PUBLIKATIONSSERVER

Nico Migenda

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

18 Publikationen

Alle markieren

[18]
2026 | Artikel | FH-PUB-ID: 6485 | OA
Migenda N, Möller R, Schenck W. H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality. PLOS One. 2026;21(1). doi:10.1371/journal.pone.0339171
HSBI-PUB | DOI | Download (ext.)
 
[17]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus M, Migenda N, Weller J, Kohlhase M, Schenck W. Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing. 2025;9(10). doi:10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[16]
2025 | Datenpublikation | FH-PUB-ID: 6486
Migenda N. Clustering in High-Dimensional Data Streams with Adaptive Local Principal Component Analysis. Universität Bielefeld; 2025. doi:10.4119/UNIBI/3006960
HSBI-PUB | DOI
 
[15]
2024 | Konferenzbeitrag | FH-PUB-ID: 4916
Weller J, Migenda N, Kühn A, Dumitrescu R. Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories. In: Hannover : publish-Ing.; 2024. doi: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. Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP. 2024;128:424-429. doi:10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[13]
2024 | Artikel | FH-PUB-ID: 4050
Migenda N, Möller R, Schenck W. Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition. 2024;146. doi:10.1016/j.patcog.2023.110030
HSBI-PUB | DOI
 
[12]
2024 | Artikel | FH-PUB-ID: 4698
Migenda N, Möller R, Schenck W. NGPCA: Clustering of high-dimensional and non-stationary data streams. Software Impacts. 2024;20. doi: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. Technical Readiness of Prescriptive Analytics Platforms: A Survey. In: IEEE, ed. 2024 35th Conference of Open Innovations Association (FRUCT). IEEE; 2024:509-519. doi:10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[10]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller J, Migenda N, Liu R, et al. Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In: Niggemann O, Beyerer J, Krantz M, Kühnert C, eds. Machine Learning for Cyber-Physical Systems. Selected Papers from the International Conference ML4CPS 2023. Vol 18. Technologien für die intelligente Automation. Cham: Springer Nature Switzerland; 2024:89-100. doi:10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[9]
2024 | Artikel | FH-PUB-ID: 4913
Weller J, Migenda N, Naik Y, et al. Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics. 2024;12(17). doi: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. 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). IEEE; 2023:1-7. doi:10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[7]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe C, Migenda N, Pelkmann D, Hötte DA, Schenck W. Collaborative System for Question Answering in German Case Law Documents. In: Camarinha-Matos LM, Ortiz A, Boucher X, Osório AL, eds. Collaborative Networks in Digitalization and Society 5.0. IFIP Advances in Information and Communication Technology. Cham: Springer International Publishing; 2022:303-312. doi: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 DA, Schenck W. 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). IEEE; 2021:29-32. doi:10.1109/AIKE52691.2021.00011
HSBI-PUB | DOI
 
[5]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt T, Migenda N, Schöne M, et al. Advanced Data Analytics Platform for Manufacturing Companies. In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ). IEEE; 2021:01-08. doi: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. Variational Autoencoder based Novelty Detection for Real-World Time Series. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:1-7. doi:10.1145/3460824.3460825
HSBI-PUB | DOI
 
[3]
2021 | Artikel | FH-PUB-ID: 1203
Migenda N, Möller R, Schenck W. Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE. 2021;16(3). doi:10.1371/journal.pone.0248896
HSBI-PUB | DOI
 
[2]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda N, Schenck W. Adaptive Dimensionality Reduction for Local Principal Component Analysis. In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE; 2020:1579-1586. doi:10.1109/ETFA46521.2020.9212129
HSBI-PUB | DOI
 
[1]
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda N, Möller R, Schenck W. Adaptive Dimensionality Adjustment for Online “Principal Component Analysis.” In: Yin H, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, Allmendinger R, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2019:76-84. doi:10.1007/978-3-030-33607-3_9
HSBI-PUB | DOI
 
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18 Publikationen

Alle markieren

[18]
2026 | Artikel | FH-PUB-ID: 6485 | OA
Migenda N, Möller R, Schenck W. H-NGPCA: Hierarchical clustering of data streams with adaptive number of clusters and adaptive dimensionality. PLOS One. 2026;21(1). doi:10.1371/journal.pone.0339171
HSBI-PUB | DOI | Download (ext.)
 
[17]
2025 | Artikel | FH-PUB-ID: 6244 | OA
Niederhaus M, Migenda N, Weller J, Kohlhase M, Schenck W. Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems. Big Data and Cognitive Computing. 2025;9(10). doi:10.3390/bdcc9100261
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[16]
2025 | Datenpublikation | FH-PUB-ID: 6486
Migenda N. Clustering in High-Dimensional Data Streams with Adaptive Local Principal Component Analysis. Universität Bielefeld; 2025. doi:10.4119/UNIBI/3006960
HSBI-PUB | DOI
 
[15]
2024 | Konferenzbeitrag | FH-PUB-ID: 4916
Weller J, Migenda N, Kühn A, Dumitrescu R. Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories. In: Hannover : publish-Ing.; 2024. doi: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. Design decisions for integrating Prescriptive Analytics Use Cases into Smart Factories. Procedia CIRP. 2024;128:424-429. doi:10.1016/j.procir.2024.03.022
HSBI-PUB | DOI
 
[13]
2024 | Artikel | FH-PUB-ID: 4050
Migenda N, Möller R, Schenck W. Adaptive local Principal Component Analysis improves the clustering of high-dimensional data. Pattern Recognition. 2024;146. doi:10.1016/j.patcog.2023.110030
HSBI-PUB | DOI
 
[12]
2024 | Artikel | FH-PUB-ID: 4698
Migenda N, Möller R, Schenck W. NGPCA: Clustering of high-dimensional and non-stationary data streams. Software Impacts. 2024;20. doi: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. Technical Readiness of Prescriptive Analytics Platforms: A Survey. In: IEEE, ed. 2024 35th Conference of Open Innovations Association (FRUCT). IEEE; 2024:509-519. doi:10.23919/FRUCT61870.2024.10516367
HSBI-PUB | DOI
 
[10]
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller J, Migenda N, Liu R, et al. Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories. In: Niggemann O, Beyerer J, Krantz M, Kühnert C, eds. Machine Learning for Cyber-Physical Systems. Selected Papers from the International Conference ML4CPS 2023. Vol 18. Technologien für die intelligente Automation. Cham: Springer Nature Switzerland; 2024:89-100. doi:10.1007/978-3-031-47062-2_9
HSBI-PUB | DOI
 
[9]
2024 | Artikel | FH-PUB-ID: 4913
Weller J, Migenda N, Naik Y, et al. Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories. Mathematics. 2024;12(17). doi: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. 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). IEEE; 2023:1-7. doi:10.1109/ADACIS59737.2023.10424368
HSBI-PUB | DOI
 
[7]
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe C, Migenda N, Pelkmann D, Hötte DA, Schenck W. Collaborative System for Question Answering in German Case Law Documents. In: Camarinha-Matos LM, Ortiz A, Boucher X, Osório AL, eds. Collaborative Networks in Digitalization and Society 5.0. IFIP Advances in Information and Communication Technology. Cham: Springer International Publishing; 2022:303-312. doi: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 DA, Schenck W. 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). IEEE; 2021:29-32. doi:10.1109/AIKE52691.2021.00011
HSBI-PUB | DOI
 
[5]
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt T, Migenda N, Schöne M, et al. Advanced Data Analytics Platform for Manufacturing Companies. In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ). IEEE; 2021:01-08. doi: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. Variational Autoencoder based Novelty Detection for Real-World Time Series. In: 2021 3rd International Conference on Management Science and Industrial Engineering. New York, NY, USA: ACM; 2021:1-7. doi:10.1145/3460824.3460825
HSBI-PUB | DOI
 
[3]
2021 | Artikel | FH-PUB-ID: 1203
Migenda N, Möller R, Schenck W. Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE. 2021;16(3). doi:10.1371/journal.pone.0248896
HSBI-PUB | DOI
 
[2]
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda N, Schenck W. Adaptive Dimensionality Reduction for Local Principal Component Analysis. In: 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE; 2020:1579-1586. doi:10.1109/ETFA46521.2020.9212129
HSBI-PUB | DOI
 
[1]
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda N, Möller R, Schenck W. Adaptive Dimensionality Adjustment for Online “Principal Component Analysis.” In: Yin H, Camacho D, Tino P, Tallón-Ballesteros AJ, Menezes R, Allmendinger R, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2019:76-84. doi:10.1007/978-3-030-33607-3_9
HSBI-PUB | DOI
 
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Zitationsstil: AMA

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