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Module Handbook Master Digital Transformation

MOD1-01 – Innovation Driven Software Engineering

Number	MOD1-01
Title	Innovation Driven Software Engineering
Language	Englisch
Type of participation	Pflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: compulsory Course frequency: every year – winter semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	1
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	Input from: None
Learning outcomes	Learning outcomes 7.1 Knowledge Knows the theoretical background of the design thinking method Knows different software development processes especially agile software development Knows required steps and processes for agile software development Knows how to express software architectures based on the UML Diagrams Knows how to use tools like git, checkstyle, bug tracking and issue management systems 7.2 Skills Can conceptualize a software based on the design thinking method Can apply and choose between software development processes Can setup and manage a team based on agile principles Can work on a software development project 7.3 Competence – attitude Can work in a team on scientific topics Can present and defend scientific results in front of an audience Can discuss the topics related to the lecture Can understand related topics and translate between different domains
Course description and course structure	Course Description Innovation driven software engineering touches every aspect of modern software development. Today's software emphasizes novelty, usability, and joy of use. Modern software is usually created in creative and highly iterative processes. Many steps in these processes involve potential users. This integration of the user can be addressed with the so-called Design Thinking method. Refined ideas and prototypes can be the foundation for new startup companies. One way to check the viability is the Business Model Canvas. Agile Software Development puts the focus back on user feedback and iterations. The agile development process is accompanied with an extensive tool chain for designing and creating software solutions. For instance, UML Diagrams, Version control systems, Bug tracker and ticket management systems. Course Structure Design Thinking Business Model Canvas Legacy process models Agile Software Development Agile Manifesto best practices UML Modelling Tooling like git, Bug tracker and ticket management systems, Checkstyle, etc. Application Focus Practical exercises Realizing a real-world project within a block-week in a team Skills trained in this course: theoretical knowledge, practical skills and scientific competencies
Teaching and training methods	Teaching and training methods Theoretical knowledge: e-learning modules on innovation driven software engineering Practical Skills: Project work, Labs, and Exercises Scientific Competences: extract information for a given topic in a small group and sum the results up
Assessment of course	Assessment of the course: Theoretical knowledge (40%): Written Exam at the end of the course, Practical Skills (40%): realizing a small real-world project within the lecture related topics of innovation driven software engineering and Scientific Competences (20%): written paper (literature review, approx. 10 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund)
Requirements for award of credits	Scientific Focus Written assignment: literature review in the style of a scientific paper up to 10 pages Performing a survey based on relevant scientific methods
Module mapping	Input for: MOD2-01 – Usability Engineering MOD-E03 – Human Centered Digitalization
References	References Solving Problems with Design Thinking - Ten Stories of What Works, Jeanne Liedtka, Andrew King, Kevin Bennett, Columbia Business School Publishing, 2013, ISBN: 0231163568 Business Model Generation: A Handbook for Visionaries, Game Changers, and Challengers, Alexander Osterwalder, Yves Pigneur, John Wiley & Sons, 2010, ISBN: 9780470876411  Software Engineering, Ian Sommerville, Addison Wesley Pub Co Inc, 2015, ISBN: 0133943038

MOD1-02 – Software Architectures

Number	MOD1-02
Title	Software Architectures
Language	Englisch
Type of participation	Pflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: compulsory Course frequency: every year – winter semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	1
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	Input from: None
Learning outcomes	Learning outcomes 7.1 Knowledge Knows the concepts and structure of modern software architecture styles Knows technologies and tools related to the operation of modern software architectures Knows particular challenges of operating distributed systems Knows how to analyze an application by different metrics Knows to maintain and operate a distributed system Knows how to distributed a system based on workload of particular components 7.2 Skills Can critically evaluate the suitability of an architectural style given a particular problem Can design, develop and operate leveraging the lecture topics Can assess and improve an existing software architecture Can analyze a distributed system by different application metrics Can distribute a container-based system by workload 7.3 Competence – attitude Can discuss and assess the differences between various architectural styles Can communicate and explain architectural decisions Can work in a team on scientific topics Can demonstrate and discuss results in a group
Course description and course structure	Course Description In recent years’ new architectural styles have emerged to cope with the increasing need of highly scalable and distributed systems. Among them are Microservices and Self-Contained Systems. The resulting systems are characterized by being componentized into independent services which communicate using well-defined interfaces. This course the students learn about modern software architecture paradigms, both conceptually and practically. Additionally, subjects related to the operation of such systems are covered, such as infrastructure-technologies and particular challenges of operation like scaling or load balancing. In addition to the lectures, the students have the opportunity to apply their knowledge in project-based group activities. Course Structure Historical development of software architecture paradigms. Characteristics of modern architectural styles. Designing Microservices and Self-Contained Services. Developing Microservices and Self-Contained Services. Infrastructure, deployment, and operation: Methods, technologies and challenges. Application Focus To complement the lectures and enable the students to apply the covered topics, there will be a project. Their students will work in small groups to participate in the design and development of a significant application. Skills trained in this course: theoretical knowledge, practical skills and scientific competencies
Teaching and training methods	Teaching and training methods Theoretical knowledge: e-learning modules on software architecture models, tool tutorials Practical Skills: Projects, Labs & Exercises, small project Scientific Competences: student research group on SW Architectures
Assessment of course	Assessment of the course: Theoretical knowledge (40%): Theoretical knowledge (40%): Written Exam at the end of the course, Practical Skills (40%): Individual programming task, realizing a small real-world project within the lecture related topics of software architectures and Scientific Competences (20%): written paper (literature review, approx. 10 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund)
Requirements for award of credits	Scientific Focus Written assignment: literature review in the style of a scientific paper up to 10 pages Performing a survey based on relevant scientific methods
Module mapping	Input for: MOD2-02 – Software-intensive Solutions MOD-E01 – Software Engineering Project
References	References Newman, S.; Building Microservices, O’Reilly Media, 2016 Wolff, E.; Microservices: Flexible Software Architecture, Addison-Wesley, 2016

MOD1-03 – Digital Systems 1

Number	MOD1-03
Title	Digital Systems 1
Language	Englisch
Type of participation	Pflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: compulsory Course frequency: every year – winter semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	1
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	Input from: none
Learning outcomes	Learning outcomes 7.1 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 7.2 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 7.3 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
Course description and course structure	Course Description 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 Introduction to M2M and IoT devices and gateways Processor architecture for embedded devices and gateways IP based communication IoT and M2M protocols Communication gateway architectures Wireline communication networks and standards Wireless communication networks and standards Case study of a state-of-the-art application, e.g. connected car or industry 4.0 Application Focus Project IoT System: students will set up and implement a IoT system with an M2M device, a gateway with wireless and wireline transmission and a IoT cloud connection. 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 the Siemens Embedded Software Developer tool chain Skills trained in this course: theoretical knowledge, practical skills and scientific competencies
Teaching and training methods	Teaching and training methods Theoretical knowledge: e-learning modules on IoT devices and protocols, tool tutorials Practical Skills: Projects, Labs & Exercises, small project with an IoT device and protocol stack Scientific Competences: own research on IoT in e-mobility
Assessment of course	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 protocol stack system => demonstration of the result
Requirements for award of credits	Scientific Focus Students will do a scientific evaluation of the potential of IoT usage in a specific domain (e.g. eMobility charging systems) based on recent scientific literature.
Module mapping	Input for: MOD2-03 – Digital Systems 2
References	References t.b.d.

MOD1-04 – R&D Project Management

Number	MOD1-04
Title	R&D Project Management
Language	Englisch
Type of participation	Pflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: compulsory Course frequency: every year – winter semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	1
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	Input from: none
Learning outcomes	Learning outcomes 7.1 Knowledge Students know the basic body of knowledge for project management Students know processes, methods and tools for risk management for R&D projects (e.g. FMEA, @risk) Students know processes, methods and tools for configuration management (esp. from SW engineering) Students know processes, methods and tools for change and claim management Students know processes, methods and tools for quality management according to ISO9001 and TS16949 Students understand the importance of Reviews in R&D projects Students understand the main challenges of large R&D projects 7.2 Skills Students can tailor processes and methods to the respective projects Students can apply the respective project management methodology Students can assess R&D projects and can extract relevant characteristics Students can develop new methods according to gaps in the existing methodology Students can do the complete planning and preparation of a real project case Students can develop relevant KPIs and scorecards for measuring effectiveness and efficiency 7.3 Competence – attitude Students develop an attitude to project management according to engineering standards Students show a quality attitude according to engineering standards Students manage projects based on structured and well defined processes and in depth analysis Students can achieve high effectiveness and efficiency in running complex and innovative R&D projects Students understand the differences between small and large projects and act accordingly
Course description and course structure	Course Description The course R&D project management is focusing on processes, methods and tools for the management of innovative research and development projects in engineering. R&D projects are characterized by creativity and a high degree of innovation and uncertainty. Advanced project management methodology has to deal with the uncertainty and has to foster creativity. Apart from this general problem, R&D project methodology has to be aligned with the engineering processes and with the different engineering domains. Topics like quality management, configuration management and specific tools for risk management are part of the methodology, too. The course enables students to understand and structure R&D projects and to choose appropriate tools and methods based on a proper analysis of the project characteristics. The students are able to tailor the methodology and they understand the remaining gaps in the methodology. They can develop new project management methods and tools to fill the gaps and they can do research to assess the effectiveness and efficiency of project management methodology in R&D. The course is based on one main project case study and several small cases for specific topics. Course Structure Characteristics of R&D projects Project management processes: planning, controlling (cost, time, quality) agile & lean V-model Milestones and Reviews Risk Management for R&D Projects Configuration & Release Management Change and Claim Management (incl. Patents) Quality Management (incl. CMMI) KPIs and Scorecards Large R&D projects and Cross Domain Projects Management of R&D organizations Application Focus Block workshop: students attend an interdisciplinary one-week workshop where they prepare and plan a project for an industry case (together with EuroMPM and Master ESM) Skills trained in this course: theoretical knowledge, practical skills and scientific competencies
Teaching and training methods	Teaching and training methods Theoretical knowledge: lectures on project management processes, methods and tools Practical Skills: interdisciplinary block week Scientific Competences: individual research paper + presentation
Assessment of course	Assessment of the course: Theoretical knowledge: Oral exam at the end of the course (40%), Practical Skills: Group assessment on results of block week (40%) and Scientific Competences: paper presentation (20%)
Requirements for award of credits	Scientific Focus Students prepare a homework and a presentation on an individually selected topic from recent project management research.
Module mapping	Input for: Usability Engineering (MOD2-01) Requirements Engineering (MOD-E02) Managing Digital Change (MOD-E08)
References	References PMBOK® - 4th edition, PMI® 2008. Kerzner, Harold: Project Management: A Systems Approach to Planning, Scheduling, and Controlling, 10th edition, New York 2009 ICB - IPMA Competence Baseline, Version 3, PMA/GPM-Eigenverlag 1999 INCOSE Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, 4th Edition, INCOSE, 2015, ISBN: 978-1-118-99940-0

MOD1-05 – Scientific & Transversal Skills 1

Number	MOD1-05
Title	Scientific & Transversal Skills 1
Language	Englisch
Type of participation	Pflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: compulsory Course frequency: every year – winter semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	1
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	Input from: none
Learning outcomes	Learning outcomes 7.1 Knowledge Knows the foundations of each topic at least up to bachelor knowledge 7.2 Skills Can apply the knowledge in the upcoming master courses 7.3 Competence – attitude Can assess the gaps in own knowledge Can use a variety of tools, online-courses, tutorials to close the gap in own responsibility
Course description and course structure	Course Description The course is intended to form a tailored program for new students according to their prior knowledge, educational and professional background. It is intended to offer a wrap up and to close gaps according to the previous bachelor degree, to converge the students from different backgrounds into one international classroom and to prepare them for the educational concepts of the Master’s programme. Students select compact courses worth 6 ECTS in total on topics relevant for the further study programme. These compact courses will enable students with different backgrounds to get a smooth start into the other master modules. The selection of courses is need based and will be assessed by interviewing the students, by doing tests (where necessary) and by consulting. 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: Compact Electronics Course (Microcontroller-Lab) Compact Programming Course (Java, C#) Modeling of Embedded Systems (UML) Tools and Techniques for Digital Systems Design (Eclipse) Mini Projects (Group Projects, with other Master’s programmes) Research Methods and Tools – part A (RMT-A): (Introduction to Scientific Domain, Literature Review, Conceptual Frameworks) International Project Communication (English) Engineering Communication 1 (German A1, or other foreign language for Germans) Application Focus Depending on choice of courses Skills trained in this course:
Teaching and training methods	Teaching and training methods Compact courses will follow a similar structure: 1 day introduction in the first two weeks of the first semester, mini-tests to assess the prior knowledge and to define the gap. Definition of the training need and selection of trainings an exercises 0,5 day assessment and tutorial in the week before Christmas (block week) Tutorials on each Friday throughout the whole semester Access to e-learning modules and MOOCs for each topic Literature recommendations and provision of exercises Submission of homework for assessment and advice by tutors Language and communication trainings will be done on a weekly basis, e.g. Monday afternoon + evening (in connection with external trainers, e.g. from Auslandsgesellschaft), external lecturer for “Research Methods and Tools – part A (RMT-A)”
Assessment of course	Assessment of the course: Depending on choice of courses
Requirements for award of credits	Scientific Focus Depending on choice of courses
Module mapping	Input for: Depending on choice of courses
References	References Depending on the chosen set of compact courses

MOD2-02 – Software-intensive Solutions

Number	MOD2-02
Title	Software-intensive Solutions
Language	Englisch
Type of participation	Pflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: compulsory Course frequency: every year – summer semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Sommersemester
Course admittance prerequisites	Input from: MOD1-02 Software Architectures MOD1-03 Digital Systems 1
Learning outcomes	Learning outcomes 7.1 Knowledge Know the difference between IoT and IoE Know the different architectures of Internet of Things Know several related technology stacks Know the most relevant communication protocols and APIs Know the requirements of various types of databases Know the difference between actuating elements, sensors, and devices Know the purpose of artificial intelligence 7.2 Skills Can identify and define the requirements for an Internet of Everything application Can apply different tools for designing an IoE application Can assess existing solutions in the area of IoE and cloud-services 7.3 Competence – attitude Can discuss Internet of Everything in the scientific context Can present and defend results Can understand and translates IoE related stuff between different domains
Course description and course structure	Course Description This course has the aim to walk through the technology stack of an Internet of Everything (IoE) solution. Students will get a holistic view on the processes, components, methods and tools and their connections and dependencies. Relevant architectures and concepts are put into the context of complete IoE solutions. This holistic view starts with the level of the devices that are connected to the internet like mobile devices or sensors and actuators. Realizing such systems commonly requires the communication with sensors and actuators on the hardware-side and communication with cloud services on the software-side. The corresponding cloud service has to process and store data like sensor values and analyze these with artificial intelligence or machine learning, which must be taken into account while developing such systems. The course intends to put the topics addressed by the first semester modules into the overall context. This forms (as a connecting element) the bridge to the more specific elective modules. Course Structure Architectures of Internet of Everything solutions APIs for Sensors and Actuators Communication protocol stacks and their intergration Database integration for IoE (Time-Series etc.) Application of Data Science in IoE solutions (Big-Data, Smart Data, etc.) Application of Artificial Intelligence and Deep-Learning in IoE solutions Integration of cloud-based services Application Focus Within a block-week real-world project together with companies are realized. Students test and test these projects within the User Innovation Center. Skills trained in this course: theoretical knowledge, practical skills and scientific competencies
Teaching and training methods	Teaching and training methods Theoretical knowledge: e-learning modules on software-intensive systems, tool tutorials Practical Skills: Project work within the User Innovation Center, Labs, and Exercises Scientific Competences: extract information of published papers about the relevant topics for the provided course-content
Assessment of course	Assessment of the course: Theoretical knowledge (40%): Written Exam at the end of the course, Practical Skills (40%): realizing a small real-world project within the lecture related topics of software intensive solutions and Scientific Competences (20%):
Requirements for award of credits	Scientific Focus Written assignment: literature review in the style of a scientific paper up to 10 pages
Module mapping	Input for: MOD-E01 Software Engineering Project
References	References Prof. Dr.-Ing. habil. Hartmut Janocha, Adaptronics and Smart Structures, Springer 2007 Taewan You, Toward the future of Internet architecture for IoE, ICTC 2016 Emil Vassev, Mike Hinchey, Awareness in Software-Intensive Systems, IEEE Computer Society 2012 Marcelo Benites Gonçalves, Everton Cavalcante, Towards a Conceptual Model for Software-Intensive System-of-Systems, ieee international conference on systems, man and cybernetics 2014 Grayson Honan, Tolga Soyata, Internet-of-Everything Oriented Implementation of Secure Digital Health (D-Health) Systems, ISCC 2016

MOD2-01 – Usability Engineering

Number	MOD2-01
Title	Usability Engineering
Language	Englisch
Type of participation	Pflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: compulsory Course frequency: every year – summer semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Sommersemester
Course admittance prerequisites	Input from: Innovation Driven Software Engineering (MOD1-01) R&D Project Management (MOD1-04) Scientific & Transversal Skills 1 (MOD1-05)
Learning outcomes	Learning outcomes 7.1 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.) 7.2 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 7.3 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
Course description and course structure	Course Description 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 students will 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 Introduction Motivation Definition Usability Engineering Processes Usability Engineering -Processes Integration into IT-projects Potential conflicts Communicating Usability Usability Engineering Tools and Methods Analyzing context of use Requirements management Concepts Evaluation 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. Mobile Computing Individual software solutions Consumer- vs. Business-Software 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 Skills trained in this course: theoretical knowledge, practical skills and scientific competencies
Teaching and training methods	Teaching and training methods Theoretical knowledge: e-learning modules and (live-)video lectures on usability engineering Practical Skills: Projects, Labs & Exercises, block week with selected tools and methods Scientific Competences: student research group on usability engineering
Assessment of course	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, e.g. International Research Conference Dortmund)
Requirements for award of credits	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.
Module mapping	Input for: Research Project Thesis (MOD3-03)
References	References 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: Howto Plan, Design, and Conduct Effective Tests, Wiley, 2008

MOD2-03 – Digital Systems 2

Number	MOD2-03
Title	Digital Systems 2
Language	Englisch
Type of participation	Pflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: compulsory Course frequency: every year – summer semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Sommersemester
Course admittance prerequisites	Input from: MOD1-03 – Digital Systems 1
Learning outcomes	Learning outcomes 7.1 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 7.2 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 7.3 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 and course structure	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 security 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 appropriate 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 Introduction to internet security for CPS Architectures for trusted platforms Secure communication Intrusion detection and advanced methods in CPS Authentication, data protection and privacy and IoT systems Introduction to the Operator-Controller-Module Real-time processing Operating systems (OS) and databases for embedded systems 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. Skills trained in this course: theoretical knowledge, practical skills and scientific competences
Teaching and training methods	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
Assessment of course	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
Requirements for award of credits	Scientific Focus Students will do a scientific evaluation of the security issues in a specific domain (e.g. eMobility charging systems) based on recent scientific literature.
Module mapping	Input for: MOD-E09 - Smart Home & Smart Building & Smart City MOD-E10 - Edge Computing
References	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 Mechatronic 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 Structured Form of Mechatronic Systems. 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2001), Villa Olmo; Como, Italy, 2001

MOD2-04 – Scientific & Transversal Skills 2

Number	MOD2-04
Title	Scientific & Transversal Skills 2
Language	Englisch
Type of participation	Pflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: compulsory Course frequency: every year – summer semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Sommersemester
Course admittance prerequisites	Input from: MOD1-05 – Scientific & Transversal Skills 1
Learning outcomes	Learning outcomes 7.1 Knowledge Knows the foundations of each topic at least up to bachelor knowledge 7.2 Skills Can apply the knowledge in the upcoming master courses 7.3 Competence – attitude Can assess the gaps in own knowledge Can use a variety of tools, online-courses, tutorials to close the gap in own responsibility
Course description and course structure	Course Description The course is intended for adding specific scientific and transversal skills to the curriculum. It is intended to prepare students for both a scientific path in the third and fourth semester or for a more application oriented path with internships and theses in industry. Students select compact courses worth 6 ECTS in total on topics relevant for the further study programme. The selection of courses is need based and will be assessed by interviewing the students, by doing tests (where necessary) and by consulting. 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: Compact Course on Business Models and Business Cases IT Tools for Projects (Redmine, Jira, Confluence) Mini Projects (Group Projects, with other Master’s programmes) Research Methods and Tools – part B (RMT-B) (Research Design, Tools, Databases, Publishing) International Negotiation & Conflict Management (English) Engineering Communication 1 (German A2, or other foreign language for Germans) Application Focus Depending on choice of courses Skills trained in this course:
Teaching and training methods	Teaching and training methods Compact courses will follow a similar structure: 1 day introduction at the end of the first semester, mini-tests to assess the prior knowledge and to define the gap. Definition of the training need and selection of trainings an exercises 0,5 day assessment and tutorial in the week before summer school Tutorials on each Friday throughout the whole semester Access to e-learning modules and MOOCs for each topic Literature recommendations and provision of exercises Submission of homework for assessment and advice by tutors Language and communication trainings will be done on a weekly basis, e.g. Monday afternoon + evening (in connection with external trainers, e.g. from Auslandsgesellschaft)
Assessment of course	Assessment of the course: Depending on choice of courses
Requirements for award of credits	Scientific Focus Depending on choice of courses
Module mapping	Input for: Depending on choice of courses
References	References Depending on choice of courses

MOD3-03 – Research Project (Thesis)

Number	MOD3-03
Title	Research Project (Thesis)
Language	Englisch
Type of participation	Pflichtfach
Credits	18
Workload (self study and contact hours)	Parameters ECTS: 18 Hours of study in total: 540 Weekly hours per semester: Contact hours: 0 Self-Study hours: 540 Course characteristics: compulsory Course frequency: every year – winter semester Maximal capacity: individual participation Course admittance prerequisites: (0 + 540)
Semester	3
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	Input from: none
Learning outcomes	Learning outcomes 7.1 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 7.2 Skills Can define and plan an own research project Can apply appropriate research methodology Can create own research findings Can describe project execution, methodology and findings in a scientific report 7.3 Competence – attitude Can run an own more complex scientific research project Masters uncertainty and unknown topics in new area Can present and defend results (in colloquium or at a conference)
Course description and course structure	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 Research Project (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. Skills trained in this course:
Teaching and training methods	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
Assessment of course	Assessment of the course: Project thesis about own research in an ongoing project as individual homework + presentation in colloquium (100%)
Requirements for award of credits	Scientific Focus The Research Project (Thesis) is embedded into the scientific activities of the university, especially within the research institutes IDiAL and IKT.
Module mapping	Input for: P – Master Thesis + Colloquium
References	References According to topic

MOD-E01 – Software Engineering Project

Number	MOD-E01
Title	Software Engineering Project
Language	Englisch
Type of participation	Wahlpflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: every year - winter semester Maximal capacity: 25 students Course admittance prerequisites: MOD1-01 Innovation Driven Software Engineering, MOD1-02 Software Architectures, MOD1-04 R&D Project Management (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	Input from: MOD1-01 Innovation Driven Software Engineering MOD1-02 Software Architectures MOD1-04 R&D Project Management MOD2-02 Software-intensive Solutions
Learning outcomes	Learning outcomes 7.1 Knowledge Students know modeling tools for software design Students know concepts and processes for agile software development Students know how to create test suites for automated software testing Students know how to use typically used tools in the software engineering process 7.2 Skills Students can apply processes and methods to specific project needs Students can evaluate and use tools for developing software systems in a team Students can use tools to support the development process in a team Students can use tools to improve software quality 7.3 Competence – attitude Can discuss and defend results in topics related to the lecture content Can work in a team on scientific topics Can understand lecture related content and translates between different domains
Course description and course structure	Course Description The aim of this course is to provide students with theoretical and practical experience in software engineering. Therefore, the students work in teams on real world tasks in cooperation with industry partners. The course focuses on software architecture and software engineering principles that are the foundation for implementing software systems. During the course, the students need to apply agile methods to their project and team for a dynamic software engineering approach. Evaluating software tools for their project is another task within this course, e.g. continuous delivery, IDEs. In summary, the students implement the complete life cycle from requirements engineering to design over the development of a software system. Course Structure The course is training software engineering skills by applying the following competences (from previous modules) within a realistic project (e.g. industry case): Object oriented modeling and design Architecture Design (Patterns, Frameworks, Libraries) Software Testing Tools Version control systems (Git, SVN, Mercurial SCM) Code management Ticket systems and bug tracker (Continuous) integration and release management Documentation Processes Classical software development Agile software development (Scrum) Requirements Engineering Project management, project planning, quality management Application Focus Within a block-week, the students realize a whole software project starting from requirements engineering to design and development. The whole process is accompanied by an industry partner and takes place in the User Innovation Center. Skills trained in this course: theoretical knowledge, practical skills and scientific competences
Teaching and training methods	Teaching and training methods Theoretical knowledge: lectures on requirements engineering Practical Skills: requirements analysis of a project with DOORS, group work to train concepts and methods, to develop skills and to work on case studies Scientific Competences: research paper on literature review about RE topic
Assessment of course	Assessment of the course: Practical Skills (50%): realizing a real-world project within the User Innovation Center during a block week and Scientific Competences (50%): written paper (literature review, reflection of project with current research, approx. 25 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund)
Requirements for award of credits	Scientific Focus Scientific documentation of the content used in the project as well as the related and applied principles Consideration of scientific sources/papers from the Requirements Engineering and Software Engineering The documentation of a scientific nature is also an assessment basis for the overall grade of this course
Module mapping	Input for: none
References	References https://www.pearson-studium.de/software-engineering-global-edition.html (Ian Sommerville, ISBN 978-1-2920-9614-8) http://eu.wiley.com/WileyCDA/WileyTitle/productCd-EHEP000908.html (Hans van Vliet) Domain Driven Design (Eric Evan) Design Pattern (Gang of Four; IBSN 978-0201633610) (https://www2.swc.rwth-aachen.de/se_buch/ (deutsch; Horst Lichter, Jochen Ludewig)) http://www.hanser-fachbuch.de/buch/UML+2+glasklar/9783446430570 (deutsch; ISBN 978-3-446-43057-0)

MOD-E02 – Smart Home & Smart Building & Smart City

Number	MOD-E02
Title	Smart Home & Smart Building & Smart City
Language	Englisch
Type of participation	Wahlpflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: every year - summer semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Sommersemester
Course admittance prerequisites	Input from: MOD1-02 Software Architectures MOD1-03 Digital Systems 1 MOD2-02 Software-intensive Solutions MOD2-03 Digital Systems 2
Learning outcomes	Learning outcomes 7.1 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 7.2 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 7.3 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
Course description and course structure	Course Description 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 reaching 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 Stakeholder and Citizen Involvement 3.4 Case Study: Smart City Alliance Dortmund Application Focus Project Smart Systems: students will set up and implement an example or a part of a Smart System (Home, Building, City). The respective case study will be taken from a recent R&D project or an industry case. The result will be a demonstrator system. Skills trained in this course: theoretical, practical and scientific skills and competences
Teaching and training methods	Teaching and training methods Theoretical knowledge: e-learning modules on Smart Systems, tool tutorials Practical Skills: Projects, Labs & Exercises, small project with Smart Systems Scientific Competences: own research on Smart Systems
Assessment of course	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
Requirements for award of credits	Scientific Focus Students will do a scientific evaluation of the potential of Smart Systems usage in a specific domain (e.g. transportation) based on recent scientific literature. It is intended to take issues from the Smart City Alliance Dortmund or from ruhrvalley.
Module mapping	Input for: None
References	References to be defined

MOD-E03 – Human Centered Digitalization

Number	MOD-E03
Title	Human Centered Digitalization
Language	Englisch
Type of participation	Wahlpflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: every year - winter semester Maximal capacity: 25 students Course admittance prerequisites: Innovation Driven Software Engineering (MOD1-01), R&D Project Management (MOD1-04) (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	Input from: Innovation Driven Software Engineering (MOD1-01) R&D Project Management (MOD1-04) Usability Engineering (MOD2-01)
Learning outcomes	Learning outcomes 7.1 Knowledge Knows relevant theoretical foundations, area: computer science and society Know methodical background of case studies and surveys Is aware of critical limitations of methods for evaluating impact 7.2 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 individuals, environment and society, during the concept and development phase Can conduct methodologically structured evaluations (e.g. field observation, lab tests) and surveys 7.3 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/features on individuals, environment and society Understands scientific publication in the related areas
Course description and course structure	Course Description Digitalization in private and professional domains is influencing intensely and sometimes even revolutionizing 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 influences of e.g. a product or service currently in development will be taken into account to shape the prospective 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 Skills trained in this course: theoretical knowledge, practical skills and scientific competences
Teaching and training methods	Teaching and training methods Theoretical knowledge: e-learning modules on formal methods, tool tutorials Practical Skills: Projects with MechatronicUML Scientific Competences: literature review and synthesis into a paper
Assessment of course	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)
Requirements for award of credits	Scientific Focus (Pre-)Studies & surveys about socioeconomic impacts of digitalization Paper with literature review/state-of-the-art
Module mapping	Input for: R&D project & Thesis
References	References 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 Communication Technology, Springer, 2014, ISBN 3662442086, 9783662442081 eHealth: Legal, Ethical and Governance Challenges, Carlisle George, Diane Whitehouse, Penny Duquenoy, 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

MOD-E04 – Requirements Engineering

MOD-E05 – IoT & Edge Computing

Number	MOD-E05
Title	IoT & Edge Computing
Language	Englisch
Type of participation	Wahlpflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: every year - summer semester Maximal capacity: 25 students Course admittance prerequisites: (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Sommersemester
Course admittance prerequisites	Input from: None
Learning outcomes	Learning outcomes 6.1 Knowledge Knows concepts and architectures of real-time embedded systems Knows key aspects of real-time networking Has acquired overview of cloud computing and selected cloud platforms 6.2 Skills Can implement, deploy and test simple IoT-systems Can set-up and utilize a cloud system Can analyze the E2E latency in distributed systems 6.3 Competence - attitude Can design an simple IoT system for a given set of requirements Can structure an IoT development project regarding function and time Can propose and implement measures to reduce latency in a distributed system
Course description and course structure	Course Description Internet of things (IoT) is a fundamental building block for digitization and the upcoming information society. This course provides insights into key IoT-technologies including embedded systems, networks and cloud computing. For the selection of use cases and technologies the course focuses on the area of Edge Computing. Within this area students learn about latency analysis and optimization in distributed systems. Last not least, the course offers hands on experiences with IoT and Edge Computing technologies through focused team projects and homework assignments. Course Structure Introduction Real-time Embedded Systems Real-Time Networking Cloud Computing Edge Computing Application Focus Students conduct a project about Edge Sensor Fusion Students work with Gabriel - Edge Computing Platform for Wearable Cognitive Assistance Skills trained in this course: theoretical, practical and scientific skills and competences
Teaching and training methods	Teaching and training methods E-learning modules and lectures on IoT and Edge Computing Small project with Eclipse IoT stack Access to the Open Edge Computing Initiative and the Living Edge Labs
Assessment of course	Assessment of the course: Oral Exam at the end of the course (50%) and individual programming task (50%): implementation of cloud based IoT system for a robot, demonstration of the result
Requirements for award of credits	Scientific Focus During the module recent topics from the Open Edge Computing Initiative will be discussed and papers from relevant conferences will be reviewed.
Module mapping	Input for: None
References	References Peter Marwedel: Embedded System Design, 2nd Edition, Springer, 2011 Andrew S. Tanenbaum, David J. Wetherall: Computer Networks, 5th Edition, Pearson Education, 2014 Thomas Erl, Zaigham Mahmood, Ricardo Puttini, Cloud Computing, Prentice Hall, 2013

MOD-E06 – Trends in Digital Transformation

MOD-E07 – Information Processing and Data Analytics

Number	MOD-E07
Title	Information Processing and Data Analytics
Language	Englisch
Type of participation	Wahlpflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: every year - winter semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	Input from: none
Learning outcomes	Learning outcomes 7.1 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 7.2 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 7.3 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
Course description and course structure	Course Description 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) Application Focus Students will be guided through a case study project where they set up a small experiment for data collection, data storage and query and data processing for an example case. They form teams and set up IT tools. Trainings: students attend an Excel training and an IBM Watson training Skills trained in this course: theoretical knowledge, practical skills and scientific competences
Teaching and training methods	Teaching and training methods Theoretical knowledge: (video-)lectures introducing concepts, methods and tools, tool tutorials Practical Skills: group work in the case study project to practice concepts and methods, to develop skills and to work on case studies Scientific Competences: presentations to communicate results and do a scientific discussion and reflection
Assessment of course	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)
Requirements for award of credits	Scientific Focus Students work in teams and set up data analytics experiments and tools for their respective case study project.
Module mapping	Input for: none
References	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

MOD-E08 – Formal Methods

Number	MOD-E08
Title	Formal Methods
Language	Englisch
Type of participation	Wahlpflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	180 (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Wintersemester
Course admittance prerequisites	programming
Learning outcomes	Knowledge Knows methodology of formal verification Knows relevant theoretical background Knows specific requirements Skills Can methods on use case Can model verification artefacts (e.g. properties) Can use MechatronicUML approach and tools Competence - attitude Can research on state of the art and theoretical background Can demonstrate and discuss results in group Can structure scientific field and get overview
Course description and course structure	Software has become the driving force in the development of self-optimizing mechatronic systems. Such systems include hard-realtime coordination, which is realized by software, at the network level between distributed components as well as controllers which are more and more implemented by software. The communication goes beyond the use of system and environmental data from controllers. If necessary, complex status information about appropriate protocols and communication channels are exchanged, which themselves can massively influence the underlying behavior of the individual components. This development leads to extremely complex hybrid (discrete / continuous) software. In addition, self-optimizing mechatronic systems are often used in safety-critical environments. This enforces the use of formal verification techniques to ensure the correctness of specified properties. In the course concepts and methods for the modelling and verification of these mechatronic systems are introduced and formally described. In order to enable an efficient verification for such mechatronic systems, techniques like abstraction, decomposition as well as rule-based modelling are introduced. Here, these non orthogonal techniques are skillfully combined. One aim is to handle all models specified by all different domains. The presented approach for the model-based verification of mechatronic systems is massively characterized by the integration of efficient verification techniques for the different domains, based on their domain specific model-based knowledge. Course Structure Motivation: What are Formal Methods? Why should we use Formal Methods? When in the overall development process should we use Formal Methods? Model Checking Theorem Proving Testing Formal Verification in practice: The MechatronicUML Approach Recent Research: literature review AMALTHEA Methodology and Tool Chain Case Studies CS01: AMALTHEA tool chain – will be used to integrate formal verification tools CS02: HVAC control system demonstrator – will be used as example CS07: Rail Cab – will be used as an example Skills trained in this course: theoretical and methodological skills
Teaching and training methods	Lectures, Labs (with MechatronicUML), homework Access to recent research papers Literature review and discussion of results
Assessment of course	Assessment of the course: Written Exam at the end of the course (50%) and group work as homework (50%): verification of an example, demonstration and presentation
Requirements for award of credits	MOD1-02 – Distributed and Parallel Systems MOD1-03 - Embedded Software Engineering MOD2-01 – Mechatronic Systems Engineering
Module mapping	Connects to: MOD-E04 – SW Architectures for Embedded Systems
References	Spivey: The Z Reference Manual (http://spivey.oriel.ox.ac.uk/mike/zrm/zrm.pdf) E. Clarke et al.: Model Checking, MIT Press T. Fischer, J. Niere, L. Torunski, and A. Zündorf: Story Diagrams: A new Graph Rewrite Language based on the Unified Modeling Language. In Proc. of the 6th International Workshop on Theory and Application of Graph Transformation (TAGT), Paderborn, Germany, 1998 W. Reisig: Petrinetze: Modellierungstechnik, Analysemethoden, Fallstudien. Vieweg+Teubner, 2010 J. Bengtsson, W. Yi: Timed Automata: Semantics, Algorithms and Tools. In Lecture Notes on Concurrency and Petri Nets. W. Reisig and G. Rozenberg (eds.), LNCS 3098, Springer-Verlag, 2004 T. Eckart, C. Heinzemann, S. Henkler, M. Hirsch, C. Priesterjahn, W. Schäfer: Modeling and verifying dynamic communication structures based on graph transformations. Computer Science - Research and Development 28(1): S. 3-22, Feb. 2013 M. Hirsch: Modell-basierte Verifikation von vernetzten mechatronischen Systemen. Dissertation, Logos Verlag, 2008

MOD-E09 – Managing Digital Change

Number	MOD-E09
Title	Managing Digital Change
Language	Englisch
Type of participation	Wahlpflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: every year - summer semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Sommersemester
Course admittance prerequisites	Input from: None
Learning outcomes	Learning outcomes 6.1 Knowledge explain the basics of the digital transformation in organizations explain and compare digital business models explain methods and tools for change management explain the core concepts of “Arbeit 4.0” 6.2 Skills analyze and develop digital transformation projects apply change management to organizations develop tailored concepts for sustainable digital transformation 6.3 Competence - attitude Students train to develop and discuss concepts in teams They can present their results to companies and discuss in a professional context Students work in teams and set up a digital transformation project for their respective case study
Course description and course structure	Course Description The digital transformation 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. Digital Transformation in Organisations 1.1 New digitalized forms of organisation 1.2 Business models and business relations in the digital era 1.3 Structural resistance of organisations against digital change 1.4 Chances and risks of digital transformation in organisations 2. Socio-economic Impact of Digital Transformation 2.1 Digital transformation as a socio-economic trend 2.2 “Arbeit 4.0” 2.3 Education and training as impact mitigation 2.3 Analysis of Case Studies 3. Sustainable Digital Transformation 3.1 Stakeholder management in digital transformation projects 3.2 Project management for digital transformation projects 3.3 Efficiency and effectivity measurement 3.4 Sustainability and maturity models Application Focus Students will be guided through a case study project where they plan a digital transformation project for an example case. This example case will be taken preferably from a real company project. Companies can bring their digital transformation projects as a case study for a block week or summer school workshop. Students form teams to prepare the respective project and present it in a kick-off presentation to the companies. Skills trained in this course: theoretical, practical and scientific skills and competences
Teaching and training methods	Teaching and training methods lectures introducing concepts, methods and tools, own literature reading group work in the case study project to practice concepts and methods, to develop skills and to work on case studies presentations to communicate results and do a scientific discussion and reflection
Assessment of course	Assessment of the course: contributions within case study project (team presentation) (50%) and written paper (literature review, report or survey, approx. 25 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund) (50%)
Requirements for award of credits	Scientific Focus Literature review and analysis. Deductive own research based on the literature. Scientific reflection and discussion in the teams.
Module mapping	Input for: None
References	References John Hayes: The Theory and Practice of Change Management, Palgrave 4th ed. 2014, ISBN-10: 1137275340 Esther Cameron, Mike Green: Making Sense of Change Management: A Complete Guide to the Models, Tools and Techniques of Organizational Change, Kogan Page, 2015, ISBN-10: 0749472588 Joe Tidd, John Bessant: Managing Innovation: Integrating Technological, Market and Organizational Change, John Wiley & Sons, 2013 Tim Teuscher: SMEs and Digital Transformation: Are Change Management Theories Transferable?, University of Strathclyde. Dept. of Strategy and Organisation, 2015 Pearl Zhu: Change Insight: Change as an Ongoing Capability to Fuel Digital Transformation, 2016, e-book, EAN: 9781483583273 Ralf T. Kreutzer,Tim Neugebauer,Annette Pattloch: Digital Business Leadership, Springer 2017, ISBN 978-3-658-11914-0

MOD-E10 – Digital Business Ecosystems

Number	MOD-E10
Title	Digital Business Ecosystems
Language	Englisch
Type of participation	Wahlpflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: every year – summer semester Maximal capacity: 25 students Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Jedes Semester
Course admittance prerequisites	Input from: None
Learning outcomes	Learning outcomes 7.1 Knowledge Students can explain the basics of cybernetics and systems theory explain and compare digital business models explain methods and tools for information supply chains explain the core concepts of DBEs 7.2 Skills Students are able to analyze and develop value chains and information supply chains apply ICT tools for information supply chains develop tailored processes for DBEs 7.3 Competence – attitude Students train to develop and discuss concepts in teams Students work in teams and set up DBE environments for their respective case study project
Course description and course structure	Course Description The term “Digital Business Ecosystem” (DBE) emerged beginning of the 2000s by adding “Digital” to Moore’s (1996) “Business Ecosystem” concept. The analysis, structuring, development and management of DBEs combine socio-economic concepts, ICT and biological concepts. Students will learn to understand, to analyse, to evaluate and to develop DBE for different application scenarios. Course Structure 1. Cybernetics and systems view 1.1 Biological Systems 1.2 Cybernetics and Systems theory, social theories 1.3 System models, e.g. Ropohl, Systems engineering 1.4 Evolutionary and self-organizing systems 2. Socio-economic view 2.1 Business Ecosystems 2.2 Business processes, business models and value chains 2.3 Innovation, competition and dynamics in business ecosystems 2.3 Analysis of Case Studies 3. ICT view 3.1 Information supply chain 3.2 ICT architectures and tools for DBEs 3.3 Efficiency and effectivity for DBEs 3.4 Analysis of Case Studies Application Focus Students will be guided through a case study project where they set up a small DBE for an example case. They form teams and set up IT tools. Skills trained in this course: theoretical knowledge, practical skills and scientific competencies
Teaching and training methods	Teaching and training methods Theoretical knowledge: Lectures introducing concepts, methods and tools Practical Skills: Group work in the case study project to practice concepts and methods, to develop skills and to work on case studies Scientific Competences: Presentations to communicate results and do a scientific discussion and reflection
Assessment of course	Assessment of the course: Theoretical knowledge: Oral or written exam at the end of the course (50%) and Practical Skills: contributions within case study project (team presentation) => demonstration of the result (50%)
Requirements for award of credits	Scientific Focus Students will do literature review on DBE and do scientific discussion and reflections
Module mapping	Input for: None
References	References F. Nashira, A. Nicolai, P. Dini, M.L. Louarn, L.R. Leon: Digital Business Ecosystem. European Commission, 2010, Retrieved from http://www.digital-ecosystems.org/book/de-book2007.html, , last accessed June 06, 2013 S. Sun, J. Yen: Information Supply Chain: A Unified Framework for Information-Sharing, P. Kantor et al. (Eds.): ISI 2005, LNCS 3495, pp. 422 – 428, 2005 CERP-IoT: Vision and Challenges for realizing the Internet of Things, European Union, 2010 A. Humphreys, K. Grayson: The Intersecting Roles of Consumer and Producer: A Critical Perspective on Co-Production, Co-Creation and Prosumption, Sociology Compass 2, 2008

MOD-E11 – Trends of Artificial Intelligence in Business Informatics

S – Research Seminar

Number	S
Title	Research Seminar
Language	Deutsch
Type of participation	Wahlpflichtfach
Credits	6
Hours per week	4
Workload (self study and contact hours)	Parameters ECTS: 6 Hours of study in total: 180 Weekly hours per semester: 4 Contact hours: 60 Self-Study hours: 120 Course characteristics: elective Course frequency: summer and winter semester Maximal capacity: individual Course admittance prerequisites: none (60 + 120)
Semester	2
Duration	1 Semester
Frequency	Jedes Semester
Course admittance prerequisites	Input from: None
Learning outcomes	Learning outcomes 6.1 Knowledge explain the basics of the digital transformation in organizations explain and compare digital business models explain methods and tools for change management explain the core concepts of “Arbeit 4.0” 6.2 Skills analyze and develop digital transformation projects apply change management to organizations develop tailored concepts for sustainable digital transformation 6.3 Competence - attitude Students train to develop and discuss concepts in teams They can present their results to companies and discuss in a professional context Students work in teams and set up a digital transformation project for their respective case study
Course description and course structure	Course Description 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 Digitalization, 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 the Dortmund International Research Conference at FH Dortmund. Application Focus The research seminar has a mainly scientific focus. Skills trained in this course: theoretical, practical and scientific skills and competences
Teaching and training methods	Teaching and training methods Research seminars are done with individual supervision: Writing of a scientific report Presentations to communicate and discuss the findings Individual review and feedback on papers and presentations
Assessment of course	Assessment of the course: contributions within case study project (team presentation) (50%) and written paper (literature review, report or survey, approx. 25 pages) and presentation (in class or at a student conference, e.g. International Research Conference Dortmund) (50%)
Requirements for award of credits	Scientific Focus The Research Seminar is embedded into the scientific activities of the university, especially within the research institutes IDiAL and IKT.
Module mapping	Input for: MOD3-03 – Research Project (Thesis) + Colloquium P – Master Thesis + Colloquium
References	References German and European Research Agendas, recent research papers

Thesis und Colloquium

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