Abstract
We demonstrate the Advanced Data mining And Machine learning System (ADAMS), a novel workflow engine designed for rapid prototyping and maintenance of complex knowledge workflows. ADAMS does not require the user to manually connect inputs to outputs on a large canvas. It uses a compact workflow representation, control operators, and a simple interface between operators, allowing them to be auto-connected. It contains an extensive library of operators for various types of analysis, and a convenient plug-in architecture to easily add new ones.
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© 2012 Springer-Verlag Berlin Heidelberg
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Reutemann, P., Vanschoren, J. (2012). Scientific Workflow Management with ADAMS. In: Flach, P.A., De Bie, T., Cristianini, N. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2012. Lecture Notes in Computer Science(), vol 7524. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33486-3_58
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DOI: https://doi.org/10.1007/978-3-642-33486-3_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33485-6
Online ISBN: 978-3-642-33486-3
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