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Sandra Zilker
Sandra Zilker
Technische Hochschule Nürnberg Georg Simon Ohm
Bestätigte E-Mail-Adresse bei fau.de
Titel
Zitiert von
Zitiert von
Jahr
Prescriptive business process monitoring for recommending next best actions
S Weinzierl, S Dunzer, S Zilker, M Matzner
International conference on business process management, 193-209, 2020
622020
XNAP: making LSTM-based next activity predictions explainable by using LRP
S Weinzierl, S Zilker, J Brunk, K Revoredo, M Matzner, J Becker
International Conference on Business Process Management, 129-141, 2020
352020
The influence of algorithm aversion and anthropomorphic agent design on the acceptance of AI-based job recommendations.
J Ochmann, L Michels, S Zilker, V Tiefenbeck, S Laumer
ICIS, 2020
272020
A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs.
S Weinzierl, M Stierle, S Zilker, M Matzner
HICSS, 1-10, 2020
222020
Bringing light into the darkness-A systematic literature review on explainable predictive business process monitoring techniques
M Stierle, J Brunk, S Weinzierl, S Zilker, M Matzner, J Becker
192021
The evaluation of the black box problem for AI-based recommendations: An interview-based study
J Ochmann, S Zilker, S Laumer
Innovation Through Information Systems: Volume II: A Collection of Latest …, 2021
182021
An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs
S Weinzierl, S Zilker, J Brunk, K Revoredo, A Nguyen, M Matzner, ...
arXiv preprint arXiv:2005.01194, 2020
182020
From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events.
S Weinzierl, S Zilker, M Stierle, M Matzner, G Park
Wirtschaftsinformatik (Zentrale Tracks), 364-368, 2020
182020
GAM (e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
P Zschech, S Weinzierl, N Hambauer, S Zilker, M Kraus
arXiv preprint arXiv:2204.09123, 2022
122022
The status quo of process mining in the industrial sector
S Dunzer, S Zilker, E Marx, V Grundler, M Matzner
Innovation Through Information Systems: Volume III: A Collection of Latest …, 2021
92021
Text-aware predictive process monitoring with contextualized word embeddings
L Cabrera, S Weinzierl, S Zilker, M Matzner
International Conference on Business Process Management, 303-314, 2022
72022
Predictive business process deviation monitoring
S Weinzierl, S Dunzer, J Tenschert, S Zilker, M Matzner
Proceedings of the 29th European Conference on Information Systems (ECIS), 2021
72021
Design principles for comprehensible process discovery in process mining
M Stierle, S Zilker, S Dunzer, JC Tenscher, G Karagegova
72020
Job seekers' artificial intelligence-related black box concerns
J Ochmann, S Zilker, S Laumer
Proceedings of the 2020 on Computers and People Research Conference, 101-102, 2020
42020
Best of both worlds: combining predictive power with interpretable and explainable results for patient pathway prediction
S Zilker, S Weinzierl, P Zschech, M Kraus, M Matzner
32023
Designing a Method for Resource-specific Next Activity Prediction
S Zilker
PACIS 2022 Proceedings, 2022
22022
Process Mining for Advanced Service Analytics–From Process Efficiency to Customer Encounter and Experience
S Zilker, E Marx, M Stierle, M Matzner
22022
Design Principles for Using Business Process Management Systems
S Dunzer, W Tang, N Höchstädter, S Zilker, M Matzner
International Conference on Business Process Management, 217-228, 2023
12023
Machine learning in business process management: A systematic literature review
S Weinzierl, S Zilker, S Dunzer, M Matzner
Expert Systems with Applications, 124181, 2024
2024
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
S Zilker, S Weinzierl, M Kraus, P Zschech, M Matzner
Health Care Management Science, 1-32, 2024
2024
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