@inproceedings{Berthold2007KNIME-5434,
  year={2007},
  title={KNIME: The Konstanz Information Miner},
  publisher={Springer},
  address={New York},
  booktitle={Data Analysis , Machine Learning and Applications : Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e. V., Albert-Ludwigs-Universität Freiburg, March 7 9 , 2007},
  author={Berthold, Michael R. and Cebron, Nicolas and Dill, Fabian and Gabriel, Thomas R. and Kötter, Tobias and Meinl, Thorsten and Ohl, Peter and Sieb, Christoph and Thiel, Kilian and Wiswedel, Bernd}
}

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    <dc:creator>Meinl, Thorsten</dc:creator>
    <dcterms:bibliographicCitation>First publ. in: Data Analysis, Machine Learning and Applications: Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7 9, 2007.  New York: Springer, 2008</dcterms:bibliographicCitation>
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    <dc:creator>Berthold, Michael R.</dc:creator>
    <dcterms:title>KNIME: The Konstanz Information Miner</dcterms:title>
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    <dcterms:abstract xml:lang="eng">The Konstanz Information Miner is a modular environment, which enables easy visual assembly and interactive execution of a data pipeline. It is designed as a teaching, research and collaboration platform, which enables simple integration of new algorithms and tools as well as data manipulation or visualization methods in the form of new modules or nodes. In this paper we describe some of the design aspects of the underlying architecture and briefly sketch how new nodes can be incorporated.</dcterms:abstract>
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