18 Publikationen
2026 | Artikel | FH-PUB-ID: 6485 | 
Migenda, Nico, et al. “H-NGPCA: Hierarchical Clustering of Data Streams with Adaptive Number of Clusters and Adaptive Dimensionality.” PLOS One, vol. 21, no. 1, e0339171, Public Library of Science (PLoS), 2026, doi:10.1371/journal.pone.0339171.
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2025 | Artikel | FH-PUB-ID: 6244 |
Niederhaus, Marvin, et al. “Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems.” Big Data and Cognitive Computing, vol. 9, no. 10, 261, MDPI AG, 2025, doi:10.3390/bdcc9100261.
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2025 | Datenpublikation | FH-PUB-ID: 6486
Migenda, Nico. Clustering in High-Dimensional Data Streams with Adaptive Local Principal Component Analysis. Universität Bielefeld, 2025, doi:10.4119/UNIBI/3006960.
HSBI-PUB
| DOI
2024 | Konferenzbeitrag | FH-PUB-ID: 4916
Weller, Julian, et al. Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories. Hannover : publish-Ing., 2024, doi:10.15488/17721.
HSBI-PUB
| DOI
2024 | Artikel | FH-PUB-ID: 5497
Weller, Julian, et al. “Design Decisions for Integrating Prescriptive Analytics Use Cases into Smart Factories.” Procedia CIRP, vol. 128, Elsevier BV, 2024, pp. 424–29, doi:10.1016/j.procir.2024.03.022.
HSBI-PUB
| DOI
2024 | Artikel | FH-PUB-ID: 4050
Migenda, Nico, et al. “Adaptive Local Principal Component Analysis Improves the Clustering of High-Dimensional Data.” Pattern Recognition, vol. 146, 110030, Elsevier BV, 2024, doi:10.1016/j.patcog.2023.110030.
HSBI-PUB
| DOI
2024 | Artikel | FH-PUB-ID: 4698
Migenda, Nico, et al. “NGPCA: Clustering of High-Dimensional and Non-Stationary Data Streams.” Software Impacts, vol. 20, 100635, Elsevier BV, 2024, doi:10.1016/j.simpa.2024.100635.
HSBI-PUB
| DOI
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus, Marvin, et al. “Technical Readiness of Prescriptive Analytics Platforms: A Survey.” 2024 35th Conference of Open Innovations Association (FRUCT), edited by IEEE, IEEE, 2024, pp. 509–19, doi:10.23919/FRUCT61870.2024.10516367.
HSBI-PUB
| DOI
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller, Julian, et al. “Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories.” Machine Learning for Cyber-Physical Systems. Selected Papers from the International Conference ML4CPS 2023, edited by Oliver Niggemann et al., vol. 18, Springer Nature Switzerland, 2024, pp. 89–100, doi:10.1007/978-3-031-47062-2_9.
HSBI-PUB
| DOI
2024 | Artikel | FH-PUB-ID: 4913
Weller, Julian, et al. “Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories.” Mathematics, vol. 12, no. 17, 2663, MDPI AG, 2024, doi:10.3390/math12172663.
HSBI-PUB
| DOI
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller, Julian, et al. “Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories.” 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS), edited by IEEE, IEEE, 2023, pp. 1–7, doi:10.1109/ADACIS59737.2023.10424368.
HSBI-PUB
| DOI
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe, Christoph, et al. “Collaborative System for Question Answering in German Case Law Documents.” Collaborative Networks in Digitalization and Society 5.0, edited by Luis M. Camarinha-Matos et al., Springer International Publishing, 2022, pp. 303–12, doi:10.1007/978-3-031-14844-6_24.
HSBI-PUB
| DOI
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
Hoppe, Christoph, et al. “Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents.” 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), IEEE, 2021, pp. 29–32, doi:10.1109/AIKE52691.2021.00011.
HSBI-PUB
| DOI
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, Tim, et al. “Advanced Data Analytics Platform for Manufacturing Companies.” 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), IEEE, 2021, pp. 01–08, doi:10.1109/ETFA45728.2021.9613499.
HSBI-PUB
| DOI
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann, Luca, et al. “Variational Autoencoder Based Novelty Detection for Real-World Time Series.” 2021 3rd International Conference on Management Science and Industrial Engineering, ACM, 2021, pp. 1–7, doi:10.1145/3460824.3460825.
HSBI-PUB
| DOI
2021 | Artikel | FH-PUB-ID: 1203
Migenda, Nico, et al. “Adaptive Dimensionality Reduction for Neural Network-Based Online Principal Component Analysis.” PLOS ONE, vol. 16, no. 3, e0248896, Public Library of Science (PLoS), 2021, doi:10.1371/journal.pone.0248896.
HSBI-PUB
| DOI
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda, Nico, and Wolfram Schenck. “Adaptive Dimensionality Reduction for Local Principal Component Analysis.” 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2020, pp. 1579–86, doi:10.1109/ETFA46521.2020.9212129.
HSBI-PUB
| DOI
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda, Nico, et al. “Adaptive Dimensionality Adjustment for Online ‘Principal Component Analysis.’” Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I, edited by Hujun Yin et al., Springer International Publishing, 2019, pp. 76–84, doi:10.1007/978-3-030-33607-3_9.
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18 Publikationen
2026 | Artikel | FH-PUB-ID: 6485 |
Migenda, Nico, et al. “H-NGPCA: Hierarchical Clustering of Data Streams with Adaptive Number of Clusters and Adaptive Dimensionality.” PLOS One, vol. 21, no. 1, e0339171, Public Library of Science (PLoS), 2026, doi:10.1371/journal.pone.0339171.
HSBI-PUB
| DOI
| Download (ext.)
2025 | Artikel | FH-PUB-ID: 6244 |
Niederhaus, Marvin, et al. “Integrating Graph Retrieval-Augmented Generation into Prescriptive Recommender Systems.” Big Data and Cognitive Computing, vol. 9, no. 10, 261, MDPI AG, 2025, doi:10.3390/bdcc9100261.
HSBI-PUB
| Dateien verfügbar
| DOI
| Download (ext.)
2025 | Datenpublikation | FH-PUB-ID: 6486
Migenda, Nico. Clustering in High-Dimensional Data Streams with Adaptive Local Principal Component Analysis. Universität Bielefeld, 2025, doi:10.4119/UNIBI/3006960.
HSBI-PUB
| DOI
2024 | Konferenzbeitrag | FH-PUB-ID: 4916
Weller, Julian, et al. Prescriptive Analytics Data Canvas: Strategic Planning For Prescriptive Analytics In Smart Factories. Hannover : publish-Ing., 2024, doi:10.15488/17721.
HSBI-PUB
| DOI
2024 | Artikel | FH-PUB-ID: 5497
Weller, Julian, et al. “Design Decisions for Integrating Prescriptive Analytics Use Cases into Smart Factories.” Procedia CIRP, vol. 128, Elsevier BV, 2024, pp. 424–29, doi:10.1016/j.procir.2024.03.022.
HSBI-PUB
| DOI
2024 | Artikel | FH-PUB-ID: 4050
Migenda, Nico, et al. “Adaptive Local Principal Component Analysis Improves the Clustering of High-Dimensional Data.” Pattern Recognition, vol. 146, 110030, Elsevier BV, 2024, doi:10.1016/j.patcog.2023.110030.
HSBI-PUB
| DOI
2024 | Artikel | FH-PUB-ID: 4698
Migenda, Nico, et al. “NGPCA: Clustering of High-Dimensional and Non-Stationary Data Streams.” Software Impacts, vol. 20, 100635, Elsevier BV, 2024, doi:10.1016/j.simpa.2024.100635.
HSBI-PUB
| DOI
2024 | Konferenzbeitrag | FH-PUB-ID: 4699
Niederhaus, Marvin, et al. “Technical Readiness of Prescriptive Analytics Platforms: A Survey.” 2024 35th Conference of Open Innovations Association (FRUCT), edited by IEEE, IEEE, 2024, pp. 509–19, doi:10.23919/FRUCT61870.2024.10516367.
HSBI-PUB
| DOI
2024 | Buchbeitrag | FH-PUB-ID: 4915
Weller, Julian, et al. “Towards a Systematic Approach for Prescriptive Analytics Use Cases in Smart Factories.” Machine Learning for Cyber-Physical Systems. Selected Papers from the International Conference ML4CPS 2023, edited by Oliver Niggemann et al., vol. 18, Springer Nature Switzerland, 2024, pp. 89–100, doi:10.1007/978-3-031-47062-2_9.
HSBI-PUB
| DOI
2024 | Artikel | FH-PUB-ID: 4913
Weller, Julian, et al. “Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories.” Mathematics, vol. 12, no. 17, 2663, MDPI AG, 2024, doi:10.3390/math12172663.
HSBI-PUB
| DOI
2023 | Konferenzbeitrag | FH-PUB-ID: 4700
Weller, Julian, et al. “Conceptual Framework for Prescriptive Analytics Based on Decision Theory in Smart Factories.” 2023 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS), edited by IEEE, IEEE, 2023, pp. 1–7, doi:10.1109/ADACIS59737.2023.10424368.
HSBI-PUB
| DOI
2022 | Konferenzbeitrag | FH-PUB-ID: 2569
Hoppe, Christoph, et al. “Collaborative System for Question Answering in German Case Law Documents.” Collaborative Networks in Digitalization and Society 5.0, edited by Luis M. Camarinha-Matos et al., Springer International Publishing, 2022, pp. 303–12, doi:10.1007/978-3-031-14844-6_24.
HSBI-PUB
| DOI
2021 | Konferenzbeitrag | FH-PUB-ID: 2570
Hoppe, Christoph, et al. “Towards Intelligent Legal Advisors for Document Retrieval and Question-Answering in German Legal Documents.” 2021 IEEE Fourth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), IEEE, 2021, pp. 29–32, doi:10.1109/AIKE52691.2021.00011.
HSBI-PUB
| DOI
2021 | Konferenzbeitrag | FH-PUB-ID: 2571
Voigt, Tim, et al. “Advanced Data Analytics Platform for Manufacturing Companies.” 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), IEEE, 2021, pp. 01–08, doi:10.1109/ETFA45728.2021.9613499.
HSBI-PUB
| DOI
2021 | Konferenzbeitrag | FH-PUB-ID: 2572
Steinmann, Luca, et al. “Variational Autoencoder Based Novelty Detection for Real-World Time Series.” 2021 3rd International Conference on Management Science and Industrial Engineering, ACM, 2021, pp. 1–7, doi:10.1145/3460824.3460825.
HSBI-PUB
| DOI
2021 | Artikel | FH-PUB-ID: 1203
Migenda, Nico, et al. “Adaptive Dimensionality Reduction for Neural Network-Based Online Principal Component Analysis.” PLOS ONE, vol. 16, no. 3, e0248896, Public Library of Science (PLoS), 2021, doi:10.1371/journal.pone.0248896.
HSBI-PUB
| DOI
2020 | Konferenzbeitrag | FH-PUB-ID: 2574
Migenda, Nico, and Wolfram Schenck. “Adaptive Dimensionality Reduction for Local Principal Component Analysis.” 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), IEEE, 2020, pp. 1579–86, doi:10.1109/ETFA46521.2020.9212129.
HSBI-PUB
| DOI
2019 | Buchbeitrag | FH-PUB-ID: 1208
Migenda, Nico, et al. “Adaptive Dimensionality Adjustment for Online ‘Principal Component Analysis.’” Intelligent Data Engineering and Automated Learning – IDEAL 2019. 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I, edited by Hujun Yin et al., Springer International Publishing, 2019, pp. 76–84, doi:10.1007/978-3-030-33607-3_9.
HSBI-PUB
| DOI