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Data and Process Science

Der Forschungsbereich Data and Process Science beschäftigt sich mit Zukunftsthemen in den Bereichen der Digitalisierung und der Zukunft der Arbeit inklusive Projekt- und Entwicklungsmanagement. Aber auch die Themenfelder der künstlichen Intelligenz, das maschinelle Lernen, Internet of Things (IoT), Data Mining sowie Predictive Statistics spielen eine große Rolle.

Publikationen

Verena Starflinger, Prof. Dr. Maren Martens

Best Practices of Circular Business Models – Benefits and Challenges (not only) for German Start-ups

Research Notes on Data and Process Science

Ulrich Ludolfinger, Prof. Dr. Maren Martens

An Autonomous Energy Management Concept for Sustainable Smart Cities

2023 IEEE European Technology and Engineering Management Summit (E-TEMS). Conference Proceedings.

Ulrich Ludolfinger, Vedran Peric, Prof. Dr. rer. Nat. Sascha Hauke, Thomas Hamacher, Prof. Dr. Maren Martens

Transformer Model Based Soft Actor-Critic Learning for HEMS

Proceedings of the 2023 IEEE International Conference on Power System Technology

Verena Starflinger, Prof. Dr. Maren Martens

Alternative Prize Money Distributions for Higher Gender Equity in Sports

Operations Research Proceedings 2022, Springer Nature Switzerland

Ulrich Ludolfinger, Daniel Zinsmeister, Vedran Peric, Prof. Dr. rer. Nat. Sascha Hauke, Thomas Hamacher, Prof. Dr. Maren Martens

Recurrent Soft Actor Critic Reinforcement Learning for Demand Response Problems

2023 IEEE Belgrade PowerTech. Conference Proceedings

Sebastian Steindl, Martin Surner

CIP-ES: Causal Input Perturbation for Explanation Surrogates

Proceedings of the 2nd Asia Conference on Algorithms, Computing and Machine Learning

Marlena Eisenschink , Christoph Glück, Miriam Maier, Elisabeth Scheuchenpflug , Prof. Dr. Maren Martens, Prof. Dr. Holger Timinger

A new trend in further education? Micro-degrees as a contribution to deal with new paradigms in SMEs

2022 IEEE German Education Conference (GeCon)

Anna Schidek, Markus Schmidtner, Prof. Dr. Maren Martens, Prof. Dr. Holger Timinger

ADmed – an adaptive technical process for the agile development of medical devices

Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 3: KMIS

Claudia Doering, Finn Reiche, Prof. Dr. Holger Timinger

Cross-University Platforms as an Enabler for Knowledge Management and Transfer

Finn Reiche, Claudia Doering

Study on Massive Open Online Courses in Universities
Alle Publikationen

Mitglieder des Forschungsbereichs Data and Process Science