KNIME

KNIME - Professional Open-Source Software. KNIME is a user-friendly graphical workbench for the entire analysis process: data access, data transformation, initial investigation, powerful predictive analytics, visualisation and reporting. The open integration platform provides over 1000 modules (nodes), including those of the KNIME community and its extensive partner network. KNIME can be downloaded onto the desktop and used free of charge. KNIME products include additional functionalities such as shared repositories, authentication, remote execution, scheduling, SOA integration and a web user interface as well as world-class support. Robust big data extensions are available for distributed frameworks such as Hadoop. KNIME is used by over 3000 organizations in more than 60 countries.


References in zbMATH (referenced in 22 articles )

Showing results 1 to 20 of 22.
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  1. Andrea Mauri: alvaDesc: A Tool to Calculate and Analyze Molecular Descriptors and Fingerprints (2020) not zbMATH
  2. Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann: From Things’ Modeling Language (ThingML) to Things’ Machine Learning (ThingML2) (2020) arXiv
  3. Berthold, Michael R.; Borgelt, Christian; Höppner, Frank; Klawonn, Frank; Silipo, Rosaria: Guide to intelligent data science. How to intelligently make use of real data (2020)
  4. Ruman Gerst; Anna Medyukhina; Marc Thilo Figge: MISA++: A standardized interface for automated bioimage analysis (2020) not zbMATH
  5. Dzemyda, Gintautas; Kurasova, Olga; Medvedev, Viktor; Dzemydaitė, Giedrė: Visualization of data: methods, software, and applications (2019)
  6. Mihelčić, Matej; Šmuc, Tomislav: Targeted and contextual redescription set exploration (2018)
  7. Reljin, Irini (ed.); Obradović, Zoran (ed.); Popović, Mirjana B. (ed.); Mladenov, Valeri (ed.): New methods for analyzing complex biomedical systems and signals (2018)
  8. Zanin, Massimiliano; Romance, Miguel; Moral, Santiago; Criado, Regino: Credit card fraud detection through parenclitic network analysis (2018)
  9. Canan Has; Jens Allmer: PGMiner: Complete proteogenomics workflow; from data acquisition to result visualization (2017) not zbMATH
  10. Curtis T. Rueden, Johannes Schindelin, Mark C. Hiner, Barry E. DeZonia, Alison E. Walter, Kevin W. Eliceiri: ImageJ2: ImageJ for the next generation of scientific image data (2017) arXiv
  11. Hu, Qiwei; Chakhar, Salem; Siraj, Sajid; Labib, Ashraf: Spare parts classification in industrial manufacturing using the dominance-based rough set approach (2017)
  12. Posch, S.; Moeller, B.: Alida - Advanced Library for Integrated Development of Data Analysis Applications (2017) not zbMATH
  13. Ralf Mikut, Andreas Bartschat, Wolfgang Doneit, Jorge Angel Gonzalez Ordiano, Benjamin Schott, Johannes Stegmaier, Simon Waczowicz, Markus Reischl: The MATLAB Toolbox SciXMiner: User’s Manual and Programmer’s Guide (2017) arXiv
  14. Berk Ekmekci, Charles E. McAnany, Cameron Mura: An Introduction to Programming for Bioscientists: A Python-based Primer (2016) arXiv
  15. Bernatavičienė, Jolita; Dzemyda, Gintautas; Kurasova, Olga; Marcinkevičius, Virginijus; Medvedev, Viktor; Treigys, Povilas: Cloud computing approach for intelligent visualization of multidimensional data (2016)
  16. Nascimento, Susana: Applying the gradient projection method to a model of proportional membership for fuzzy cluster analysis (2016)
  17. Fournier-Viger, Philippe; Gomariz, Antonio; Gueniche, Ted; Soltani, Azadeh; Wu, Cheng-Wei; Tseng, Vincent S.: SPMF: a Java open-source pattern mining library (2014)
  18. Madeyski, Lech; Majchrzak, Marek: Software measurement and defect prediction with DePress extensible framework (2014) ioport
  19. Piccolo, Stephen R.; Frey, Lewis J.: ML-flex: a flexible toolbox for performing classification analyses in parallel (2012)
  20. Berthold, Michael R.; Borgelt, Christian; Höppner, Frank; Klawonn, Frank: Guide to intelligent data analysis. How to intelligently make sense of real data (2010)

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