Publications

Elena Williams, Manuel Kienast, Evelyn Medawar, Janis Reinelt, Alberto Merola, Sophie Anne Ines Klopfenstein, Anne Rike Flint, Patrick Heeren, Akira-Sebastian Poncette, Felix Balzer, Nico Scherf, und others. A standardized clinical data harmonization pipeline for scalable ai application deployment (fhir-dhp): Validation and usability study. JMIR Medical Informatics, (11):e43847, JMIR Publications Toronto, Canada, 2023. [PUMA: topic_neuroinspired (fhir-dhp) ai application clinical data deployment harmonization pipeline scalable standardized]

Johannes Gerritzen, Andreas Hornig, Benjamin Gröger, und Maik Gude. A Data Driven Modelling Approach for the Strain Rate Dependent 3D Shear Deformation and Failure of Thermoplastic Fibre Reinforced Composites: Experimental Characterisation and Deriving Modelling Parameters. Journal of Composites Science, (6)10:318, MDPI, 2022. [PUMA: topic_engineering 3D Approach Characterisation Composites Data Deformation Dependent Driven Experimental Failure Fibre Modelling Rate Reinforced Shear Strain Thermoplastic]

Patrick Ebel, Pavlo Bazilinskyy, Angel Hsing-Chi Hwang, Wendy Ju, Hauke Sandhaus, Aravinda Ramakrishnan Srinivasan, Qian Yang, und Philipp Wintersberger. Breaking Barriers: Workshop on Open Data Practices in AutoUI Research. Adjunct Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 227--230, 2023. [PUMA: topic_visualcomputing AutoUI Data Open Practices Research Workshop]

Patrick Ebel, Kim Julian Gülle, Christoph Lingenfelder, und Andreas Vogelsang. Exploring Millions of User Interactions with ICEBOAT: Big Data Analytics for Automotive User Interfaces. Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 81--92, 2023. [PUMA: topic_visualcomputing Analytics Automotive Big Data ICEBOAT Interactions Interfaces User]

Patrick Ebel, Ibrahim Emre Göl, Christoph Lingenfelder, und Andreas Vogelsang. Destination Prediction Based on Partial Trajectory Data. 2020. [PUMA: Based Data Destination Partial Prediction Trajectory on] URL

Patrick Ebel, Kim Julian Gülle, Christoph Lingenfelder, und Andreas Vogelsang. ICEBOAT: An Interactive User Behavior Analysis Tool for Automotive User Interfaces. Adjunct Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology, Association for Computing Machinery, New York, NY, USA, 2022. [PUMA: topic_visualcomputing Data Design Driving Human-Computer In-Vehicle Information Interaction Naturalistic System Tools Visualization] URL

Weizhou Luo, Zhongyuan Yu, Rufat Rzayev, Marc Satkowski, Stefan Gumhold, Matthew McGinity, und Raimund Dachselt. PEARL: Physical Environment based Augmented Reality Lenses for In-Situ Human Movement Analysis. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 381Association for Computing Machinery, New York, NY, USA, April 2023. [PUMA: topic_visualcomputing Analytics Immersive In-situ affordance analysis augmented/mixed data movement physical reality referents visualization] URL

Suryanarayana Maddu, Bevan L. Cheeseman, Ivo F. Sbalzarini, und Christian L. Müller. Stability selection enables robust learning of partial differential equations from limited noisy data. arXiv, 2019. [PUMA: (cs.LG), (math.NA), (physics.data-an), Analysis Analysis, Computer Data FOS: Learning Machine Mathematics, Numerical Physical Probability Statistics and information sciences sciences,] URL

André Petermann, Martin Junghanns, Stephan Kemper, Kevin Gómez, Niklas Teichmann, und Erhard Rahm. Graph Mining for Complex Data Analytics. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), 1316--1319, Dezember 2016. [PUMA: Data Intelligence Mining;Business algorithms;Conferences;Graph analysis;Data and design mining;Business;Algorithm models;Libraries;Partitioning]

David Nam, Julius Chapiro, Valerie Paradis, Tobias Paul Seraphin, und Jakob Nikolas Kather. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep., (4)4:100443, Elsevier BV, April 2022. [PUMA: topic_lifescience AI, Artificial CNN, Communications DICOM, Diagnosis; Digital HCC, Imaging Individual ML, MVI, Medicine; NAFLD, NASH, Prognosis Reporting TACE, TRIPOD, Transparent WSIs, a and artificial carcinoma; chemoembolisation; convolutional data deep diagnostic disease; fatty for hepatocellular images; imaging; in integration intelligence; invasion; learning; liver machine microvascular model multimodal multivariable network; neural non-alcoholic of or prediction slide steatohepatitis; support system; transarterial whole]

Daniel Ayala, Inma Hernández, David Ruiz, und Erhard Rahm. Multi-source dataset of e-commerce products with attributes for property matching. Data Brief, (41)107884:107884, Elsevier BV, April 2022. [PUMA: area_bigdata integration; engineering; Data Ontology; Property matching]

Pascal Kerschke, Holger H Hoos, Frank Neumann, und Heike Trautmann. Automated algorithm selection: Survey and perspectives. Evol. Comput., (27)1:3--45, MIT Press, 2019. [PUMA: Automated algorithm analysis; approaches; automated combinatorial configuration; continuous data exploratory feature-based landscape learning; machine metalearning optimisation; selection; streams.;]

Diego Esteves, Anisa Rula, Aniketh Janardhan Reddy, und Jens Lehmann. Toward Veracity Assessment in RDF Knowledge Bases: An Exploratory Analysis. J. Data and Information Quality, (9)3Association for Computing Machinery, New York, NY, USA, Februar 2018. [PUMA: DeFacto, analysis benchmark, checking, data data, exploratory fact linked quality, trustworthiness,] URL

Katja Hoffmann, Katja Cazemier, Christoph Baldow, Silvio Schuster, Yuri Kheifetz, Sibylle Schirm, Matthias Horn, Thomas Ernst, Constanze Volgmann, Christian Thiede, Andreas Hochhaus, Martin Bornhäuser, Meinolf Suttorp, Markus Scholz, Ingmar Glauche, Markus Loeffler, und Ingo Roeder. Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology. BMC Med. Inform. Decis. Mak., (20)1:28, Februar 2020. [PUMA: Clinical Computer Data Haematology; Individual Mathematical Model-based Routine Support decision-making; management; modelling; optimization; planning; simulation; system therapy treatment workflow;]

Kristian Schultz, Saptarshi Bej, Waldemar Hahn, Markus Wolfien, Prashant Srivastava, und Olaf Wolkenhauer. ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets. Pattern Recognition, (147):110138, 2024. [PUMA: topic_lifescience Convex GAN, Imbalanced LoRAS, Tabular data data, learning, space] URL

Markus Wolfien, Najia Ahmadi, Kai Fitzer, Sophia Grummt, Kilian-Ludwig Heine, Ian-C Jung, Dagmar Krefting, Andreas Kühn, Yuan Peng, Ines Reinecke, Julia Scheel, Tobias Schmidt, Paul Schmücker, Christina Schüttler, Dagmar Waltemath, Michele Zoch, und Martin Sedlmayr. Ten topics to get started in medical informatics research. J. Med. Internet Res., (25):e45948, Juli 2023. [PUMA: topic_lifescience clinical communication; data data; digital health health; informatics; interdisciplinary medical research]

Najia Ahmadi, Michele Zoch, Patricia Kelbert, Richard Noll, Jannik Schaaf, Markus Wolfien, und Martin Sedlmayr. Methods used in the development of common data models for health data: Scoping review. JMIR Med. Inform., (11):e45116, August 2023. [PUMA: topic_lifescience ; Data Development Healthcare Healthcare; Informatics Informatics; Interoperability Interoperability; Medical Observational Outcomes Partnership Partnership; Process Process; Repositories Repositories; Standardized Suggestive common data data; electronic elements elements; harmonisation harmonisation; health involvement model model; record record; stakeholder]

Akshay Akshay, Mitali Katoch, Navid Shekarchizadeh, Masoud Abedi, Ankush Sharma, Fiona C Burkhard, Rosalyn M Adam, Katia Monastyrskaya, und Ali Hashemi Gheinani. Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis. Gigascience, (13)Januar 2024. [PUMA: topic_federatedlearn AutoML; analysis; classification data learning; machine problems; visualization]