| Overview on hybrid approaches to fault detection and diagnosis: Combining data-driven, physics-based and knowledge-based models Y Wilhelm, P Reimann, W Gauchel, B Mitschang Procedia CIRP 99, 278-283, 2021 | 57 | 2021 |
| SIMPL–A Framework for Accessing External Data in Simulation Workflows P Reimann, M Reiter, H Schwarz, D Karastoyanova, F Leymann Datenbanksysteme für Business, Technologie und Web (BTW), 2011 | 48 | 2011 |
| Data-Driven Fault Diagnosis in End-of-Line Testing of Complex Products V Hirsch, P Reimann, B Mitschang 2019 IEEE International Conference on Data Science and Advanced Analytics …, 2019 | 30 | 2019 |
| A framework to guide the selection and configuration of machine-learning-based data analytics solutions in manufacturing AGV Zacarias, P Reimann, B Mitschang Procedia CIRP 72, 153-158, 2018 | 28 | 2018 |
| Application Fields and Research Gaps of Process Mining in Manufacturing Companies S Dreher, P Reimann, C Gröger INFORMATIK 2020, 2021 | 21 | 2021 |
| Analytical Approach to Support Fault Diagnosis and Quality Control in End-Of-Line Testing V Hirsch, P Reimann, O Kirn, B Mitschang Procedia CIRP 72, 1333-1338, 2018 | 20 | 2018 |
| Extended Techniques for Flexible Modeling and Execution of Data Mashups. P Hirmer, P Reimann, M Wieland, B Mitschang DATA, 111-122, 2015 | 18 | 2015 |
| A New Process Model for the Comprehensive Management of Machine Learning Models. C Weber, P Hirmer, P Reimann, H Schwarz ICEIS (1), 415-422, 2019 | 17 | 2019 |
| Exploiting Domain Knowledge to Address Multi-Class Imbalance and a Heterogeneous Feature Space in Classification Tasks for Manufacturing Data V Hirsch, P Reimann, B Mitschang Proceedings of the 46th International Conference on Very Large Databases …, 2020 | 16 | 2020 |
| Data Science Approaches to Quality Control in Manufacturing: A Review of Problems, Challenges and Architecture Y Wilhelm, U Schreier, P Reimann, B Mitschang, H Ziekow Springer Proceedings Series Communications in Computer and Information …, 2020 | 16 | 2020 |
| A model management platform for industry 4.0–enabling management of machine learning models in manufacturing environments C Weber, P Hirmer, P Reimann Business Information Systems: 23rd International Conference, BIS 2020 …, 2020 | 15 | 2020 |
| Event-driven business process management in Engineer-to-Order supply chains J Minguez, S Zor, P Reimann Proceedings of the 2011 15th International Conference on Computer Supported …, 2011 | 15 | 2011 |
| Design, implementation, and evaluation of a tight integration of database and workflow engines P Reimann, H Schwarz, B Mitschang Journal of Information and Data Management 2 (3), 353, 2011 | 13 | 2011 |
| A pattern approach to conquer the data complexity in simulation workflow design P Reimann, H Schwarz, B Mitschang OTM Confederated International Conferences" On the Move to Meaningful …, 2014 | 12 | 2014 |
| Datenmanagementpatterns in Simulationsworkflows P Reimann, H Schwarz Datenbanksysteme für Business, Technologie und Web (BTW) 2030, 2013 | 12 | 2013 |
| Data patterns to alleviate the design of scientific workflows exemplified by a bone simulation P Reimann, H Schwarz, B Mitschang Proceedings of the 26th International Conference on Scientific and …, 2014 | 11 | 2014 |
| Generating BPEL processes from a BPEL4Chor description P Reimann Universitätsbibliothek der Universität Stuttgart, 2007 | 10 | 2007 |
| A Real-World Application of Process Mining for Data-Driven Analysis of Multi-Level Interlinked Manufacturing Processes A Birk, Y Wilhelm, S Dreher, C Flack, P Reimann, C Gröger Procedia CIRP 104, 417-422, 2021 | 9 | 2021 |
| MMP-A Platform to Manage Machine Learning Models in Industry 4.0 Environments C Weber, P Reimann 2020 IEEE 24th International Enterprise Distributed Object Computing …, 2020 | 9 | 2020 |
| Konzepte zur Datenverarbeitung in Referenzarchitekturen für Industrie 4.0: Konsequenzen bei der Umsetzung einer IT-Architektur C Weber, M Wieland, P Reimann Datenbank-Spektrum 18, 39-50, 2018 | 8 | 2018 |
