding Common Ground in Organizations and Communities. Berrett-Koehler.

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 ana- lyze 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 neighbour, 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

• analyse and develop digital transformation projects
• apply change management to organizations
• design people development and trainings 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 Organisations – 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 organisation with a focus on organisational change
• conduct a digital transformation project in an existing company or organisation 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 organisation), 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 and understanding: The students

• are able to relate the foundational principles and concepts of requirements engineering (RE) to each other
• can explain its role in the software development lifecycle and industrial software production
• know RE frameworks and modeling techniques (e.g., UML, BPMN), and their applications in real-world scenarios
• know relevant RE processes and interfaces to other processes
• know concepts and recent research on product line and variability management

Skills: The students are able to

• apply best practices in addressing conflicting stakeholder requirements, ambiguous needs, and dynamic project goals
• model requirements with RE tools
• set up and integrate modern RE tools into tool chains and design flows
• derive requirements in a structured and comprehensive way

Communication and cooperation: The students have the ability to

• effectively communicate with diverse stakeholders, including clients, developers, and end-users, to elicit and refine requirements
• work collaboratively in teams to deliver concepts and solutions, balancing multiple perspectives and interests
• facilitate workshops and collaborative activities to drive stakeholder alignment and consensus on requirements

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

• set up and lead RE in a cross-domain team
• develop a reflective understanding of their role as requirements engineers in contributing to successful systems
• recognize the ethical and professional responsibilities associated with translating stakeholder needs into solutions
• critically evaluate practices in RE to identify improvements and innovations in their work

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.

Course Structure
1)    Introduction to Requirements Engineering

•     Definition, relevance, and challenges
•     Role depending on system types and project characteristics

2)    Requirements Elicitation

•     Stakeholder identification
•     Interviews, focus groups, and ethnography
•     Brainstorming and collaborative workshops
•     Creativity and innovation

3)    Requirements Documentation

•     Requirements Specification (SRS) standards
•     Informal methods: prototypes, storyboards
•     Modeling requirements: UML, BPMN, user stories
•     Tools: JIRA, Confluence, ReqIF

4)    Validation and Verification

•     Quality attributes: completeness, consistency, correctness
•     Prototyping and user feedback
•     Requirements testing strategies

5)    Requirements Management

•     Prioritization techniques: MoSCoW, Kano, Weighted Scoring
•     Traceability matrices
•     Impact analysis for changes
•     Versioning and change management

6)    Advanced Topics

•     Software product lines, adaptive systems and crowd-based systems
•     Domain-specific languages
•     Generative AI and natural language processing in RE

The course is designed to provide hands-on experience in applying Requirements Engineering (RE) principles to real-world problems. Practical skills are developed through workshops, case studies, and collaborative project work that mimic the complexities of industry scenarios. In workshops, students engage in activities such as conducting stakeholder interviews, facilitating workshops, and creating prototypes. These sessions are designed to bridge the gap between theoretical knowledge and real-world application. The group project plays a central role in skill development, requiring students to apply a complete RE lifecycle to a given problem, from elicitation to validation and traceability. This immersive, hands-on experience ensures that students graduate with practical skills that are immediately applicable in professional settings.

To foster scientific rigor, the course includes activities that develop research, critical thinking, and analytical skills. Students are tasked with analyzing and critiquing existing requirements documents, exploring advanced techniques, and investigating domain-specific challenges. These activities emphasize a scientific approach to problem-solving, encouraging students to base their decisions on established RE principles and frameworks. The course also includes a research component, requiring students to explore emerging trends or challenges in Requirements Engineering, such as the impact of AI, agile environments, or autonomous systems. Students are expected to write a research paper or case study, integrating evidence from academic literature with their own analyses. By engaging with scientific articles, conducting systematic evaluations, and justifying their choices with data, students develop a research-oriented mindset that prepares them for both academic pursuits and informed professional decision-making.

R&D Project Management (MOD1-04)

none

5,00%

Basics & Practitioner
Pohl, K.; Requirements Engineering: Fundamentals, Principles, and Techniques, 2nd edition, Springer 2025.
Handbook on Natural Language Processing for Requirements Engineering . Ferarri, A.; Ginde, G.; (editors), Springer 2025

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

Ramachandran, M.; Zaigham, M.; Requirements Engineering for Service and Cloud Computing, Sprin- ger, 2017

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

Pohl, K., Rupp, C., Pohl, K.: Requirements engineering fundamentals: a study guide for the certified professional for requirements engineering exam; foundation level - IREB compliant. Rocky Nook, Santa Barbara, California, USA (2015).

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

Research (Conferences, Journals & selected articles)

• Working Conference on Requirements Engineering: Foundation for Software Quality (REFSQ)
• IEEE International Requirements Engineering Conference (RE)
• Requirements Engineering Journal, Springer
• International Workshop on Requirements Engineering and Testing, at ICSE International Conference on Software Engineering, IEEE Press
• IEEE Transactions on Software Engineering
• IEEE Systems Journal
• Groen, E.C., Seyff, N., Ali, R., Dalpiaz, F., Doerr, J., Guzman, E., Hosseini, M., Marco, J., Oriol, M., Perini, A., Stade, M.: The Crowd in Requirements Engineering: The Landscape and Challenges. IEEE Softw. 34, 44–52 (2017). https://doi.org/10.1109/MS.2017.33.

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: Upon successful completion of this module, students will acquire the ability to:

•  Design Complex Distributed Software Systems:

1. Develop sophisticated software systems tailored to specified requirements, leveraging widely recognized design frameworks such as UML (Unified Modeling Language), SoaML (Service-oriented Architecture Modeling Language), or SysML (Systems Modeling Language).
2. Demonstrate an understanding of the intricacies involved in creating scalable and maintainable system architectures.

•  Apply Advanced Architectural Styles:

1. Evaluate and apply appropriate architectural patterns, such as Microservices or Moduliths, to develop robust software solutions.
2. Tailor the architectural approach to address the specific needs and constraints of a given use case or application domain.

•  Develop Deployment Strategies for Cloud-Based Environments:

1. Create and implement scalable deployment strategies for distributed software systems, ensuring high availability and fault tolerance.
2. Utilize cloud platforms and container orchestration tools, such as Kubernetes, AWS, or Microsoft Azure, to deploy and manage applications efficiently in diverse operating environments.

•  Design and Implement Comprehensive Testing Strategies:

1. Create and implement scalable deployment strategies for distributed software systems, ensuring high availability and fault tolerance.
2. Utilize cloud platforms and container orchestration tools, such as Kubernetes, AWS, or Microsoft Azure, to deploy and manage applications efficiently in diverse operating environments.

The primary aim of this course is to provide students with both a solid theoretical foundation and practical experience in software engineering for Microservice Architecture. Throughout the course, students work collaboratively in teams on use cases from real work examples or research project. This practical engagement bridges the gap between academic concepts and professional application.

The course places significant emphasis on the principles of software architecture and engineering, which form the foundation for designing and implementing robust and efficient software systems. Students explore key concepts, best practices, and design patterns in software development to equip them with the skills necessary for creating scalable and maintainable software system.

To ensure adaptability and dynamic project execution, the course integrates Agile methodologies. Students adopt frameworks such as Scrum to manage their projects, fostering teamwork and promoting iterative development. By applying these methodologie, students experience the flexibility and collaborative advantages of agile workflows, which are widely used in the software industry.

The course also requires students to undertake the complete software development lifecycle, beginning with requirements engineering to capture and analyze user needs. Students then proceed through system design, coding, testing, deployment, and maintenance, gaining a holistic understanding of the entire process. This comprehensive approach ensures that students are prepared to tackle all phases of software development, from initial concept to final deployment.

By the end of the course, students will have developed the skills to design, build, and manage software systems in a team-oriented, real-world setting. They will have a deep understanding of software engineering principles, practical experience with Agile methodologies, and familiarity with industry-standard tools and processes. This course ultimately aims to prepare students to meet the demands of the modern software industry and contribute effectively to complex development projects.

Course Structure

• Introduction Microservice Architecture
• Introduction use case for the software system to develop
• Agile Methodologies in Software Development
• Requirements engineering
• Designing of the software system
• Implementation of the software system
• Deployment of the software system
• Testing of the software system  

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

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 Theoretical knowledge (50%): Written or Oral Exam at the end of the course

none

5,00%

Newman, Sam. Building microservices. " O'Reilly Media, Inc.", 2021.

Richardson, Chris. Microservices patterns: with examples in Java. Simon and Schuster, 2018.

Richards, Mark. Microservices vs. service-oriented architecture. Sebastopol: O'Reilly Media, 2015.

Pautasso, Cesare, et al. "Microservices in practice, part 1: Reality check and service design." IEEE software 34.01 (2017): 91-98.

Pautasso, Cesare, et al. "Microservices in practice, part 2: Service integration and sustainability." IEEE Software 34.02 (2017): 97-104.

Dragoni, Nicola, et al. "Microservices: yesterday, today, and tomorrow." Present and ulterior software engineering (2017): 195-216.

Alshuqayran, Nuha, Nour Ali, and Roger Evans. "A systematic mapping study in microservice architecture." 2016 IEEE 9th international conference on service-oriented computing and applications (SOCA). IEEE, 2016.

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 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 virtualised network functions. She understands the design and implementation of commmunication 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 virtualised 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
• Virtualised Network Functions using CumulusVX and OPNSense as examples
• Network Management based on SNMP und deployment of Zabbix as network monitoring system
• Reference Architectures for company and data centre 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 neighbour, 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-neighbour 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 (regularisation, 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 tea- ching 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 defence (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 Digitalisation, 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. IDiAL.

Scientific Focus

The Master thesis is embedded into the scientific activities of the university, especially within the research institutes IDiAL and IKT.
 

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