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dc.contributor.authorReutemann, Peter
dc.contributor.authorVanschoren, Joaquin
dc.identifier.citationReutemann, P. & Vanschoren, J. (2012). Scientific workflow management with ADAMS. In Lecture Notes in Computer Science, 2012, Volume 7524 LNAI, Issue Part 2: Machine Learning and Knowledge Discovery in Databases,pp. 833-837.en_NZ
dc.description.abstractWe 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.en_NZ
dc.subjectdata miningen_NZ
dc.subjectmachine learningen_NZ
dc.subjectscientific workflowsen_NZ
dc.titleScientific workflow management with ADAMSen_NZ
dc.typeChapter in Booken_NZ

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