Bereich Supply Chain Services des Fraunhofer IIS
Bereich Supply Chain Services des Fraunhofer IIS

Wissenschaftliche Publikationen

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Boll, S., Schnell, M., Faisst, W., Mordvinova, O., Pflaum, A., Rabe, M., ... & Zielinski, O. (2022). Mit Künstlicher Intelligenz zu nachhaltigen Geschäftsmodellen: Nachhaltigkeit von, durch und mit KI (Whitepaper).

 

Freund, M., Fries, J., Dorsch, R., Schiller, P., & Harth, A. (2023, November). WoT2Pod: An Architecture enabling an Edge-to-Cloud Continuum. In Proceedings of the 13th International Conference on the Internet of Things (pp. 42-49).

 

Fries, J., Freund, M., & Harth, A. (2023). A Solid Architecture for Machine Data Exchange with Access Control. Proceedings of the 1st Semantic Web on Constrained Things, Hersonissos, Greece, 3412, 74-81.

 

Freund, M., Fries, J., Wehr, T., & Harth, A. (2023). Generating Visual Programming Blocks based on Semantics in W3C Thing Descriptions. In SWoCoT@ ESWC (pp. 1-15).

 

Freund, M., Rott, J., Dorsch, R., & Harth, A. (2024). FAIR Internet of Things Data: Enabling Process Optimization at Munich Airport. In ESWC: Extended Semantic Web Conference. Hersonissos, Greece.

 

Freund, M., Schmid, S., Dorsch, R., & Harth, A. (2024, May). FlexRML: A Flexible and Memory Efficient Knowledge Graph Materializer. In European Semantic Web Conference (pp. 40-56). Cham: Springer Nature Switzerland.

 

Goschenhofer, J. (2022). Deep semi-supervised learning for time-series classification. In Deep Learning Applications, Volume 4 (pp. 361-384). Singapore: Springer Nature Singapore.

 

Hauff, M., Comet, L. M., Moosmann, P., Lange, C., Chrysakis, I., & Theissen-Lipp, J. (2024, May). FAIRness in Dataspaces: The Role of Semantics for Data Management. In The Second International Workshop on Semantics in Dataspaces, co-located with the Extended Semantic Web Conference.

 

Henselmann, D., & Harth, A. (2024). Towards Modeling the Structure of Product Dependencies in Supply Networks to Identify Bottlenecks Among Suppliers.

 

Kaminwar, S. R., Goschenhofer, J., Thomas, J., Thon, I., & Bischl, B. (2023). Structured verification of machine learning models in industrial settings.
Big Data, 11(3), 181-198.

 

Meckler, S., Dorsch, R., & Filipp, P. (2024, September). Vibration-Based Operating Status Monitoring of a Production Line with Low-Cost IoT Devices. In IFIP International Conference on Advances in Production Management Systems (pp. 428-442). Cham: Springer Nature Switzerland.

 

Meckler, S., & Harth, A. (2023, October). SPARQL_edit: Editing RDF Literals in Knowledge Graphs via View-Update Translations. In International Semantic Web Conference (pp. 176-193). Cham: Springer Nature Switzerland.

 

Meckler, S., Dorsch, R., Henselmann, D., & Harth, A. (2023, April). The web and linked data as a solid foundation for dataspaces. In Companion Proceedings of the ACM Web Conference 2023 (pp. 1440-1446).

 

Mehringer, J., Frechen, H., Beck, N., Sukowski, F., Stocker, T., Freier, D., & Schäfer, F. (2023). Cast control: AI-based explanations of casting defects linking process and quality inspection data. Combining of Design, Casting, Computer Simulation, Checking and Cyclic Behaviour for Efficient Cast Components, March 6th-8th, 2023, Darmstadt, 145.

 

Schmitt-Rüth, S., & Simon, M. (2022). Akzeptanzbasierte Bewertungen sozio-ethischer Risikoaspekte in Technikentwicklungsprojekten–Anwendung und Empfehlungen mit dem praxisorientierten Vorgehensmodell HEART. In Faktor Mensch (pp. 133-156). Wiesbaden: Springer Fachmedien Wiesbaden.

 

 

 

 

 

 

Aßenmacher, M., Rauch, L., Goschenhofer, J., Stephan, A., Bischl, B., Roth, B., Sick, B. (2023): Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. In: Proceedings of the 7th Workshop on Interactive Adaptive Learning (Co-Located with ECML-PKDD 2023)


Bärmann A., Burlacu R., Hager L., Kutzer K. (2023): An Approximation Algorithm for Optimal Piecewise Linear Interpolations of Bounded Variable Products. In: Journal of Optimization Theory and Applications


Bärmann, A., Martin, A., Schneider, O. (2023): The Bipartite Boolean Quadric Polytope with Multiple-Choice Constraints. In: SIAM Journal on Optimization.


 Bärmann A., Gemander P., Hager L., Nöth F., Schneider O. (2023): EETTlib - Energy-efficient train timetabling library. In: Networks


Bärmann A., Gemander P., Martin A. (2023): A Stochastic Optimization Approach to Energy-Efficient Underground Timetabling Under Uncertain Dwell and Running Times. (2023) In: Transportation Science


Bärmann A., Gemander P., Merkert M., Wiertz A., Martínez F. (2023): Algorithms for the clique problem with multiple-choice constraints under a series–parallel dependency graph. In: Discrete Applied Mathematics


Beer A., Draganov A., Hohma E., Jahn P., Frey C.M.M., Assent I. (2023): Connecting the Dots--Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining


Deutel M., Kontes G., Mutschler C., Teich, J. (2023): Augmented Random Search for Multi-Objective Bayesian Optimization of Neural Networks. In: ArXiv preprint

 

Gilhuber S., Busch J., Rotthues D., Frey C.M.M., Seidl T. (2023): DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. In: Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023


Gilhuber S., Hvingelby R., Fok M.L.A., Seidl T. (2023): How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases


Goschenhofer J., Bischl B., Kira Z. (2023): ConstraintMatch for semi-constrained Clustering. In: International Joint Conference on Neural Networks (IJCNN)


[150] Karl F., Pielok T., Moosbauer J., Pfisterer F., Coors S., Binder M., Schneider L., Thomas J., Richter J., Lang M., Garrido-Merchán E. C., Branke J., Bischl B. (2023): Multi-Objective Hyperparameter Optimization in Machine Learning — An Overview. In: ACM Transactions on Evolutionary Learning and Optimization


[149] Kemeter L. M., Hvingelby R., Sierak P., Schön T., Gosswami B. (2023): Towards reducing data acquisition and labeling for defect detection using simulated data. In: Proceedings of the 22th Conference on European Conference on Machine Learning and Principles and Practice of Knowledge Discovery ECML’23


Marzilger R. (2023): Identify Game Tactics in Soccer by Clustering Positional Data. In: Data Science Blog medium.com.

 

Schneider L., Bischl B., Thomas J. (2023): Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models. In: Proceedings of the Genetic and Evolutionary Computation Conference.

 

Schön T., Gosswami B., Hvingelby R., Suth D., Kemeter L. M., Sierak P. (2023): Automated defect recognition in X-ray projections using neural networks trained on simulated and real-world data. In: Proceedings of the 12th Conference on Industrial Computed Tomograph ICT’23


Wagner F., Bärmann A., Liers F., Weissenbäck M. (2023): Improving Quantum Computation by Optimized Qubit Routing. In: Journal of Optimization Theory and Applications