PUBLIKATIONSSERVER

Nico Migenda

https://orcid.org/0000-0002-7223-1735

16 Publikationen

Alle markieren

[16]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus, M., Migenda, N., Weller, J., Kohlhase, M., & Schenck, W. (2025). Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing, 9(10). https://doi.org/10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[15]
2024 | Konferenzbeitrag | FH-PUB-ID: 4916
Weller, J., Migenda, N., Kühn, A., & Dumitrescu, R. (2024). Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories. Presented at the 6th Conference on Production Systems and Logistics, Honolulu, Hawaii: Hannover : publish-Ing. https://doi.org/10.15488/17721
HSBI-PUB | DOI
 
[14]
2024 | Artikel | FH-PUB-ID: 5497
Weller, J., Migenda, N., Enzberg, S. von, Kohlhase, M., Schenck, W., & Dumitrescu, R. (2024). Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP, 128, 424–429. https://doi.org/10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[13]
2024 | Artikel | FH-PUB-ID: 4050
Migenda, N., Möller, R., & Schenck, W. (2024). Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition, 146. https://doi.org/10.1016/j.patcog.2023.110030
HSBI-PUB | DOI
 
[12]
2024 | Artikel | FH-PUB-ID: 4698
Migenda, N., Möller, R., & Schenck, W. (2024). NGPCA: Clustering of high-dimensional and non-stationary data streams. Software Impacts, 20. https://doi.org/10.1016/j.simpa.2024.100635
HSBI-PUB | DOI
 
[11]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus, M., Migenda, N., Weller, J., Schenck, W., & Kohlhase, M. (2024). Technical Readiness of Prescriptive Analytics Platforms: A Survey. In IEEE (Ed.), 2024 35th Conference of Open Innovations Association (FRUCT) (pp. 509–519). Tampere, Finland: IEEE. https://doi.org/10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[10]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller, J., Migenda, N., Liu, R., Wegel, A., von Enzberg, S., Kohlhase, M., … Dumitrescu, R. (2024). Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In O. Niggemann, J. Beyerer, M. Krantz, & C. Kühnert (Eds.), Machine Learning for Cyber-Physical Systems. Selected papers from the International Conference ML4CPS 2023 (Vol. 18, pp. 89–100). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[9]
2024 | Artikel | FH-PUB-ID: 4913
Weller, J., Migenda, N., Naik, Y., Heuwinkel, T., Kühn, A., Kohlhase, M., … Dumitrescu, R. (2024). Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics, 12(17). https://doi.org/10.3390/math12172663
HSBI-PUB | DOI
 
[8]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller, J., Migenda, N., Wegel, A., Kohlhase, M., Schenck, W., & Dumitrescu, R. (2023). Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In IEEE (Ed.), 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS) (pp. 1–7). Marrakesh, Morocco: IEEE. https://doi.org/10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[7]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe, C., Migenda, N., Pelkmann, D., Hötte, D. A., & Schenck, W. (2022). Collaborative System for Question Answering in German Case Law Documents. In L. M. Camarinha-Matos, A. Ortiz, X. Boucher, & A. L. Osório (Eds.), Collaborative Networks in Digitalization and Society 5.0 (pp. 303–312). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-14844-6_24
HSBI-PUB | DOI
 
[6]
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
Hoppe, C., Pelkmann, D., Migenda, N., Hotte, D. A., & Schenck, W. (2021). Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents. In 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 29–32). Laguna Hills, CA, USA: IEEE. https://doi.org/10.1109/AIKE52691.2021.00011
HSBI-PUB | DOI
 
[5]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (2021). Advanced Data Analytics Platform for Manufacturing Companies. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) (pp. 01–08). Vasteras, Sweden: IEEE. https://doi.org/10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[4]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann, L., Migenda, N., Voigt, T., Kohlhase, M., & Schenck, W. (2021). Variational Autoencoder based Novelty Detection for Real-World Time Series. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 1–7). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460825
HSBI-PUB | DOI
 
[3]
2021 | Artikel | FH-PUB-ID: 1203
Migenda, N., Möller, R., & Schenck, W. (2021). Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE, 16(3). https://doi.org/10.1371/journal.pone.0248896
HSBI-PUB | DOI
 
[2]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda, N., & Schenck, W. (2020). Adaptive Dimensionality Reduction for Local Principal Component Analysis. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1579–1586). Vienna, Austria: IEEE. https://doi.org/10.1109/ETFA46521.2020.9212129
HSBI-PUB | DOI
 
[1]
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda, N., Möller, R., & Schenck, W. (2019). Adaptive Dimensionality Adjustment for Online “Principal Component Analysis.” 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 (pp. 76–84). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-33607-3_9
HSBI-PUB | DOI
 
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16 Publikationen

Alle markieren

[16]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus, M., Migenda, N., Weller, J., Kohlhase, M., & Schenck, W. (2025). Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing, 9(10). https://doi.org/10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[15]
2024 | Konferenzbeitrag | FH-PUB-ID: 4916
Weller, J., Migenda, N., Kühn, A., & Dumitrescu, R. (2024). Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories. Presented at the 6th Conference on Production Systems and Logistics, Honolulu, Hawaii: Hannover : publish-Ing. https://doi.org/10.15488/17721
HSBI-PUB | DOI
 
[14]
2024 | Artikel | FH-PUB-ID: 5497
Weller, J., Migenda, N., Enzberg, S. von, Kohlhase, M., Schenck, W., & Dumitrescu, R. (2024). Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP, 128, 424–429. https://doi.org/10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[13]
2024 | Artikel | FH-PUB-ID: 4050
Migenda, N., Möller, R., & Schenck, W. (2024). Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition, 146. https://doi.org/10.1016/j.patcog.2023.110030
HSBI-PUB | DOI
 
[12]
2024 | Artikel | FH-PUB-ID: 4698
Migenda, N., Möller, R., & Schenck, W. (2024). NGPCA: Clustering of high-dimensional and non-stationary data streams. Software Impacts, 20. https://doi.org/10.1016/j.simpa.2024.100635
HSBI-PUB | DOI
 
[11]
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus, M., Migenda, N., Weller, J., Schenck, W., & Kohlhase, M. (2024). Technical Readiness of Prescriptive Analytics Platforms: A Survey. In IEEE (Ed.), 2024 35th Conference of Open Innovations Association (FRUCT) (pp. 509–519). Tampere, Finland: IEEE. https://doi.org/10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[10]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller, J., Migenda, N., Liu, R., Wegel, A., von Enzberg, S., Kohlhase, M., … Dumitrescu, R. (2024). Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In O. Niggemann, J. Beyerer, M. Krantz, & C. Kühnert (Eds.), Machine Learning for Cyber-Physical Systems. Selected papers from the International Conference ML4CPS 2023 (Vol. 18, pp. 89–100). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[9]
2024 | Artikel | FH-PUB-ID: 4913
Weller, J., Migenda, N., Naik, Y., Heuwinkel, T., Kühn, A., Kohlhase, M., … Dumitrescu, R. (2024). Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics, 12(17). https://doi.org/10.3390/math12172663
HSBI-PUB | DOI
 
[8]
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller, J., Migenda, N., Wegel, A., Kohlhase, M., Schenck, W., & Dumitrescu, R. (2023). Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories. In IEEE (Ed.), 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS) (pp. 1–7). Marrakesh, Morocco: IEEE. https://doi.org/10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[7]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe, C., Migenda, N., Pelkmann, D., Hötte, D. A., & Schenck, W. (2022). Collaborative System for Question Answering in German Case Law Documents. In L. M. Camarinha-Matos, A. Ortiz, X. Boucher, & A. L. Osório (Eds.), Collaborative Networks in Digitalization and Society 5.0 (pp. 303–312). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-14844-6_24
HSBI-PUB | DOI
 
[6]
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
Hoppe, C., Pelkmann, D., Migenda, N., Hotte, D. A., & Schenck, W. (2021). Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents. In 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (pp. 29–32). Laguna Hills, CA, USA: IEEE. https://doi.org/10.1109/AIKE52691.2021.00011
HSBI-PUB | DOI
 
[5]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, T., Migenda, N., Schöne, M., Pelkmann, D., Fricke, M., Schenck, W., & Kohlhase, M. (2021). Advanced Data Analytics Platform for Manufacturing Companies. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ) (pp. 01–08). Vasteras, Sweden: IEEE. https://doi.org/10.1109/ETFA45728.2021.9613499
HSBI-PUB | DOI
 
[4]
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann, L., Migenda, N., Voigt, T., Kohlhase, M., & Schenck, W. (2021). Variational Autoencoder based Novelty Detection for Real-World Time Series. In 2021 3rd International Conference on Management Science and Industrial Engineering (pp. 1–7). New York, NY, USA: ACM. https://doi.org/10.1145/3460824.3460825
HSBI-PUB | DOI
 
[3]
2021 | Artikel | FH-PUB-ID: 1203
Migenda, N., Möller, R., & Schenck, W. (2021). Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE, 16(3). https://doi.org/10.1371/journal.pone.0248896
HSBI-PUB | DOI
 
[2]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda, N., & Schenck, W. (2020). Adaptive Dimensionality Reduction for Local Principal Component Analysis. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1579–1586). Vienna, Austria: IEEE. https://doi.org/10.1109/ETFA46521.2020.9212129
HSBI-PUB | DOI
 
[1]
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda, N., Möller, R., & Schenck, W. (2019). Adaptive Dimensionality Adjustment for Online “Principal Component Analysis.” 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 (pp. 76–84). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-33607-3_9
HSBI-PUB | DOI
 
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