H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality
N. Migenda, R. Möller, W. Schenck, PLOS One 21 (2026).

Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems
M. Niederhaus, N. Migenda, J. Weller, M. Kohlhase, W. Schenck, Big Data and Cognitive Computing 9 (2025).

Clustering in High-Dimensional Data Streams with Adaptive Local Principal Component Analysis
N. Migenda, Clustering in High-Dimensional Data Streams with Adaptive Local Principal Component Analysis, Universität Bielefeld, 2025.

Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories
J. Weller, N. Migenda, Y. Naik, T. Heuwinkel, A. Kühn, M. Kohlhase, W. Schenck, R. Dumitrescu, Mathematics 12 (2024).

Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories
J. Weller, N. Migenda, R. Liu, A. Wegel, S. von Enzberg, M. Kohlhase, W. Schenck, R. Dumitrescu, in: O. Niggemann, J. Beyerer, M. Krantz, C. Kühnert (Eds.), Machine Learning for Cyber-Physical Systems. Selected Papers from the International Conference ML4CPS 2023, Springer Nature Switzerland, Cham, 2024, pp. 89–100.

Adaptive local Principal Component Analysis improves the clustering of high-dimensional data
N. Migenda, R. Möller, W. Schenck, Pattern Recognition 146 (2024).

Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories
J. Weller, N. Migenda, A. Kühn, R. Dumitrescu, in: Hannoverf: publish-Ing., 2024.

Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories
J. Weller, N. Migenda, S. von Enzberg, M. Kohlhase, W. Schenck, R. Dumitrescu, Procedia CIRP 128 (2024) 424–429.

NGPCA: Clustering of high-dimensional and non-stationary data streams
N. Migenda, R. Möller, W. Schenck, Software Impacts 20 (2024).

Technical Readiness of Prescriptive Analytics Platforms: A Survey
M. Niederhaus, N. Migenda, J. Weller, W. Schenck, M. Kohlhase, in: IEEE (Ed.), 2024 35th Conference of Open Innovations Association (FRUCT), IEEE, 2024, pp. 509–519.

Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories
J. Weller, N. Migenda, A. Wegel, M. Kohlhase, W. Schenck, R. Dumitrescu, in: IEEE (Ed.), 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS), IEEE, 2023, pp. 1–7.

Collaborative System for Question Answering in German Case Law Documents
C. Hoppe, N. Migenda, D. Pelkmann, D.A. Hötte, W. Schenck, in: L.M. Camarinha-Matos, A. Ortiz, X. Boucher, A.L. Osório (Eds.), Collaborative Networks in Digitalization and Society 5.0, Springer International Publishing, Cham, 2022, pp. 303–312.

Adaptive dimensionality reduction for neural network-based online principal component analysis
N. Migenda, R. Möller, W. Schenck, PLOS ONE 16 (2021).

Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents
C. Hoppe, D. Pelkmann, N. Migenda, D.A. Hotte, W. Schenck, in: 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), IEEE, 2021, pp. 29–32.

Advanced Data Analytics Platform for Manufacturing Companies
T. Voigt, N. Migenda, M. Schöne, D. Pelkmann, M. Fricke, W. Schenck, M. Kohlhase, in: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), IEEE, 2021, pp. 01–08.

Variational Autoencoder based Novelty Detection for Real-World Time Series
L. Steinmann, N. Migenda, T. Voigt, M. Kohlhase, W. Schenck, in: 2021 3rd International Conference on Management Science and Industrial Engineering, ACM, New York, NY, USA, 2021, pp. 1–7.

Adaptive Dimensionality Reduction for Local Principal Component Analysis
N. Migenda, W. Schenck, in: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2020, pp. 1579–1586.

Adaptive Dimensionality Adjustment for Online cPrincipal Component Analysisd
N. Migenda, R. Möller, W. Schenck, in: H. Yin, D. Camacho, P. Tino, A.J. Tallón-Ballesteros, R. Menezes, R. Allmendinger (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I, Springer International Publishing, Cham, 2019, pp. 76–84.