• Fill Hans-Georg

• Fill Hans-Georg

• Härer Felix
• Muff Fabian

In this course we will investigate emgerging technologies in the area of digitalization of enterprises such as blockchains and smart contracts, augmented and virtual reality, data warehouses, machine learning platforms, semantic technologies and others. The concrete range of technologies will be announced at the beginning of the course. The goal is to conduct explorative research on the characteristics of these technologies and the resulting opportunities for business applications.

For this purpose, small projects will be conducted in teams for analyzing particular usage scenarios for the technologies, designing according solutions using techniques of conceptual modeling and implementing them in the form of prototypes. Following lectures for introducing the foundations of the technologies, students will set up their own projects and regularly report upon the advancement in the projects in the form of common presentations. At the end, final presentations of the projects including working prototypes and the submission of a seminal report based on scientific standards are required. For the implementation of the prototypes it is necessary to dispose of programming knowledge, e.g. in Java, Node.js, or Python.

Moodle Link

The goal of this course is to get a fundamental understanding of common technologies in the context of digitalization of enterprises and being able to create according software applications. Further, the writing of seminal papers is trained for preparing students to conduct their own research as e.g. required for master and doctoral theses.

* Narayanan, Arvind, Clark, Jeremy (2017): Bitcoin's Academic
Pedigree. ACMQueue, Volume 15, issue 4.
https://queue.acm.org/detail.cfm?id=3136559

* Antonopoulos, Andreas M., Wood, Gavin (2018): Mastering Ethereum.
O’Reilly. https://github.com/ethereumbook/ethereumbook

* Hyperledger Fabric (2020): Documentation.
https://hyperledger-fabric.readthedocs.io/en/release-2.2/whatis.html

* Three.js Documentation (2020): https://threejs.org/docs/

* KNIME Analytics Documentation (2020): https://docs.knime.com/

* RapidMiner Educational License Program (2020):
https://rapidminer.com/educational-program/

* R Platform Manuals (2020): https://cran.r-project.org/manuals.html

* Protégé Documentation (2020):
https://protegewiki.stanford.edu/wiki/Main_Page

Course with continuous evaluation: after the registration period, you can no longer cancel your registration (see session calendar on the Faculty's website).
 

Exam lenght: 90 minutes

Only as a retake exam