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Martin Käppel
Martin Käppel
Research Assistant, University of Bayreuth
Verified email at uni-bayreuth.de
Title
Cited by
Cited by
Year
Leveraging small sample learning for business process management
M Käppel, S Schönig, S Jablonski
Information and Software Technology 132, 106472, 2021
222021
Evaluating predictive business process monitoring approaches on small event logs
M Käppel, S Jablonski, S Schönig
International Conference on the Quality of Information and Communications …, 2021
182021
Upper-bounded model checking for declarative process models
N Schützenmeier, M Käppel, S Petter, S Jablonski
The Practice of Enterprise Modeling: 14th IFIP WG 8.1 Working Conference …, 2021
92021
Logic based look-ahead for the execution of multi-perspective declarative processes
M Käppel, N Schützenmeier, S Schönig, L Ackermann, S Jablonski
Enterprise, Business-Process and Information Systems Modeling: 20th …, 2019
92019
Model-Agnostic Event Log Augmentation for Predictive Process Monitoring
M Käppel, S Jablonski
International Conference on Advanced Information Systems Engineering, 381-397, 2023
82023
Automaton-based comparison of Declare process models
N Schützenmeier, M Käppel, L Ackermann, S Jablonski, S Petter
Software and Systems Modeling 22 (2), 667-685, 2023
82023
Language-independent look-ahead for checking multi-perspective declarative process models
M Käppel, L Ackermann, S Schönig, S Jablonski
Software and Systems Modeling, 1-23, 2021
72021
Bridging Research Fields: An Empirical Study on Joint, Neural Relation Extraction Techniques
L Ackermann, J Neuberger, M Käppel, S Jablonski
International Conference on Advanced Information Systems Engineering, 471-486, 2023
62023
Detection of declarative process constraints in LTL formulas
N Schützenmeier, M Käppel, S Petter, S Schönig, S Jablonski
Enterprise and Organizational Modeling and Simulation: 15th International …, 2019
62019
Cost-sensitive predictive business process monitoring
M Käppel, S Jablonski, S Schönig
European Conference on Advances in Databases and Information Systems, 14-26, 2021
52021
Scenario-Based Model Checking of Declarative Process Models.
N Schützenmeier, M Käppel, M Fichtner, S Jablonski
ICEIS (2), 406-417, 2023
42023
A Comparative Study for the Selection of Machine Learning Algorithms based on Descriptive Parameters.
C Kumar, M Käppel, N Schützenmeier, P Eisenhuth, S Jablonski
DATA, 408-415, 2019
42019
Comparing the Expressiveness of Imperative and Declarative Process Models
N Schützenmeier, S Jablonski, M Käppel, L Ackermann
International Workshop on Model-Driven Organizational and Business Agility …, 2023
32023
Deviance analysis by means of redescription mining
M Käppel, E Ahmeti, S Jablonski
International Conference on Business Process Modeling, Development and …, 2022
32022
Recent Advances in Data-Driven Business Process Management
L Ackermann, M Käppel, L Marcus, L Moder, S Dunzer, M Hornsteiner, ...
arXiv preprint arXiv:2406.01786, 2024
22024
Attention Please: What Transformer Models Really Learn for Process Prediction
M Käppel, L Ackermann, S Jablonski, S Härtl
International Conference on Business Process Management, 203-220, 2024
12024
Redescription mining-based business process deviance analysis
E Ahmeti, M Käppel, S Jablonski
Software and Systems Modeling, 1-29, 2024
2024
TeaPie—A Tool for Efficient Annotation of Process Information Extraction Data
J Neuberger, J Herrmann, M Käppel, H van der Aa, S Jablonski
2024
Correction: Automaton-based comparison of Declare process models
N Schützenmeier, M Käppel, L Ackermann, S Jablonski, S Petter
Software and Systems Modeling 22 (2), 687-687, 2023
2023
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