Publications

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

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;]

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]