Optimizing Data Science: Strategic Selection and Implementation of Data Science Process Models
Im Promotionsvorhaben werden unterstützende Artefakte entwickelt, um die strategische Auswahl und praktische Implementierung von Data Science Process Models (DSPM) zu optimieren, das Prozesswissen nachhaltig zu vertiefen und Unternehmen zu befähigen, das zunehmende Volumen an Data Science Initiativen effektiv zu managen und durchzuführen.
Projektverantwortliche: Stefan Rösl
Mitglied im Verbundkolleg: 17.05.2024
Betreuender OTH Amberg-Weiden:
Prof. Dr. Christian Schieder
Betreuender Hochschule Neu-Ulm:
Prof. Dr. Daniel Schallmo
Stefan Rösl
OTH Amberg-Weiden s.roesl@oth-aw.de Projekt: Optimizing Data Science: Strategic Selection and Implementation of Data Science Process Models
Publikationen:
‚A Taxonomy of Data Science Process Models: Insights from Science and Practice.‘
Rösl, S., Schieder, C. (2024).
ECIS 2024 Proceedings.
https://www.researchgate.net/
‚No Need to Cry over Spilt Milk: A Workflow for Regenerating Graph
Data Using Robotic Process Automation.‘
Auer, T., Schieder, C. (2024).
In: Mandviwalla, M., Söllner, M., Tuunanen, T. (eds)
Design Science Research for a Resilient Future. DESRIST 2024.
https://www.researchgate.net/
‚Next-Generation Business Process Management (BPM): A Systematic Literature Review of Cognitive Computing and Improvementsin BPM.‘
Hildebrand, D., Rösl, S., Auer, T., Schieder, C. (2024).
Proceedings of the S-BPM ONE 2024.
https://www.researchgate.net/
‚ Data Catalogs in an industrial SME context – A
systematic literature review. Proceedings of the S-BPM ONE 2024.‘
Kick, D., Rösl, S., Auer, T., Schieder, C. (2024).
Proceedings of the S-BPM ONE 2024.
https://www.researchgate.net/
‚ Walking Away from Omelas:
Towards a Comprehensive Model for Successful Adoption of Industry 4.0 Technologies in SMEs.‘
Rüeck, L., Broy, V., Riedl, S., Rösl, S., Auer, T., Schieder, C. (2024).
Proceedings of the S-BPM ONE 2024.
https://www.researchgate.net/
‚ Bridging the Operationalization Gap: Towards a Situational
Approach for Data Analytics in Manufacturing SMEs. ‚
Rösl, S., Auer, T., Schieder, C. (2023).
Proceedings of the International Conference
on Innovative Intelligent Industrial Production and Logistics (IN4PL) 2023.
https://www.researchgate.net/
‚Addressing the Data Challenge in Manufacturing SMEs: A
Comparative Study of Data Analytics Applications with a Simplified Reference Model.‘
Rösl, S., Auer, T., Schieder, C. (2023).
Proceedings of the S-BPM ONE 2023.
https://www.researchgate.net/
‚Exploring Potential Barriers for the Adoption of Cognitive
Technologies in Industrial Manufacturing SMEs – Preliminary Results of a Qualitative Study.‘
Auer, T., Rösl, S., Schieder, C. (2023).
Proceedings of the S-BPM ONE 2023.
https://www.researchgate.net/