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 9 articles )

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  1. 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
  2. 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
  3. Fournier-Viger, Philippe; Gomariz, Antonio; Gueniche, Ted; Soltani, Azadeh; Wu, Cheng-Wei; Tseng, Vincent S.: SPMF: a Java open-source pattern mining library (2014)
  4. Madeyski, Lech; Majchrzak, Marek: Software measurement and defect prediction with DePress extensible framework (2014) ioport
  5. Piccolo, Stephen R.; Frey, Lewis J.: ML-flex: a flexible toolbox for performing classification analyses in parallel (2012)
  6. Berthold, Michael R; Borgelt, Christian; Höppner, Frank; Klawonn, Frank: Guide to intelligent data analysis. How to intelligently make sense of real data (2010)
  7. Alcalá-Fdez, J.; Sánchez, L.; García, S.; del Jesus, M.J.; Ventura, S.; Garrell, J.M.; Otero, J.; Romero, C.; Bacardit, J.; Rivas, V.M.; Fernández, J.C.; Herrera, F.: KEEL: a software tool to assess evolutionary algorithms for data mining problems (2009) ioport
  8. Berthold, Michael R.; Cebron, Nicolas; Dill, Fabian; Gabriel, Thomas R.; Kötter, Tobias; Meinl, Thorsten; Ohl, Peter; Thiel, Kilian; Wiswedel, Bernd: KNIME - the Konstanz information miner: version 2.0 and beyond (2009) ioport
  9. Tiwari, Abhishek; Sekhar, Arvind K.T.: Workflow based framework for life science informatics (2007)