Digital Transformation

The research area "Digital Transformation" complements the study programs of Management, Communication & IT and Digital Business & Software Engineering respectively in the fields of applied research and development of practice-oriented solutions for digitization and their comprehensive integration into practice. We work on technical, social, organisational and individual aspects in an interdisciplinary way.

In the context of digital transformation, the secure, analytical handling of data, the interaction of people with technical systems, the design of new work environments, and the adaptation of work and production processes and their control in real as well as virtual worlds are essential for us.

 

Data & Analytics

Large amounts of data generated from a wide variety of data sources (e.g. IoT, etc.) can be used as a basis for decisions, improvement of products and processes and for the development of new business models. Data & Analytics deals with the storage, preparation, analysis and visualization of data using suitable system architectures.

  • Implementation of concrete projects and tasks in companies
  • Integration of the Internet of Things as an essential data source
  • Visualization of data (e.g. dashboards)
  • Application of modern tools (e.g. from the field of artificial intelligence, for decision making)
  • Further development of existing IT system architectures

IT Security & Privacy

IT security and privacy make a contribution to the secure handling of data and information systems, which are becoming increasingly relevant in the course of increasing digitalization.

  • Security of machine learning
  • Network security
  • Encryption technologies
  • Multimedia security

Technology Interaction & Innovation

Technology Interaction & Innovation deals with the interaction of people and technology in the professional and private environment. In the field of innovation research, the focus is on the methodology of Design Thinking.

  • Experience and usability of social and web-based services
  • Evaluation of the acceptance of assistance technologies
  • Study of trust in intelligent systems

Operational Excellence & Agile Governance

The improvement of business decisions and the establishment of stable processes in the company - taking into account rapidly changing technologies, frameworks and the regulatory environment - are essential design fields of operational excellence.

  • Internet of Things
  • Smart production
  • Data evaluation of large amounts of data
  • Audits
  • Compliance and IT governance of agile action

Next World of Work / Virtual & Augmented Reality

Next World of Work / Virtual & Augmented Reality unites different topics in the context of a changing world of work.

  • Creation and agility of jobs
  • Life-long learning
  • Talent diversity & innovativeness
  • Organizational design
  • Leadership
  • Innovative technologies e.g. Virtual Reality (VR) & Augmented Reality (AR)
Contact
Peter J. Mirski
Prof. Dr. Peter J. Mirski Head of Department & Studies

If you have any questions regarding this research area, please contact us:
This email address is being protected from spambots. You need JavaScript enabled to view it.


Game Over Eva(sion): Safe Deep Learning with Game Theory
Duration:
2019 - 2021

Team:
Alexander Monz, BA MA

Description:
The project "Game Over Eva(sion): Securing Deep Learning with Game Theory" aims to protect deep learning classifiers against targeted attacks. Deep learning classifiers are not only popular in scientific research but are also more and more adapted to the daily life, as self-driving cars, smartphones, and digital personal assistants use these kind of algorithms. Unfortunately, recent research has shown that almost all deep learning classifiers are vulnerable to so-called evasion attacks. In an evasion attack, an attacker can slightly modify a benign object and by this achieves a misclassification with a very high probability. The robustness of deep learning classifiers to these attacks is an open problem. We will model the competition of an attacker who is able to launch an evasion attack and the defender who wants to train a deep learning classifier that is robust against such an attack with means of game theory. In a first step, we will analyze which concepts of other research areas, such as adversarial classification, can be translated to the domain of secure deep learning. Then, we will develop a game-theoretic model that captures all relevant aspects and dependencies between the attacker's and the defender's strategies. In the course of the project, we expect the first theoretically well-founded results on the achievable security of deep learning classifiers in the presence of evasion attacks. Furthermore, we will evaluate existing countermeasures against evasion attacks to gain insights about their optimality when facing a strategic attacker. Finally, we want to implement the key properties from out theoretical models in a practical deep learning classifier. We will compare our classifier against other state-of-the-art classifiers in terms of robustness against evasion attacks and accuracy on benign input objects. By this, we can validate if it is possible to make deep learning classifiers robust against evasion attacks. The expected results of the project will enable us to judge if deep learning classifiers are suitable for scenarios where an attacker has incentives to explicitly fool them, i.e., security-critical areas and widespread consumer products.

Process Analysis and Data Model Preparation / Tiroler Rohre GmbH
Duration:
2019 - 2021

Project Lead:
Prof. (FH) Dr. Christian Ploder

Description:
Industry and business are currently undergoing a significant transformation towards an automated and digitalized world. The challenges for manufacturing companies are manifold and include technical, economic, and sociological aspects. The Tiroler Rohrer GmbH is currently facing the challenge of carrying out a wall thickness optimization on one of the gravity die casting machines (machine 4), whereby the main focus will be on the tip end. The following reasons are given: - wall thickness at the tip end is still difficult to control - The simulation did not yield a clear result for control - peak wall thickness depends on too many parameters - Through automated measurement of the wall thickness profile for each pipe, new process data is available - By reading out all (available) process parameters, patterns between process parameters and wall thicknesses should be made visible.

Traclink Data Analysis
Duration:
2018 - 2019

Project Lead:
FH-Prof. Dr. Peter J. Mirski

Team:
Dr. Pascal Schöttle

Matthias Janetschek, PhD

FH-Prof. Dr. Reinhard Bernsteiner

Dipl.-Ing. Sarah Dörschlag, BSc

Description:
The Lindner company manufactures tractors for green, mountain and arable farming and vehicles for municipal services (Unitrac), which are already equipped with various sensors. These sensors are used to collect different vehicle data, which are stored on a server. From this data memory the data can be tapped and prepared for further processing. A uniform software structure, evaluation tools, apps and a user interface are missing for the analysis of the data. The company Lindner would like to achieve an optimization of the work process based on the analysis of the vehicle data, coupled with external data (e.g. weather forecast). This includes, among other things, automated driving, ploughing, snow removal with the help of GPS data, irrigation of plants (in viticulture), calculation of the required amount of road salt based on the weather situation, calculation of the actual consumption of road salt, etc. In winter 2018/19, special attention will be paid to the further data collection and processing of the actual amount of road salt in municipal applications. Thus, with the support of weather forecasts and GPS data, the amount of road salt should be predicted and optimized.

Heidelberg Laureate Forum - women‘s careers in mathematics and computer science
Duration:
2020 - 2023

Project Lead:
FH-Prof. Dr. Peter J. Mirski

Team:
FH-Prof. Dr. Dietmar Kilian

Carina Kollmitzer

Elisabeth Rabanser, MSc

Description:
Integration of various topics in the context of a changing world of work: * women‘s careers in mathematics and computer science * creation & agility of jobs, lifelong learning, talented diversity & innovativeness, innovative technologies, organizational design * ESCO european skills and competences framework Women in Technology - Education & Career


  • Disztinger, P., Schlögl, S., & Groth, A. (2017). Technology Acceptance of Virtual Reality for Travel Planning. In R. Schegg & B. Stangl (Eds.), Information and Communication Technologies in Tourism 2017: Proceedings of the International Conference in Rome, Italy, January 24-26, 2017 (pp. 255-268). Cham: Springer International Publishing.
  • Thomas Baumhauer, Pascal Schöttle, and Matthias Zeppelzauer. "Machine Unlearning: Linear Filtration for Logit-based Classifiers". In: https://arxiv.org/abs/2002.02730

no lectures available at the moment